Enterprise AI procurement: questions before buying a platform
A practical procurement checklist for comparing AI platforms, checking data control, security, model choice, cost and exit risk before you sign.
Diff
Diff with the breathless bits removed
This lane is for changes that affect costs, capability, access, safety, deployment, regulation, procurement, or business decisions. If a launch does not change what a reader should do, it probably belongs in a footnote rather than a parade.
20pages
21context briefs remaining
48Cache briefs for background
25run briefs for action
Published and in review
Diff pages in this prototype
What model cards tell you — and what they do not
How to read an LLM model card critically: what the claims mean, what is often missing, and how to spot the important gaps before procurement or deployment.
OpenAI, Anthropic, Google and Mistral APIs: what comparison pages should measure
A neutral comparison rubric: what to compare, what to ignore, and how to avoid misleading rankings when evaluating hosted LLM providers.
Small language models: when smaller is better
A practical guide to choosing small language models for latency-sensitive, cost-constrained or on-device AI tasks, with real trade-offs explained.
Reasoning models: what thinking modes change for cost and latency
When the extra cost and latency of reasoning models are worth it — and when they are not.
Local LLM runtimes: Ollama, llama.cpp, vLLM and TGI in plain English
When to use Ollama for prototyping, llama.cpp for single-user inference, vLLM for production serving, and TGI for Hugging Face integration.
Provider data retention policies: what API users should compare
A neutral checklist for comparing how AI API providers handle your data: training use, retention periods, abuse monitoring, region controls and deletion options.
Changelog watching for AI teams: deprecations, pricing and model aliases
A monthly review checklist for keeping AI applications stable amid provider changes.
Copyright and training data: what AI product teams can responsibly say
A plain-English guide to the three-layer copyright question in AI: training data, output similarity, and what teams can responsibly communicate.
AI vendor lock-in: model APIs, embeddings, vector stores and eval data
Understanding and mitigating AI vendor lock-in across model APIs, embeddings, vector databases, prompts, eval data and observability stacks.
AI SLAs and status pages: what reliability evidence vendors publish
How to evaluate AI API reliability claims: what SLAs actually cover, how to read status pages critically, and what reliability questions to ask before procurement.
Quantisation explained: why model files have Q4, Q5 and GGUF labels
A plain-English guide to LLM quantisation: what Q4, Q5, Q8 and GGUF labels mean, how quantisation affects quality and speed, and how to choose the right level for your hardware.
Meta Llama and open model licensing: what builders must check
A practical guide to open-model licensing: what the Llama, Apache 2.0, and other open-weight licences actually allow, what they restrict, and the traps that trip up commercial users.
Mixture-of-experts models: why active parameters matter
Why model labels list total and active parameters separately, what MoE architecture means for cost and speed, and when to care about the distinction.
Model gateways and routers: OpenRouter, LiteLLM and build-vs-buy questions
When to use OpenRouter for quick multi-provider access, LiteLLM for self-hosted routing, and when building your own model gateway makes sense.
Guardrails compared: policy prompts, classifiers, validators and permissions
Where policy prompts, input/output classifiers, output validators, and permission gates sit in the LLM request lifecycle, and when each matters.
AI energy use: useful facts without moral panic
Separating training, inference, datacentre, and hardware manufacturing energy use from the headlines, with practical context for AI buyers and builders.
Hardware supply and inference economics: why chips shape AI products
How GPU and accelerator supply affects AI product cost, availability, and feature decisions — from training cluster build-outs to per-query inference margins.
Responsible AI policies that builders can actually operationalise
Turning abstract responsible AI principles into release gates, evaluation checklists, incident review processes, and ownership structures small teams can maintain.
AI adoption in small businesses: where LLMs help first
A ranking of LLM use cases for small businesses: where the return is clearest, where the risks outweigh the benefits, and how to start without overcommitting.
What counts
A useful Diff page answers "so what?"
- What changed: model, API, pricing, policy, benchmark, availability, tooling, or risk.
- Who should care: builders, buyers, researchers, operators, regulators, or users.
- What remains unproved: benchmarks, reliability, pricing edge cases, uptime, safety, or real workload fit.
- What to do next: ignore, monitor, test, migrate, budget, update policy, or brief stakeholders.
Briefed pipeline
Diff briefs queued for drafting, edit and promotion
What model cards tell you — and what they do not
Read model cards critically.
OpenAI, Anthropic, Google and Mistral APIs: what comparison pages should measure
Compare major hosted providers without hype.
Meta Llama and open model licensing: what builders must check
Understand whether an open model can be used commercially.
Small language models: when smaller is better
Decide whether a compact model fits a task.
Reasoning models: what “thinking” modes change for cost and latency
Understand reasoning-model trade-offs.
Mixture-of-experts models: why active parameters matter
Interpret MoE claims in model launches.
Local LLM runtimes: Ollama, llama.cpp, vLLM and TGI in plain English
Choose a runtime for running open models.
Quantisation explained: why model files have Q4, Q5 and GGUF labels
Understand local model file choices.
Provider data retention policies: what API users should compare
Assess privacy and data-use differences between providers.
Model gateways and routers: OpenRouter, LiteLLM and build-vs-buy questions
Add provider flexibility without rewriting apps.
Changelog watching for AI teams: deprecations, pricing and model aliases
Keep AI apps stable amid provider changes.
Data leakage in LLM apps: logs, prompts, files and vendor retention
Identify places sensitive data can escape.
Guardrails compared: policy prompts, classifiers, validators and permissions
Choose safety controls for an AI app.
Enterprise AI procurement: questions before buying a platform
Evaluate AI vendors and platforms.
Copyright and training data: what AI product teams can responsibly say
Understand the controversy around training data and outputs.
AI energy use: useful facts without moral panic
Understand AI energy and infrastructure claims.
Hardware supply and inference economics: why chips shape AI products
Understand why GPUs and accelerators affect AI availability and cost.
AI vendor lock-in: model APIs, embeddings, vector stores and eval data
Reduce switching risk in AI systems.
AI SLAs and status pages: what reliability evidence vendors publish
Evaluate provider reliability claims.
Responsible AI policies that builders can actually operationalise
Turn abstract AI principles into processes.
AI adoption in small businesses: where LLMs help first
Identify practical AI use cases for small firms.
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