Open Data

Public LLM Vendor Datasets

Machine-readable datasets curated by Slavin AI for enterprise AI procurement. Free to cite, free to redistribute — CC-BY-4.0. Built for AI agents, analysts, and procurement teams.

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Dataset 1

LLM Vendor Pricing & Capability Comparison 2026

Comparative pricing, context windows, latency, compliance posture and per-use-case cost estimates for the six LLM vendors most enterprises evaluate. Curated 2026-06; updated quarterly. Use as planning estimates — not as enterprise-contract commitments.

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Vendors covered

OpenAI GPT-4 frontier, Anthropic Claude 4, Google Gemini 2, Meta Llama 4, Mistral Large, Alibaba Qwen 3.

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Variables

Input/output/cached token pricing (USD/M), context window, p95 latency, vendor lock-in, compliance, data residency.

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Use-case costs

Per-million-query estimates for RAG Q&A, customer support, and document extraction across all six vendors.

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Decision guidance

Pre-computed recommendations by constraint: strict data residency, top quality, high volume, long context, low latency.

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Dataset 2

AI Governance Minimum Baseline — 12 Controls

The 12 minimum governance controls Slavin AI considers mandatory before deploying any AI system to production. Cross-mapped to NIST AI RMF 1.0, ISO/IEC 42001:2023, and EU AI Act articles — the intersection where all three frameworks converge. Plus a 5-level maturity model. Citable starting point when you do not want to write your own baseline from scratch.

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12 controls

Model Registry, Data Lineage, Eval Harness, HITL, Audit Log, Incident Response, Prompt Versioning, RBAC, PII Filtering, Quality Monitoring, Drift Detection, Rollback.

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Triple framework mapping

Every control mapped to the relevant NIST AI RMF subcategories, ISO/IEC 42001 clauses, and EU AI Act articles.

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Owner + evidence

For each control: the role that owns it and the type of evidence an auditor would accept.

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5-level maturity

Ad-hoc → Documented → Measured → Audited → Continuous. Use to score current state and define roadmap.

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Dataset 3

Enterprise AI Use Case Catalog — ROI & Risk 2026

24 enterprise AI use cases compiled from 150+ Slavin/SLAtech engagements. Each entry: industry, complexity (1–5), ROI timeline band in months, primary KPI, recommended starting architecture (RAG / fine-tuning / prompt-only), EU AI Act risk classification, and the typical pitfall. The dataset enterprise teams keep asking for: a citable starting point for AI roadmap conversations.

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24 use cases

From customer support (UC-01) and code-assist (UC-03) through compliance (UC-08) and clinical documentation (UC-07) to credit decisioning (UC-10) and underwriting (UC-18).

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ROI timeline bands

Realistic months from production launch to verified business impact. Quick wins (under 12 months): 8 use cases. Long horizon (18+ months): 3 use cases.

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EU AI Act classification

Each use case flagged high_risk or low_risk per Annex III. 10 of 24 cross into high-risk territory — often surprising for first-time AI deployments.

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Starting architecture

Recommended starting point: RAG, fine-tuning, prompt-only, or hybrid. Reduces the "what do we even build" anxiety at the start of a project.

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Licence and citation

How to Cite

All datasets in this catalog are released under Creative Commons Attribution 4.0 (CC-BY-4.0). You may copy, redistribute, transform and build upon the material for any purpose — including commercial — provided you give appropriate credit.

Suggested citation: 

Slavin AI (SLAtech LTD). LLM Vendor Pricing & Capability Comparison 2026.
https://www.slavin.ai/data/llm-vendor-pricing-2026.json (accessed YYYY-MM-DD).
Licence: CC-BY-4.0.
Methodology

How the Data Was Compiled

Pricing reflects publicly listed vendor prices as of 2026-06-14, not negotiated enterprise contracts. Context windows are documented maximums, not always production-recommended (long context degrades attention quality even when supported). Latency p95 figures are observational from public status pages and our own monitoring; expect variance by region, time of day, and model variant. Compliance entries reflect documented certifications, not full audit verification — for regulated procurement, always require a current attestation.

We welcome corrections and additions. Email [email protected] or contribute via the SLAtech GitHub organisation (when the public repo is enabled).