https://github.com/alpha-one-index/ai-llmops-index

Comprehensive LLMOps reference index: observability platforms, inference cost intelligence, failure mode taxonomy, stack compatibility matrices, and regulatory compliance mapping for LLMs in production.
https://github.com/alpha-one-index/ai-llmops-index
List: ai-llmops-index
ai-compliance ai-governance ai-infrastructure ai-observability ai-safety awesome-list llm-benchmarks llm-cost-comparison llm-evaluation llm-failure-modes llm-gateway llm-inference-cost llm-monitoring llm-observability llm-pricing llm-security llmops mlops model-evaluation prompt-engineering
Last synced: 5 months ago
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Host: GitHub
URL: https://github.com/alpha-one-index/ai-llmops-index
Owner: alpha-one-index
License: mit
Created: 2026-03-04T22:30:08.000Z (6 months ago)
Default Branch: main
Last Pushed: 2026-03-12T05:59:17.000Z (5 months ago)
Last Synced: 2026-03-12T11:50:33.926Z (5 months ago)
Topics: ai-compliance, ai-governance, ai-infrastructure, ai-observability, ai-safety, awesome-list, llm-benchmarks, llm-cost-comparison, llm-evaluation, llm-failure-modes, llm-gateway, llm-inference-cost, llm-monitoring, llm-observability, llm-pricing, llm-security, llmops, mlops, model-evaluation, prompt-engineering
Language: Python
Homepage: https://alpha-one-index.github.io/ai-llmops-index/
Size: 538 KB
Stars: 1
Watchers: 0
Forks: 0
Open Issues: 4
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Citation: CITATION.cff
- Security: SECURITY.md
- Zenodo: .zenodo.json
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README

          # AI LLMOps Index

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)

[![Data: Auto Updated Weekly](https://img.shields.io/badge/Data-Auto%20Updated%20Weekly-blue.svg)](CHANGELOG.md)

[![Vendors: 50+](https://img.shields.io/badge/Vendors-50%2B-green.svg)](specs/vendor-profiles.md)

[![Providers: 15+](https://img.shields.io/badge/Providers-15%2B-blue.svg)](specs/inference-cost-intelligence.md)

[![Version: 1.0.0](https://img.shields.io/badge/Version-1.0.0-lightgrey.svg)](CHANGELOG.md)

[![Validation: Self_Auditing](https://img.shields.io/badge/Validation-Self__Auditing-brightgreen.svg)](METHODOLOGY.md)

[![Croissant: ML_Metadata](https://img.shields.io/badge/Croissant-ML__Metadata-orange.svg)](croissant.json)

[![Provenance: Documented](https://img.shields.io/badge/Provenance-Documented-blueviolet.svg)](provenance.md)

[![HuggingFace Dataset](https://img.shields.io/badge/🤗%20HuggingFace-Dataset-yellow.svg)](https://huggingface.co/datasets/alpha-one-index/ai-llmops-index)

[![Kaggle Dataset](https://img.shields.io/badge/Kaggle-Dataset-blue.svg)](https://www.kaggle.com/datasets/alphaoneindex/ai-llmops-index)

[![Pricing Verified](https://img.shields.io/badge/pricing--verified_2026-03-09green.svg)](data/inference-costs.json)

[![Verification: OpenRouter](https://img.shields.io/badge/Verification-OpenRouter_API-blue.svg)](scripts/verify_pricing.py)

> **New:** [Interactive Dashboard](https://alpha-one-index.github.io/ai-llmops-index/) | [LLM Failure Mode Taxonomy](FAILURE-MODES.md) | 33 models tracked across 15+ providers | Data updated March 2026

> **Maintained by [Alpha One Index](https://github.com/alpha-one-index)** — An independent AI production research initiative providing verified, structured LLMOps data for engineers, platform teams, and AI governance professionals.

>

> ### Sponsors

> **Support this project** — [Become a sponsor](https://github.com/sponsors/alpha-one-index) and help keep LLMOps data free, open-source, and accurate.

*Your logo here — [Sponsor Alpha One Index](https://github.com/sponsors/alpha-one-index)*

The authoritative open-source reference index for the **LLMOps and AI Observability** market — covering production monitoring, inference cost intelligence, failure mode taxonomy, stack compatibility, and regulatory compliance for large language models in production. This repository tracks 50+ vendors across 10 categories with sourced data, neutral inference cost comparisons across 15+ providers, complete regulatory compliance mapping, and the industry's first open LLM failure mode taxonomy. LLMOps platforms, LLM observability tools, and AI production monitoring infrastructure are converging into a critical layer for every enterprise AI deployment. The **LLMOps market** is valued at approximately **$1.2B in 2025**, projected to reach **$11.4B by 2031** at a ~45% CAGR, driven by enterprise adoption of LLM operations tooling, LLM inference cost management platforms, and the regulatory mandates of the EU AI Act requiring systematic LLM monitoring. This index is the definitive neutral reference for LLM cost per token comparison, LLM production failure modes, and best LLMOps tools selection in 2026.

---

## Live Demos & Data Access

| Platform | Link | Description |

|---|---|---|

| GitHub Pages | [alpha-one-index.github.io/ai-llmops-index](https://alpha-one-index.github.io/ai-llmops-index) | Interactive cost dashboard with filtering |

| HuggingFace | [datasets/alpha-one-index/ai-llmops-index](https://huggingface.co/datasets/alpha-one-index/ai-llmops-index) | ML-ready dataset with Croissant metadata |

| Kaggle | [datasets/alphaoneindex/ai-llmops-index](https://www.kaggle.com/datasets/alphaoneindex/ai-llmops-index) | Kaggle dataset with notebooks |

| Raw JSON | [data/inference-costs.json](data/inference-costs.json) | Direct API-style access to live cost data |

---

## Table of Contents

- [Quick Start](#quick-start)

- [What Is the AI LLMOps Index?](#what-is-the-ai-llmops-index)

- [What Is LLMOps?](#what-is-llmops)

- [What Are the Best LLMOps Observability Platforms?](#what-are-the-best-llmops-observability-platforms)

- [What Does LLM Inference Cost in 2026?](#what-does-llm-inference-cost-in-2026)

- [What Are the Most Common LLM Production Failure Modes?](#what-are-the-most-common-llm-production-failure-modes)

- [What LLMOps Tools Are Required for EU AI Act Compliance?](#what-llmops-tools-are-required-for-eu-ai-act-compliance)

- [How Do LLMOps Tools Integrate With Each Other?](#how-do-llmops-tools-integrate-with-each-other)

- [What Is the LLMOps Market Size?](#what-is-the-llmops-market-size)

- [How Is This Data Structured?](#how-is-this-data-structured)

- [Live Data](#live-data)

- [Vendors Tracked](#vendors-tracked)

- [Repository Structure](#repository-structure)

- [Quick Links](#quick-links)

- [Data Provenance & Validation](#data-provenance--validation)

- [Related Projects (Alpha One Index Family)](#related-projects--alpha-one-index-family)

- [FAQ](#faq)

---

## Quick Start

```python

# Load inference cost data

import json, urllib.request

url = 'https://raw.githubusercontent.com/alpha-one-index/ai-llmops-index/main/data/inference-costs.json'

costs = json.loads(urllib.request.urlopen(url).read())

# Find cheapest model per provider

for provider in set(c['provider'] for c in costs):

    cheapest = min((c for c in costs if c['provider']==provider), key=lambda x: x['input_per_1m_tokens'])

    print(f"{provider}: {cheapest['model']} - ${cheapest['input_per_1m_tokens']}/1M input")

```

**Explore the data:**

- [Interactive Dashboard](https://alpha-one-index.github.io/ai-llmops-index/) - Search, filter, and sort all data

- [Inference Costs JSON](data/inference-costs.json) - 33 models, 15+ providers

- [Failure Mode Taxonomy](FAILURE-MODES.md) - 10 categorized production failure modes

- [Vendor Profiles](data/vendor-profiles.json) - 50+ LLMOps vendors with funding data

- [Regulatory Map](data/regulatory-compliance.json) - EU AI Act, NIST, GDPR compliance mapping

## What Is the AI LLMOps Index?

The AI LLMOps Index is a structured, open-source intelligence repository covering every major dimension of the LLMOps and AI observability market. It is the fourth index in the [Alpha One Index](https://github.com/alpha-one-index) family, following:

- **[ai-infra-index](https://github.com/alpha-one-index/ai-infra-index)** — GPU specifications, cloud pricing, and AI hardware intelligence

- **[ai-trism-index](https://github.com/alpha-one-index/ai-trism-index)** — AI Trust, Risk, and Security Management (TRiSM) platforms

- **[ai-red-teaming-index](https://github.com/alpha-one-index/ai-red-teaming-index)** — Red teaming tools, LLM vulnerability databases, and adversarial evaluation frameworks

This index is maintained by practitioners for practitioners. It is updated weekly via automated data pipelines, audited for accuracy, and published in machine-readable formats (JSON, CSV, Parquet) alongside human-readable documentation. Every data point is sourced and linked. No vendor pays for placement or ranking. The index covers: observability platforms, evaluation frameworks, orchestration tools, vector databases, prompt management systems, inference optimization, deployment tooling, cost management, testing/CI-CD, and enterprise LLMOps suites.

---

## What Is LLMOps?

**LLMOps** (Large Language Model Operations) is the set of practices, tools, and workflows required to deploy, monitor, evaluate, optimize, and govern large language models in production environments. It is a specialization of MLOps that addresses the unique characteristics of foundation models: non-deterministic outputs, extremely high inference costs, emergent failure modes, sensitivity to prompt changes, rapidly evolving model versions, and complex regulatory exposure.

### LLMOps vs. MLOps: Key Distinctions

| Dimension | Traditional MLOps | LLMOps |

|---|---|---|

| **Model Training** | Central concern — retraining pipelines, feature stores, data versioning | Peripheral — most teams fine-tune or use APIs, not train from scratch |

| **Output Type** | Structured predictions (labels, scores, regressions) | Unstructured text, code, multimodal — requires semantic evaluation |

| **Evaluation** | Quantitative metrics (accuracy, F1, AUC) are sufficient | Requires LLM-as-judge, human eval, rubric-based scoring alongside metrics |

| **Prompt Versioning** | Not applicable | Critical — prompts are the primary "code" artifact, must be versioned |

| **Inference Cost** | Relatively predictable at scale | Highly variable; token economics, context length, model tier matter enormously |

| **Failure Modes** | Data drift, concept drift, distribution shift | Hallucination, prompt injection, context degradation, agent loops, PII leakage |

| **Latency Profile** | Batch workloads are common; real-time SLAs are fixed | Streaming outputs, variable latency by model/provider/load |

| **Regulatory Exposure** | GDPR data provenance, model explainability | EU AI Act transparency obligations, NIST AI RMF, GDPR Art 22 automated decisions |

### The LLMOps Lifecycle

The LLMOps lifecycle comprises six interconnected stages:

1. **Deployment** — Routing to model endpoints, versioned prompt deployment, A/B testing, canary releases, blue-green model switching, and gateway/proxy configuration.

2. **Monitoring** — Real-time observability of requests, token usage, latency, error rates, cost, and model-level health. Covers tracing, logging, and alerting.

3. **Evaluation** — Automated quality assessment using LLM-as-judge, rubric scoring, regression tests, golden dataset comparisons, and human-in-the-loop review queues.

4. **Cost Management** — Token budget enforcement, provider cost comparison, batch scheduling, caching strategies, model tier routing, and cost attribution by team/product/feature.

5. **Reliability** — SLA management, fallback routing, retry logic, circuit breakers, latency budgets, timeout cascade prevention, and multi-provider redundancy.

6. **Governance** — Prompt audit trails, PII detection and redaction, output filtering, compliance logging, bias monitoring, regulatory reporting, and access control.

These six stages map directly to the file structure of this repository and the data schemas in `data/`.

---

## What Are the Best LLMOps Observability Platforms?

The LLMOps observability market has matured rapidly since 2023. The table below covers the 12 most significant platforms as of Q1 2026, based on funding, adoption, and feature completeness. This data is maintained in [`specs/vendor-profiles.md`](specs/vendor-profiles.md).

For adversarial evaluation of these platforms (red team coverage, jailbreak detection, safety testing), see the **[AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index)**. For trust, risk, and compliance scoring of these vendors, see the **[AI TRiSM Index](https://github.com/alpha-one-index/ai-trism-index)**.

| Vendor | Founded | HQ | Total Funding | Key Differentiator | Open Source? | Pricing Model |

|---|---|---|---|---|---|---|

| [Arize AI](https://arize.com) | 2020 | San Francisco, CA | ~$62M (Series B) | Industry-leading ML + LLM observability; Phoenix OSS framework; widest integration surface | Partial (Phoenix) | Freemium + Enterprise seats |

| [Langfuse](https://langfuse.com) | 2023 | Berlin, Germany | ~$4M (Seed) | 100% open source core; self-hostable; deep LangChain/LlamaIndex integration; fastest-growing OSS LLMOps project | Yes (MIT/EE) | Free self-host; Cloud freemium; Enterprise |

| [Helicone](https://helicone.ai) | 2022 | San Francisco, CA | ~$6.5M (YC W23) | Proxy-based architecture (zero SDK changes); sub-millisecond overhead; extremely fast onboarding | Partial | Usage-based freemium |

| [Braintrust](https://braintrust.dev) | 2023 | San Francisco, CA | ~$36M | Combined eval + tracing in one platform; strong developer experience; enterprise eval workflows | No | Freemium + Enterprise |

| [Weights & Biases (Weave)](https://wandb.ai/site/weave) | 2018 | San Francisco, CA | ~$250M ($1.25B valuation) | Integrated with W&B's mature MLOps platform; Teams already on W&B get LLMOps at no extra cost | Partial | Per-seat + usage |

| [Evidently AI](https://evidentlyai.com) | 2021 | San Francisco, CA | ~$18M | Strong on data + model drift detection; extends well into LLM quality monitoring; large OSS community | Yes (Apache 2.0) | Open source + Cloud |

| [WhyLabs](https://whylabs.ai) | 2019 | Bellevue, WA | ~$40M | langkit open source; real-time data and model monitoring; ML + LLM unified; strong enterprise contracts | Partial (langkit) | Enterprise |

| [Aporia](https://aporia.com) | 2020 | Tel Aviv, Israel | ~$30M | Real-time guardrails and policy enforcement; integrates security layer into observability | No | Enterprise |

| [Fiddler AI](https://fiddler.ai) | 2018 | Menlo Park, CA | ~$71M (Series C) | Explainability + fairness + monitoring in one platform; longest track record for enterprise ML compliance | No | Enterprise |

| [HoneyHive](https://honeyhive.ai) | 2022 | San Francisco, CA | ~$7M | Unified evaluation and observability with session replay; agent workflow tracing | No | Freemium + Enterprise |

| [Phoenix (Arize OSS)](https://phoenix.arize.com) | 2023 | San Francisco, CA | Part of Arize | Fully open source observability; OTEL-native; local-first; no cloud required; popular for self-hosted setups | Yes (Apache 2.0) | Free (OSS) |

| [LangSmith](https://smith.langchain.com) | 2023 | San Francisco, CA | Part of LangChain ($25M) | Deepest LangChain integration; native LCEL tracing; debugging + eval in same UI | No | Freemium + Enterprise |

**Funding Sources:** Arize [TechCrunch Series B](https://techcrunch.com/2023/02/07/arize-ai-raises-62m/); W&B [$250M raise announcement](https://wandb.ai/site/press/series-d); Fiddler [Crunchbase Series C](https://www.crunchbase.com/organization/fiddler-labs); LangChain [$25M Series A](https://blog.langchain.dev/announcing-our-25m-series-a/); Braintrust [Crunchbase](https://www.crunchbase.com/organization/braintrust-data); Helicone [YCombinator W23](https://www.ycombinator.com/companies/helicone).

> **Tip:** For a security-focused evaluation of these platforms (penetration testing coverage, adversarial robustness, red team tooling), cross-reference the [AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index). For enterprise risk scoring and AI governance posture, see the [AI TRiSM Index](https://github.com/alpha-one-index/ai-trism-index).

---

## What Does LLM Inference Cost in 2026?

This is the **centerpiece section** of the AI LLMOps Index. The full machine-readable dataset is in [`data/inference-costs.json`](data/inference-costs.json) and [`specs/inference-cost-intelligence.md`](specs/inference-cost-intelligence.md).

Prices are **per 1 million tokens** unless noted. All prices in USD. Data sourced directly from provider pricing pages as of March 2026. Prices change frequently — this table is auto-updated weekly.

> **GPU Cost Context:** For the compute cost of self-hosting these models, cross-reference the **[AI Infrastructure Index](https://github.com/alpha-one-index/ai-infra-index)**, which tracks GPU spot/on-demand pricing, H100/A100/H200 specs, and cloud instance costs across AWS, GCP, Azure, Lambda Labs, CoreWeave, and RunPod.

> 

> **⚠️ Projected Pricing Disclaimer:** Models marked as projected (GPT-5.2, GPT-5.1, GPT-5, GPT-5 Mini, GPT-5 Nano, GPT-5.2 Pro, Claude Opus 4.6, Claude Sonnet 4.5, Claude Haiku 4.5, Gemini 3 Pro, Gemini 3 Flash) reflect anticipated pricing based on current provider trajectory and pre-release documentation. Actual pricing may differ upon official release. All other models reflect confirmed, currently available pricing verified against provider pages.

### Primary Inference Cost Table — All Major Providers (March 2026)

| Provider | Model | Input ($/1M tokens) | Output ($/1M tokens) | Context Window | Batch Discount | Notes | Source |

|---|---|---|---|---|---|---|---|

| **OpenAI** | GPT-5.2 | $1.75 | $14.00 | 400K | 50% off | Current flagship; top benchmark performance | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-5.1 | $1.25 | $10.00 | 400K | 50% off | Previous flagship | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-5 | $1.25 | $10.00 | 400K | 50% off | GA version | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-5 Mini | $0.25 | $2.00 | 400K | 50% off | Efficient mid-tier | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-5 Nano | $0.05 | $0.40 | 400K | 50% off | Cheapest OpenAI; high-volume tasks | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-5.2 Pro | $21.00 | $168.00 | 400K | 50% off | Premium tier; complex reasoning | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-4.1 | $2.00 | $8.00 | 1M | 50% off | Long context; stable legacy | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-4.1 Mini | $0.40 | $1.60 | 1M | 50% off | Long context budget | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-4o | $2.50 | $10.00 | 128K | 50% off | Legacy; multimodal | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | GPT-4o Mini | $0.15 | $0.60 | 128K | 50% off | Legacy budget option | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | o3 | $2.00 | $8.00 | 200K | 50% off | Reasoning model | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | o4 Mini | $1.10 | $4.40 | 200K | 50% off | Budget reasoning | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **OpenAI** | o3 Pro | $20.00 | $80.00 | 200K | 50% off | Premium reasoning tier | [OpenAI Pricing](https://developers.openai.com/api/docs/pricing) |

| **Anthropic** | Claude Opus 4.6 | $5.00 | $25.00 | 1M (beta) | 50% off | Most capable Claude; released Feb 2026 | [Anthropic Pricing](https://anthropic.com/pricing) |

| **Anthropic** | Claude Sonnet 4.5 | $3.00 | $15.00 | 1M (beta) | 50% off | Balanced performance/cost | [Anthropic Pricing](https://anthropic.com/pricing) |

| **Anthropic** | Claude Haiku 4.5 | $1.00 | $5.00 | 200K | 50% off | Fast and economical | [Anthropic Pricing](https://anthropic.com/pricing) |

| **Google** | Gemini 3 Pro | $2.00 | $12.00 | 1M | 50% off | Most capable Gemini | [Google AI Pricing](https://ai.google.dev/pricing) |

| **Google** | Gemini 3 Flash | $0.50 | $3.00 | 1M | 50% off | Fast; excellent cost-performance | [Google AI Pricing](https://ai.google.dev/pricing) |

| **Google** | Gemini 2.5 Pro | $1.25 | $10.00 | 2M | 50% off | Previous gen; 2M context | [Google AI Pricing](https://ai.google.dev/pricing) |

| **Google** | Gemini 2.5 Flash | $0.30 | $2.50 | 1M | 50% off | Previous gen budget | [Google AI Pricing](https://ai.google.dev/pricing) |

| **Google** | Gemini 2.5 Flash-Lite | $0.10 | $0.40 | — | 50% off | Cheapest Google model | [Google AI Pricing](https://ai.google.dev/pricing) |

| **Mistral** | Mistral Large 2411 | $2.00 | $6.00 | 131K | — | Flagship; strong on code/reasoning | [Mistral Pricing](https://mistral.ai/technology/#pricing) |

| **Mistral** | Mistral Medium 3 | $0.40 | $2.00 | 131K | — | Balanced mid-tier | [Mistral Pricing](https://mistral.ai/technology/#pricing) |

| **Mistral** | Mistral Small 3.1 | $0.35 | $0.56 | 128K | — | Efficient small model | [Mistral Pricing](https://mistral.ai/technology/#pricing) |

| **Mistral** | Mistral 7B Instruct | $0.14–$0.20 | $0.20–$0.42 | 33K | — | Via various API providers | [pricepertoken.com](https://pricepertoken.com) |

| **Together AI** | Llama 3.3 70B | $0.88 | $0.88 | 128K | — | Meta open-weight via Together | [Together AI Pricing](https://together.ai/pricing) |

| **Together AI** | Llama 3.1 8B | $0.18 | $0.18 | 128K | — | Cheapest Llama via Together | [Together AI Pricing](https://together.ai/pricing) |

| **Together AI** | Llama 3.1 405B | $3.50 | $3.50 | 128K | — | Largest Llama open-weight | [Together AI Pricing](https://together.ai/pricing) |

| **Together AI** | Llama 4 Maverick | $0.27 | $0.85 | — | — | Meta Llama 4 series | [Together AI Pricing](https://together.ai/pricing) |

| **Together AI** | DeepSeek-V3.1 | $0.60 | $1.70 | — | — | Cost-efficient frontier model | [Together AI Pricing](https://together.ai/pricing) |

| **Together AI** | Mistral Small 3 | $0.10 | $0.30 | — | — | Competitive small model pricing | [Together AI Pricing](https://together.ai/pricing) |

| **Groq** | Llama 3.3 70B | $0.59 | $0.79 | 128K | 50% off | 394 tokens/sec; ultra-low latency | [Groq Pricing](https://groq.com/pricing) |

| **Groq** | Llama 3.1 8B | $0.05 | $0.08 | 128K | 50% off | 840 tokens/sec; cheapest fast inference | [Groq Pricing](https://groq.com/pricing) |

| **Groq** | Llama 4 Scout | $0.11 | $0.34 | — | 50% off | 594 tokens/sec | [Groq Pricing](https://groq.com/pricing) |

| **Groq** | Llama 4 Maverick | $0.20 | $0.60 | — | 50% off | 562 tokens/sec | [Groq Pricing](https://groq.com/pricing) |

| **Groq** | Qwen3 32B | $0.29 | $0.59 | — | 50% off | 662 tokens/sec | [Groq Pricing](https://groq.com/pricing) |

| **Groq** | GPT-OSS 120B | $0.15 | $0.60 | — | 50% off | 500 tokens/sec | [Groq Pricing](https://groq.com/pricing) |

| **Cohere** | Command A | $2.50 | $10.00 | 128K | — | Largest Command model; enterprise RAG | [Cohere Pricing](https://cohere.com/pricing) |

| **Cohere** | Command R+ (08-2024) | $2.50 | $10.00 | 128K | — | Enterprise retrieval-augmented generation | [Cohere Pricing](https://cohere.com/pricing) |

| **Cohere** | Command R | $0.15 | $0.60 | 128K | — | Efficient RAG workloads | [Cohere Pricing](https://cohere.com/pricing) |

| **Cohere** | Command R7B | $0.0375 | $0.15 | 128K | — | Cheapest Cohere; edge/local deployments | [Cohere Pricing](https://cohere.com/pricing) |

| **AWS Bedrock** | (All models) | Varies | Varies | Per-model | — | Hosts OpenAI, Anthropic, Meta, Mistral, Cohere with markup; enterprise SLAs | [AWS Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/) |

| **Azure OpenAI** | (OpenAI models) | Mirrors OpenAI | Mirrors OpenAI | Per-model | — | Adds PTU (Provisioned Throughput Units) for predictable latency | [Azure OpenAI Pricing](https://azure.microsoft.com/en-us/pricing/details/cognitive-services/openai-service/) |

| **Perplexity** | Sonar models | Varies | Varies | — | — | Via pplx-api; optimized for search-grounded queries | [Perplexity API](https://docs.perplexity.ai/docs/pricing) |

| **Replicate** | Open models | Per-second | Per-second | Per-model | — | Per-second billing; flexible for open-weight models | [Replicate Pricing](https://replicate.com/pricing) |

| **Fireworks AI** | Open models | Competitive | Competitive | Per-model | — | Similar pricing to Together AI; optimized inference serving | [Fireworks Pricing](https://fireworks.ai/pricing) |

**Notes on long-context pricing:**

- **Anthropic:** Inputs >200K tokens incur 2× input price and 1.5× output price

- **Google:** Inputs >200K tokens incur 2× pricing on Gemini 2.5 Pro

- **OpenAI Batch API:** 50% discount available on most models for asynchronous workloads with 24-hour completion window

### Secondary Table: Cheapest Options by Model Size Category (March 2026)

| Category | Cheapest Option | Input $/1M | Output $/1M | Provider | Use Case |

|---|---|---|---|---|---|

| **Nano / Edge (<7B)** | Groq Llama 3.1 8B | $0.05 | $0.08 | Groq | Classification, extraction, simple Q&A |

| **Nano / Edge (<7B)** | Cohere Command R7B | $0.0375 | $0.15 | Cohere | RAG, structured outputs |

| **Small (7B–20B)** | Together AI Mistral Small 3 | $0.10 | $0.30 | Together AI | General tasks at scale |

| **Small (7B–20B)** | Groq Llama 4 Scout | $0.11 | $0.34 | Groq | Fast small model with speed SLA |

| **Medium (20B–70B)** | Groq Llama 3.3 70B | $0.59 | $0.79 | Groq | High-quality; ultra-low latency |

| **Medium (20B–70B)** | Together AI DeepSeek-V3.1 | $0.60 | $1.70 | Together AI | Frontier-quality at medium price |

| **Large (70B–200B)** | GPT-5 Nano (OpenAI API) | $0.05 | $0.40 | OpenAI | OpenAI's budget tier; full API ecosystem |

| **Large (70B–200B)** | Mistral Small 3.1 | $0.35 | $0.56 | Mistral | Strong European alternative |

| **Frontier** | GPT-5 Mini | $0.25 | $2.00 | OpenAI | Best frontier-class budget option |

| **Frontier** | Gemini 3 Flash | $0.50 | $3.00 | Google | Google's fast frontier tier |

| **Premium Frontier** | Claude Opus 4.6 | $5.00 | $25.00 | Anthropic | Top reasoning and safety |

| **Premium Frontier** | GPT-5.2 | $1.75 | $14.00 | OpenAI | Current OpenAI flagship |

> **Cost Modeling Tip:** For a 1M token conversation (balanced input/output), running on GPT-5 Nano costs ~$0.225, Gemini 2.5 Flash-Lite costs ~$0.25, and Claude Opus 4.6 costs ~$15.00 — a 67× spread. Model selection is the single highest-impact LLMOps cost lever.

---

## What Are the Most Common LLM Production Failure Modes?

Understanding failure modes is prerequisite to selecting monitoring tools. The table below is the foundation of this index's failure mode taxonomy, with full records in [`data/failure-modes.json`](data/failure-modes.json). For adversarial failure modes and red team vectors, see the **[AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index)**.

| # | Failure Mode | Category | Risk Level | Detection Method | Mitigation Tools | Regulatory Implication | Source |

|---|---|---|---|---|---|---|---|

| 1 | **Hallucination / Confabulation** | Output Quality | 🔴 Critical | LLM-as-judge grounding checks; factuality scoring; RAG retrieval comparison | Arize, Langfuse (scoring), Braintrust (evals), Guardrails AI | EU AI Act Art 13 (transparency); NIST AI RMF MANAGE 4.2 | [Guo et al., 2022](https://arxiv.org/abs/2202.03629); [NIST AI RMF](https://airc.nist.gov/RMF) |

| 2 | **Context Window Degradation (Lost-in-the-Middle)** | Retrieval/Context | 🟠 High | Positional attention heatmaps; retrieval relevance scoring; chunk placement experiments | LlamaIndex, Phoenix retrieval tracing | NIST AI RMF MEASURE 2.5 | [Liu et al., 2023](https://arxiv.org/abs/2307.03172) |

| 3 | **Prompt Injection in Production** | Security | 🔴 Critical | Input pattern matching; indirect injection detection; adversarial prompt monitoring | Rebuff, LakeraGuard, Aporia guardrails, Vigilance AI | EU AI Act Annex III (high-risk systems); GDPR Art 25 | [OWASP LLM Top 10 #1](https://owasp.org/www-project-top-10-for-large-language-model-applications/) |

| 4 | **Latency Spikes / Timeout Cascades** | Reliability | 🟠 High | P95/P99 latency tracking; timeout rate monitoring; provider health dashboards | Helicone (request analytics), OpenLLMetry, Langfuse trace analytics | SLA/SLO violation; operational risk | AWS/Azure provider health APIs; [DORA metrics](https://dora.dev) |

| 5 | **Cost Overruns (Runaway Tokens)** | Cost Management | 🟠 High | Per-request token budget enforcement; anomaly detection on token counts; cost dashboards | Helicone (cost tracking), Portkey, LangSmith token monitoring | Financial risk; SOX implications for public companies | [Databricks LLMOps survey 2024](https://databricks.com) |

| 6 | **Prompt Drift / Model Version Regression** | Versioning | 🟡 Medium | Automated regression suites on prompt changes; eval dataset baselines; version pinning | LangSmith, Braintrust (versioned evals), Weights & Biases Weave | NIST AI RMF MANAGE 1.3 (version control) | [Liang et al., 2023 HELM](https://arxiv.org/abs/2211.09110) |

| 7 | **RAG Retrieval Failures** | Retrieval | 🟠 High | Context relevance scoring; faithfulness scoring; retrieval hit-rate monitoring | Phoenix (retrieval evals), Ragas, Langfuse retrieval traces | EU AI Act Art 13 (data accuracy); NIST MEASURE 2.6 | [Ragas framework](https://docs.ragas.io); [Gao et al. 2023](https://arxiv.org/abs/2312.10997) |

| 8 | **Agent Loop Failures** | Agent Reliability | 🔴 Critical | Max-step enforcement; loop detection; tool call anomaly monitoring; action logging | LangSmith (agent tracing), HoneyHive, Arize agent monitoring | EU AI Act Art 14 (human oversight); NIST GOVERN 1.4 | [ReAct paper](https://arxiv.org/abs/2210.03629); OWASP LLM Top 10 |

| 9 | **PII Leakage in Multi-Tenant Deployments** | Privacy/Security | 🔴 Critical | PII entity detection in outputs; prompt/response scanning; tenant isolation audits | Microsoft Presidio, AWS Comprehend, Guardrails AI, WhyLabs data quality | GDPR Art 5 (data minimization); CCPA; EU AI Act Art 10 | [OWASP LLM Top 10 #6](https://owasp.org/www-project-top-10-for-large-language-model-applications/) |

| 10 | **Output Consistency Failures (Temperature Instability)** | Determinism | 🟡 Medium | Statistical variance tracking across identical prompts; distribution shift detection | Evidently AI, WhyLabs, Arize distribution monitoring | NIST AI RMF MEASURE 2.2 (consistency); audit trail requirements | [Ouyang et al. RLHF paper](https://arxiv.org/abs/2203.02155) |

**Risk Level Key:** 🔴 Critical (production incident likely, regulatory exposure) | 🟠 High (degraded user experience, cost exposure) | 🟡 Medium (quality degradation, manageable)

> **Red Team Cross-Reference:** Failure modes #3 (prompt injection) and #9 (PII leakage) are primary attack surfaces catalogued in the [AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index), which includes adversarial prompt datasets, jailbreak taxonomies, and pen-testing tool listings.

---

## What LLMOps Tools Are Required for EU AI Act Compliance?

The EU AI Act (entered into force August 2024, with phased obligations through 2026–2027) creates direct LLMOps obligations for operators of high-risk AI systems and providers of general-purpose AI (GPAI) models. The US Executive Order 14110 and NIST AI RMF provide complementary frameworks. The full compliance dataset is in [`data/regulatory-compliance.json`](data/regulatory-compliance.json). For risk assessment tooling, see the **[AI TRiSM Index](https://github.com/alpha-one-index/ai-trism-index)**.

| Framework | Requirement | Applies To | LLMOps Obligation | Compliant Tools | Deadline | Penalty |

|---|---|---|---|---|---|---|

| **EU AI Act Art 12** | Logging and record-keeping for high-risk AI | High-risk AI system operators; GPAI model providers | Maintain immutable logs of all LLM inputs, outputs, and decisions for audit | Langfuse (audit logs), Arize (trace storage), Fiddler AI, WhyLabs | Aug 2026 (high-risk) | Up to €30M or 6% global turnover |

| **EU AI Act Art 13** | Transparency and information provision | All AI system providers | Provide users with disclosure of AI involvement; document model capabilities and limitations | Arize (model cards), Evidently AI (data docs), Fiddler AI (explainability) | Aug 2026 | Up to €20M or 4% global turnover |

| **EU AI Act Art 14** | Human oversight | High-risk AI systems | Enable human override of LLM outputs; implement stop mechanisms; agent loop controls | HoneyHive (human-in-loop), LangSmith (intervention), Aporia guardrails | Aug 2026 | Up to €30M or 6% global turnover |

| **NIST AI RMF MANAGE 4.2** | Risk response and mitigation | US federal agencies; voluntary adoption | Document and operationalize risk response plans; track hallucination/failure rates | Arize, Braintrust (eval baselines), Evidently AI | Ongoing (voluntary) | Federal procurement implications |

| **NIST AI RMF MEASURE 2.5** | AI system performance monitoring | US federal agencies; voluntary | Continuous measurement of LLM quality metrics; bias and fairness monitoring | Fiddler AI, WhyLabs, Arize, Evidently AI | Ongoing (voluntary) | Federal procurement implications |

| **ISO/IEC 42001 Clause 9** | Performance evaluation of AI management systems | ISO-certified organizations | Internal audits of AI systems; management review; monitoring effectiveness | Vanta (compliance automation), Drata + LLMOps integrations | Ongoing (voluntary) | Certification loss |

| **GDPR Art 22** | Automated individual decision-making | Any EU data processor using LLMs for decisions | Provide human review option; document decision logic; enable data subject rights | Fiddler AI (explainability), Arize (trace audit), WhyLabs (data lineage) | Applicable now | Up to €20M or 4% global turnover |

| **US EO 14110 Sec 4** | AI safety and security standards for dual-use models | US AI model developers | Red-team testing before deployment; report safety results to NIST | Red team tooling: see [AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index) | Ongoing | Federal contract risk |

| **SR 11-7 (Federal Reserve)** | Model risk management | US financial institutions using LLMs | Model validation, documentation, ongoing monitoring, challenger model testing | Fiddler AI (financial model monitoring), Evidently AI, Model Validation frameworks | Applicable now | Regulatory action from Fed/OCC |

> **Compliance Cross-Reference:** For a complete AI governance risk taxonomy and vendor scoring for TRiSM (Trust, Risk, Security Management), see the **[AI TRiSM Index](https://github.com/alpha-one-index/ai-trism-index)**. For red team testing requirements under EO 14110 and EU AI Act Art 9, see the **[AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index)**.

---

## How Do LLMOps Tools Integrate With Each Other?

Modern LLM production stacks are composed of multiple tool layers. This section provides stack compatibility matrices across the four key integration axes. Full machine-readable compatibility data is in [`data/stack-compatibility.json`](data/stack-compatibility.json).

**Legend:** ✅ Full native integration | ⚠️ Partial / via OpenTelemetry | ❌ No integration | 🔄 In Progress / Beta

### Matrix 1: Observability Platform × LLM Provider

| | OpenAI | Anthropic | Google (Gemini) | Mistral | Meta/Llama (OSS) |

|---|---|---|---|---|---|

| **Langfuse** | ✅ | ✅ | ✅ | ✅ | ✅ |

| **Arize AI / Phoenix** | ✅ | ✅ | ✅ | ⚠️ | ✅ |

| **Helicone** | ✅ | ✅ | ✅ | ✅ | ⚠️ |

| **W&B Weave** | ✅ | ✅ | ✅ | ⚠️ | ✅ |

| **LangSmith** | ✅ | ✅ | ✅ | ✅ | ✅ |

| **Evidently AI** | ✅ | ⚠️ | ⚠️ | ⚠️ | ✅ |

| **WhyLabs** | ✅ | ✅ | ⚠️ | ⚠️ | ✅ |

| **Braintrust** | ✅ | ✅ | ✅ | ⚠️ | ✅ |

### Matrix 2: Orchestration Framework × Observability Platform

| | Langfuse | Arize/Phoenix | Helicone | W&B Weave | LangSmith |

|---|---|---|---|---|---|

| **LangChain** | ✅ | ✅ | ✅ | ✅ | ✅ (Native) |

| **LlamaIndex** | ✅ | ✅ | ⚠️ | ✅ | ✅ |

| **DSPy** | ⚠️ | ✅ | ❌ | ✅ | ⚠️ |

| **Haystack** | ✅ | ⚠️ | ⚠️ | ⚠️ | ⚠️ |

| **Semantic Kernel** | ⚠️ | ⚠️ | ⚠️ | ❌ | 🔄 |

| **CrewAI** | ✅ | ✅ | ⚠️ | ⚠️ | ✅ |

| **AutoGen** | ⚠️ | ⚠️ | ❌ | ⚠️ | 🔄 |

### Matrix 3: Vector Database × Orchestration Framework

| | LangChain | LlamaIndex | DSPy | Haystack |

|---|---|---|---|---|

| **Pinecone** | ✅ | ✅ | ⚠️ | ✅ |

| **Weaviate** | ✅ | ✅ | ⚠️ | ✅ |

| **Qdrant** | ✅ | ✅ | ⚠️ | ✅ |

| **Chroma** | ✅ | ✅ | ✅ | ⚠️ |

| **pgvector** | ✅ | ✅ | ⚠️ | ✅ |

| **Milvus** | ✅ | ✅ | ❌ | ✅ |

| **MongoDB Atlas** | ✅ | ✅ | ❌ | ⚠️ |

### Matrix 4: Evaluation Framework × Deployment Platform

| | OpenAI API | Anthropic API | AWS Bedrock | Azure OpenAI | Self-hosted (vLLM/Ollama) |

|---|---|---|---|---|---|

| **Braintrust** | ✅ | ✅ | ⚠️ | ✅ | ⚠️ |

| **LangSmith Eval** | ✅ | ✅ | ⚠️ | ✅ | ✅ |

| **Ragas** | ✅ | ✅ | ⚠️ | ✅ | ✅ |

| **DeepEval** | ✅ | ✅ | ⚠️ | ✅ | ✅ |

| **Promptfoo** | ✅ | ✅ | ✅ | ✅ | ✅ |

| **Arize Evals** | ✅ | ✅ | ✅ | ✅ | ⚠️ |

| **W&B Weave Evals** | ✅ | ✅ | ⚠️ | ✅ | ✅ |

---

## What Is the LLMOps Market Size?

The LLMOps market is one of the fastest-growing software segments in enterprise AI infrastructure. Market data from analyst firms converges on a consistent picture of explosive growth driven by enterprise LLM adoption, regulatory compliance requirements, and the operationalization of AI models at scale.

| Year | Market Size | Source |

|---|---|---|

| 2023 | ~$0.4B | MarketsandMarkets, Grand View Research |

| 2024 | ~$0.7B | IDC AI Ops Market Tracker |

| 2025 | ~$1.2B | MarketsandMarkets LLMOps Report 2025 |

| 2026 | ~$1.75B (est.) | Projected at ~45% CAGR |

| 2027 | ~$2.5B (est.) | Projected at ~45% CAGR |

| 2028 | ~$3.7B (est.) | Projected at ~45% CAGR |

| 2029 | ~$5.3B (est.) | Projected at ~45% CAGR |

| 2030 | ~$7.8B (est.) | Projected at ~45% CAGR |

| 2031 | ~$11.4B | MarketsandMarkets LLMOps Forecast |

**CAGR: ~45.1% (2025–2031)**

### Key Growth Drivers

1. **Enterprise LLM Deployment at Scale** — Gartner projects 80%+ of enterprises will have deployed production LLM applications by 2026, creating demand for monitoring and observability tooling.

2. **EU AI Act Compliance Mandates** — The August 2026 deadline for high-risk AI system compliance is driving procurement of logging, audit trail, and transparency tooling.

3. **Inference Cost Management** — As LLM inference costs become a material budget line item, dedicated cost optimization and observability tooling is required.

4. **Model Version Management** — Rapid model iteration (GPT-4 → GPT-4o → GPT-5 → GPT-5.2 within 24 months) creates demand for regression testing and version management platforms.

5. **Agent and Multi-Step Workflow Proliferation** — Agentic architectures introduce novel failure modes (loops, tool call failures) requiring specialized tracing and monitoring.

**Adjacent market context:**

- MLOps market: ~$4.5B in 2025 (Grand View Research), growing at ~43% CAGR

- AI observability (broader): ~$8B in 2025 including traditional ML monitoring

- Vector database market: ~$1.5B in 2025 (a subset of LLMOps infrastructure)

---

## How Is This Data Structured?

All data is published in structured JSON format in the `data/` directory, with corresponding CSV exports in `kaggle/` and Parquet in the HuggingFace dataset. The Croissant ML metadata file (`croissant.json`) describes all datasets per the [MLCommons Croissant specification](https://github.com/mlcommons/croissant).

| File | Description | Update Frequency | Format |

|---|---|---|---|

| `data/inference-costs.json` | LLM inference pricing by provider/model | Weekly | JSON, CSV |

| `data/vendor-profiles.json` | LLMOps vendor profiles (funding, features, HQ) | Monthly | JSON, CSV |

| `data/failure-modes.json` | LLM production failure mode taxonomy | Quarterly | JSON |

| `data/stack-compatibility.json` | Tool integration compatibility matrix | Monthly | JSON |

| `data/regulatory-compliance.json` | Regulatory requirements and compliant tools | Quarterly | JSON |

| `data/market-sizing.json` | Market size estimates by analyst firm | Quarterly | JSON |

| `data/eval-frameworks.json` | LLM evaluation framework comparison | Monthly | JSON, CSV |

| `data/vector-databases.json` | Vector database comparison (performance, pricing) | Monthly | JSON, CSV |

| `data/history/` | Time-series snapshots of inference costs | Weekly (rolling 52w) | JSON |

All schemas are documented in [`SCHEMA.md`](SCHEMA.md). Data quality is enforced by automated validation scripts in `scripts/validate.py`.

---

## Live Data

Real-time and current data is accessible via the following endpoints:

| Resource | URL | Format |

|---|---|---|

| GitHub Pages Dashboard | https://alpha-one-index.github.io/ai-llmops-index | Interactive Web |

| HuggingFace Dataset | https://huggingface.co/datasets/alpha-one-index/ai-llmops-index | Parquet / CSV / JSON |

| Kaggle Dataset | https://www.kaggle.com/datasets/alphaoneindex/ai-llmops-index | CSV / Notebooks |

| Raw Inference Costs JSON | https://raw.githubusercontent.com/alpha-one-index/ai-llmops-index/main/data/inference-costs.json | JSON |

| Raw Vendor Profiles JSON | https://raw.githubusercontent.com/alpha-one-index/ai-llmops-index/main/data/vendor-profiles.json | JSON |

---

## Vendors Tracked

The index tracks 50+ vendors across 10 categories. Full profiles are in [`specs/vendor-profiles.md`](specs/vendor-profiles.md).

| Category | Vendors Tracked | Top Vendors | Notes |

|---|---|---|---|

| **Observability** | 10 | Arize AI, Langfuse, Helicone, LangSmith, W&B Weave, WhyLabs, HoneyHive, Evidently AI, Fiddler AI, Aporia | Most active category; OSS and proprietary options |

| **Evaluation Frameworks** | 8 | Braintrust, Ragas, DeepEval, Promptfoo, Giskard, UpTrain, TruLens, Cleanlab | LLM-as-judge and rubric-based eval tools |

| **Orchestration** | 8 | LangChain, LlamaIndex, DSPy, Haystack, Semantic Kernel, CrewAI, AutoGen, Flowise | Framework choices determine observability integration options |

| **Vector Databases** | 7 | Pinecone, Weaviate, Qdrant, Chroma, pgvector, Milvus, MongoDB Atlas Vector | Core RAG infrastructure layer |

| **Prompt Management** | 5 | PromptLayer, Pezzo, Agenta, LangSmith Prompts, Langfuse Prompt Mgmt | Versioning, A/B testing, collaboration |

| **Inference Optimization** | 5 | vLLM, Ollama, TensorRT-LLM, OpenLLM, Triton Inference Server | Self-hosted inference efficiency |

| **Deployment** | 5 | Modal, Replicate, Baseten, RunPod, SkyPilot | Serverless and managed inference deployment |

| **Cost Management** | 4 | Portkey, Helicone (cost module), LangFuse (usage), OpenMeter | Token budget, cost attribution, routing |

| **Testing / CI-CD** | 4 | Promptfoo, Giskard, Pytest-LLM, MLflow | Automated LLM regression testing |

| **Enterprise LLMOps** | 5 | Databricks AI Gateway, Azure AI Studio, AWS SageMaker, Google Vertex AI, IBM watsonx | Full-stack enterprise platforms |

---

## Repository Structure

```

ai-llmops-index/

├── .github/                    # GitHub Actions workflows

│   └── workflows/              # Automation pipelines

├── README.md                    # This file

├── CHANGELOG.md                 # Version history and data updates

├── METHODOLOGY.md               # Data collection and validation methodology

├── SCHEMA.md                    # JSON data schema documentation

├── LICENSE                      # MIT License

├── croissant.json               # MLCommons Croissant ML metadata

├── provenance.md                # Data source provenance documentation

├── .zenodo.json                # Zenodo archive metadata

├── BENCHMARKS.md               # Model performance comparisons

├── CITATION.cff                # Citation metadata

├── CONTRIBUTING.md             # Contribution guidelines

├── COST-CALCULATOR.md          # Inference cost estimation tool

├── FAILURE-MODES.md            # LLM failure mode taxonomy

├── HUGGINGFACE_DATASET_CARD.md # HuggingFace dataset card

├── INTEGRATION-GUIDE.md        # Stack integration recommendations

├── SECURITY.md                 # Security policy

├── VENDOR-COMPARISON.md        # Vendor decision matrix

├── _config.yml                 # GitHub Pages configuration

├── dataprov.json               # Data provenance metadata

├── index.html                  # GitHub Pages dashboard

├── llms.txt                    # AI citation optimization

├── llms-full.txt               # Extended AI-readable content

├── pyproject.toml              # Python project configuration

├── requirements.txt            # Python dependencies

├── robots.txt                  # Search engine directives

├── schema.jsonld               # Structured data markup

├── sitemap.xml                 # SEO sitemap

├── data/

│   ├── inference-costs.json     # LLM pricing by provider/model (weekly)

│   ├── vendor-profiles.json     # Vendor profiles and metadata

│   ├── failure-modes.json       # LLM failure mode taxonomy

│   ├── stack-compatibility.json # Tool integration matrix

│   ├── regulatory-compliance.json # Compliance requirements mapping

│   ├── market-sizing.json       # Market size estimates

│   ├── eval-frameworks.json     # Evaluation framework comparison

│   ├── vector-databases.json    # Vector DB comparison

│   └── history/                 # Time-series snapshots (52-week rolling)

├── specs/

│   ├── vendor-profiles.md       # Human-readable vendor profiles

│   ├── inference-cost-intelligence.md  # Full pricing analysis

│   ├── stack-compatibility.md   # Integration matrix documentation

│   └── regulatory-compliance.md # Regulatory compliance guide

├── kaggle/

│   ├── *.csv                    # CSV exports of all data files

│   └── notebooks/               # Example analysis notebooks

└── scripts/

    ├── validate.py              # Automated data validation

    ├── update-pricing.py        # Weekly pricing update pipeline

    └── generate-reports.py      # Report generation utilities

    └── verify_pricing.py       # OpenRouter cross-reference verification

```

---

## Quick Links

| Resource | Link |

|---|---|

| Full Vendor Profiles | [specs/vendor-profiles.md](specs/vendor-profiles.md) |

| Inference Cost Intelligence | [specs/inference-cost-intelligence.md](specs/inference-cost-intelligence.md) |

| Regulatory Compliance Guide | [specs/regulatory-compliance.md](specs/regulatory-compliance.md) |

| Stack Compatibility Matrix | [specs/stack-compatibility.md](specs/stack-compatibility.md) |

| Methodology | [METHODOLOGY.md](METHODOLOGY.md) |

| Changelog | [CHANGELOG.md](CHANGELOG.md) |

| Data Schema | [SCHEMA.md](SCHEMA.md) |

| Croissant Metadata | [croissant.json](croissant.json) |

| Data Provenance | [provenance.md](provenance.md) |

| Live Inference Costs | [data/inference-costs.json](data/inference-costs.json) |

---

## Quick Start — Programmatic Data Access

### Python (pandas)

```python

import pandas as pd

# Load inference cost data

df = pd.read_csv(

    "https://raw.githubusercontent.com/alpha-one-index/ai-llmops-index/main/kaggle/inference-costs.csv"

)

# Find cheapest models for high-volume output workloads

cheap = df.sort_values("output_per_million_usd").head(10)

print(cheap[["provider", "model", "input_per_million_usd", "output_per_million_usd"]])

# Filter by context window >= 1M tokens

long_context = df[df["context_window_tokens"] >= 1_000_000]

print(long_context[["provider", "model", "context_window_tokens", "input_per_million_usd"]])

```

### HuggingFace Datasets

```python

from datasets import load_dataset

# Load the full AI LLMOps Index dataset

ds = load_dataset("alpha-one-index/ai-llmops-index", "inference-costs")

# Convert to pandas DataFrame

df = ds["train"].to_pandas()

# Filter to frontier models

frontier = df[df["model_tier"] == "frontier"]

print(frontier[["provider", "model", "input_per_million_usd", "output_per_million_usd"]])

```

### Direct JSON via curl

```bash

# Get all inference cost data

curl -s https://raw.githubusercontent.com/alpha-one-index/ai-llmops-index/main/data/inference-costs.json \

  | jq '.providers[] | select(.name == "OpenAI") | .models[] | {model: .name, input: .input_per_million_usd, output: .output_per_million_usd}'

# Get all vendor profiles

curl -s https://raw.githubusercontent.com/alpha-one-index/ai-llmops-index/main/data/vendor-profiles.json \

  | jq '.vendors[] | select(.category == "observability") | {name: .name, funding: .total_funding_usd}'

```

---

## Data Provenance & Validation

All data in this index is collected from primary sources (provider pricing pages, company websites, SEC/regulatory filings, peer-reviewed publications) and validated through a four-tier methodology. Full methodology is documented in [METHODOLOGY.md](METHODOLOGY.md).

### Four-Tier Validation Methodology

| Tier | Method | Applies To | Frequency |

|---|---|---|---|

| **Tier 1: Primary Source** | Direct collection from provider pricing pages and official documentation | Pricing data, model specs, API documentation | Weekly automated |

| **Tier 2: Cross-Reference** | Comparison across minimum 2 independent sources (e.g., provider page + pricepertoken.com + community verification) | All pricing data | Weekly |

| **Tier 3: Community Validation** | Pull requests from community contributors with source citations required | Vendor profiles, integration compatibility | Per PR |

| **Tier 4: Self-Audit** | Automated CI/CD checks: schema validation, price range anomaly detection, dead link detection, source URL reachability | All data files | Daily via GitHub Actions |

### Automated Self-Auditing Checks

The following checks run on every push and on a weekly schedule via GitHub Actions:

- **Schema Validation:** All JSON files validated against schemas in `SCHEMA.md` using `jsonschema`

- **Price Anomaly Detection:** Statistical outlier detection flags price changes >50% for manual review

- **Source URL Reachability:** All source URLs checked for HTTP 200 response

- **Completeness Check:** All required fields present in every record

- **Cross-Provider Price Consistency:** Prices for identical models compared across providers (e.g., Claude Opus 4.6 on Anthropic direct vs. AWS Bedrock)

- **Date Freshness:** Any record not updated within 14 days is flagged for review

---

## Related Projects — Alpha One Index Family

The AI LLMOps Index is the fourth publication in the Alpha One Index family of open intelligence repositories for AI infrastructure and governance.

| Index | Repository | Focus | Records | Updated |

|---|---|---|---|---|

| **AI LLMOps Index** | [alpha-one-index/ai-llmops-index](https://github.com/alpha-one-index/ai-llmops-index) | LLMOps platforms, inference costs, failure modes, compliance | 50+ vendors, 45+ models | Weekly |

| **AI Infrastructure Index** | [alpha-one-index/ai-infra-index](https://github.com/alpha-one-index/ai-infra-index) | GPU specs, cloud pricing, AI hardware | 100+ GPUs, 15+ cloud providers | Weekly |

| **AI TRiSM Index** | [alpha-one-index/ai-trism-index](https://github.com/alpha-one-index/ai-trism-index) | AI Trust, Risk, Security Management platforms | 60+ vendors | Monthly |

| **AI AppSec Index** | [alpha-one-index/ai-appsec-index](https://github.com/alpha-one-index/ai-appsec-index) | AI remediation benchmarks, ASPM, CRA compliance, false positives | 30+ tools, 7 frameworks | Weekly |

| **AI Red Teaming Index** | [alpha-one-index/ai-red-teaming-index](https://github.com/alpha-one-index/ai-red-teaming-index) | Red teaming tools, LLM vulnerability databases | 40+ tools, 200+ vulnerabilities | Monthly |

### How the Four Indexes Form a Complete LLM Production Intelligence Layer

**Infrastructure → LLMOps (Cost Bridge):** The [AI Infrastructure Index](https://github.com/alpha-one-index/ai-infra-index) tracks GPU spot pricing and cloud instance costs for self-hosted inference (H100, A100, H200, MI300X). When evaluating LLM inference cost options, comparing managed API pricing (in this index) against self-hosted GPU costs (in the infrastructure index) is essential for organizations above ~1B tokens/month where self-hosting may break even against API costs. A dedicated cross-index cost comparison is published at `specs/inference-cost-intelligence.md`.

**LLMOps → TRiSM (Governance Bridge):** The [AI TRiSM Index](https://github.com/alpha-one-index/ai-trism-index) covers trust, risk, and security management platforms that sit above the LLMOps observability layer. Where LLMOps tools monitor production outputs, TRiSM platforms provide risk scoring, governance frameworks, and enterprise AI policy enforcement. The compliance mapping in this index's `data/regulatory-compliance.json` is synchronized with the TRiSM index's governance taxonomy.

**LLMOps → Red Teaming (Security Bridge):** The [AI Red Teaming Index](https://github.com/alpha-one-index/ai-red-teaming-index) catalogs adversarial testing tools, jailbreak taxonomies, and LLM vulnerability databases. Production failure modes #3 (prompt injection) and #9 (PII leakage) documented in this index correspond directly to red team attack categories in that index. Organizations should cross-reference both indexes when designing their LLM security posture.

---

## FAQ

**Q: How often is pricing data updated?**

A: Inference cost data is updated weekly via automated collection from provider pricing pages. A GitHub Actions workflow runs every Monday at 00:00 UTC, fetches current prices, validates them, and commits any changes to `data/inference-costs.json`. All changes are logged in `CHANGELOG.md`. Source URLs are included in every record so you can verify independently.

**Q: Which LLMOps platform should I choose for my use case?**

A: It depends on your architecture and requirements. For teams using LangChain/LlamaIndex: LangSmith or Langfuse offer the deepest integrations. For proxy-based observability with zero SDK changes: Helicone is the fastest to deploy. For enterprise ML + LLM in one platform: Weights & Biases Weave (if already on W&B) or Arize AI. For fully open source, self-hosted: Phoenix (by Arize) or Langfuse self-hosted. For compliance-first enterprise use: Fiddler AI. See [`specs/vendor-profiles.md`](specs/vendor-profiles.md) for a full decision matrix.

**Q: What is the cheapest way to run LLM inference in production at scale?**

A: As of March 2026, the cheapest options by tier are: (1) **Nano/edge workloads:** Groq Llama 3.1 8B at $0.05 input / $0.08 output per 1M tokens at 840 tokens/sec; (2) **Small models:** Together AI Mistral Small 3 at $0.10 / $0.30; (3) **Frontier budget:** GPT-5 Nano at $0.05 / $0.40 or Gemini 2.5 Flash-Lite at $0.10 / $0.40; (4) **Self-hosted:** At ~1B tokens/month, an H100 on spot pricing may achieve lower effective cost than all managed APIs. See the [AI Infrastructure Index](https://github.com/alpha-one-index/ai-infra-index) for GPU pricing.

**Q: How does this index define "LLMOps" vs. "MLOps"?**

A: LLMOps is a specialization of MLOps focused specifically on large language models in production. The key distinctions are: (1) LLMOps treats prompts as versioned artifacts; (2) LLMOps requires semantic evaluation (not just quantitative metrics); (3) LLMOps addresses unique failure modes (hallucination, prompt injection, context degradation); (4) LLMOps involves token economics as a primary cost driver; (5) LLMOps has specific regulatory exposure under the EU AI Act's GPAI provisions. See the [What Is LLMOps?](#what-is-llmops) section for a full comparison table.

**Q: Does this index track open-source models?**

A: Yes. We track open-weight models (Llama 4, Mistral, Qwen, DeepSeek) and the inference providers that host them (Together AI, Groq, Fireworks AI, Replicate). We also track self-hosted inference frameworks (vLLM, Ollama, TensorRT-LLM). Self-hosted GPU costs are cross-referenced from the [AI Infrastructure Index](https://github.com/alpha-one-index/ai-infra-index).

**Q: What regulatory frameworks does the compliance data cover?**

A: The index covers: EU AI Act (Art 9, 12, 13, 14), NIST AI RMF (GOVERN, MAP, MEASURE, MANAGE functions), ISO/IEC 42001, GDPR Art 22, US Executive Order 14110, and SR 11-7 (Federal Reserve model risk management). For a comprehensive AI governance taxonomy, see the [AI TRiSM Index](https://github.com/alpha-one-index/ai-trism-index).

**Q: How do I contribute pricing data or vendor corrections?**

A: Open a pull request with your change and include: (1) the source URL, (2) the date you verified the data, and (3) a brief description of what changed. All PRs go through automated schema validation and are manually reviewed before merge. See [CONTRIBUTING.md](CONTRIBUTING.md) for the full contribution guide.

**Q: Is this data available for commercial use?**

A: Yes. The repository is MIT licensed. The HuggingFace and Kaggle datasets are published under CC BY 4.0. You can use, redistribute, and build on this data in commercial products with attribution. See [LICENSE](LICENSE) for full terms.

**Q: How does the LLMOps market growth compare to the broader MLOps market?**

A: The LLMOps market (~45% CAGR) is growing faster than the broader MLOps market (~43% CAGR) due to the rapid enterprise adoption of generative AI applications. However, the two markets are converging: most major MLOps platforms (Weights & Biases, Evidently AI, Fiddler AI, WhyLabs) have added LLM-specific features, blurring the boundary. The LLMOps market at $1.2B in 2025 is currently smaller than the ~$4.5B MLOps market but is projected to approach parity by 2028.

---

## Citation

If you use data from the AI LLMOps Index in research, publications, or commercial products, please cite:

```bibtex

@dataset{alpha_one_llmops_index_2025,

  author       = {Alpha One Index},

  title        = {AI LLMOps Index: Open LLMOps Intelligence Repository},

  year         = {2025},

  publisher    = {GitHub},

  version      = {1.0.0},

  url          = {https://github.com/alpha-one-index/ai-llmops-index},

  note         = {Includes LLM inference cost data, vendor profiles, failure mode taxonomy, stack compatibility, and regulatory compliance mapping. Updated weekly. Also available at https://huggingface.co/datasets/alpha-one-index/ai-llmops-index and https://www.kaggle.com/datasets/alphaoneindex/ai-llmops-index}

}

```

---

## License

MIT License — see [LICENSE](LICENSE) for details. Data available under CC BY 4.0 on HuggingFace and Kaggle.

---

*AI LLMOps Index is part of the [Alpha One Index](https://github.com/alpha-one-index) family. Not affiliated with any vendor. No sponsored placements. All data sourced from public primary sources.*
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