Compliance and fine-tune migration
Vendor assessments, access control, training configs and what a fine-tune's metrics mean on a new base model.
A provider migration is usually planned as an engineering change: swap the client, map the request bodies, run the regression suite. The parts that actually delay it are elsewhere. Somebody has to redo a vendor risk assessment that was signed off two years ago. Somebody has to work out who can call the fine-tuned model on the new side, and whether the answer is a key, a project or an IAM role. Finance has to explain why the switch month cost more than either the old or the new provider, in two currencies, across two billing cycles that do not line up.
These pages are about that layer. They stay away from what any particular vendor’s contract or audit report says, because those are negotiated per customer and change without notice, and they stay away from quoting prices that go stale. What is left is the part that transfers: what a clause type does, what a scoping model implies, and the arithmetic that makes a before-and-after comparison mean something.
What a Provider Migration Means for an Existing SOC 2 Vendor Assessment
Why a new inference provider triggers a fresh vendor risk assessment even when the outgoing one was approved, and what to request to make the new review short.
9 min read
Migrating Fine-Tuned Model Access Control Between Providers
How access to a tuned model artefact is scoped on different platforms, and which parts of your access story do not survive the move.
9 min read
Recreating a Fine-Tune's Training Config on a New Provider
Which tuning hyperparameters are exposed, what they are called, and why the ones that look portable are the ones that are not.
9 min read
What a Fine-Tune's Evaluation Metrics Mean on a New Base Model
Why training loss and eval scores from a tune on one base cannot be compared to a retune on another, and the four-cell measurement that recovers a usable answer.
11 min read
Mapping Frequency and Presence Penalty Parameters Between APIs
What the two penalties do to the logits, which APIs express them, and what actually substitutes when the target has neither.
9 min read
What to Do When a Migration Target Has No Equivalent for a Sampling Parameter
The rejection errors an unsupported sampling parameter produces, why silent acceptance is worse, and the closest working substitute for each control.
9 min read
Migrating a Cost-Per-Conversation Metric Between Providers
Rebuilding cost per conversation from each provider's own usage fields, including the cache accounting difference that makes a naive sum wrong.
10 min read
Reconciling Historical Cost Data Across a Provider Migration
A common schema and a step-by-step normalisation that makes two providers' usage records and invoices comparable on one axis.
10 min read
What Currency and Billing-Cycle Differences Mean for a Provider Migration
Why the switch month reconciles to nothing sensible, and what to change so the comparison and the accruals still work.
9 min read
Other topics
- LLM fundamentals & architecture
- Tokens, tokenization & context windows
- Prompt engineering
- Reasoning models & test-time compute
- Multimodal AI: vision, audio, video
- RAG & retrieval
- Embeddings & vector search
- AI agents & tool use
- Structured output & function calling
- Fine-tuning & post-training
- Local inference errors, string by string
- Running local models day to day
- Testing code that calls an LLM
- Snapshot and property testing for model output
- Regression suites for prompts
- Eval gates in CI
- Flaky tests against a model
- Determinism and the cost of testing
- Contract and streaming tests
- Testing tool calls and retrieval
- Inference, serving & latency
- Rolling out a prompt change
- Testing AI systems in practice
- Forecasting a time series
- Machine learning on tabular data
- Geospatial data and models
- Understanding audio that is not speech
- Understanding video
- Core computer vision tasks
- Machine learning on graphs
- Point clouds and 3D
- Evaluation, benchmarks & LLM-as-judge
- Sensor and IoT data
- Logs and event streams
- Models over biological sequences
- Machine learning on molecules
- Embedding and searching code
- Extracting invoices and purchase orders
- Receipts, statements and tax forms
- Insurance policies and contracts
- Deeds, court filings and patents
- Extracting from medical records
- Observability & LLMOps
- CVs, certificates and identity documents
- Shipping, customs and technical documents
- Meetings, email, chat and filled-in forms
- Building an extraction pipeline
- Business, property and inspection documents
- Contract clauses and insurance claims
- Regulated and compliance documents
- Consumer, travel and closing documents
- Mapping one chat API onto another
- SDK and framework migrations
- Hallucination & failure modes
- Re-embedding and model deprecation
- Cutting over between providers
- Parity gaps, shims and legacy endpoints
- Moving between model versions
- Migrating vector stores and caches
- Mapping capabilities and parameters
- Migrating pipelines and agents
- Contracts, runbooks and rollback
- Auditing a codebase before a cutover
- LLM cost engineering
- Routing, cost tracking and multi-tenancy
- What a migration does to your prompts
- AI security & prompt injection
- Privacy, compliance & data residency
- AI governance, policy & society
- Building reliable AI applications
- AI hardware, GPUs & compute
- Open-weight models & local inference
- AI for developers & coding agents
- AI in industry: vertical playbooks
- AGI, superintelligence, alignment & the long future
- Machine learning foundations
- NLP fundamentals & classical tasks
- Data engineering for AI
- Synthetic data & dataset curation
- AI product design & UX
- Search, ranking & recommendation
- Enterprise adoption & change management
- AI careers, skills & teams
- Reading AI research
- AI in science & discovery
- Robotics & embodied AI
- AI economics, markets & business models
- AI myths, hype & media literacy
- Context engineering
- Shipping AI features: patterns & anti-patterns
- Build it: end-to-end AI tutorials
- Python for AI: hands-on recipes
- TypeScript, React and the web
- Frameworks and SDKs
- Errors and troubleshooting
- AI facts, numbers and statistics
- The history of AI
- The maths behind AI
- Architectures beyond the transformer
- Reinforcement learning
- Diffusion and generative media
- Speech, audio and voice engineering
- Benchmarks, one at a time
- AI search visibility
- Infrastructure and operations
- Databases and storage for AI
- Knowledge graphs and structured knowledge
- Classical ML in production
- Regulation, jurisdiction by jurisdiction
- Prompt recipes and pattern library
- AI for people who do not write code
- Writing, media and creative work
- Edge and on-device AI
- Interpretability and model internals
- Field notes
- OpenAI model behaviour
- Claude model behaviour
- Gemini model behaviour
- Llama model behaviour
- Mistral model behaviour
- Qwen model behaviour
- DeepSeek model behaviour
- Cohere model behaviour
- Grok model behaviour
- Small model behaviour
- Hybrid model architectures
- Token cost by language and script
- Transliteration, romanization and script handling
- Locale-correct output
- Multilingual generation quality
- Multilingual pipelines
- The EU AI Act, article by article
- AI under the GDPR and EU data law
- US AI regulation, state and sector
- International AI governance and standards
- AI litigation and enforcement
- Running AI workloads on AWS
- Running AI workloads on Google Cloud
- Running AI workloads on Azure
- AI at the edge: Workers, Vercel and Netlify
- Serving models on Kubernetes
- Operating AI infrastructure
- Quantization formats and what they cost
- llama.cpp, flag by flag
- Ollama and the desktop local-model runtimes
- Local models on Apple Silicon
- Hardware for local inference
- Running speech and embedding models locally
- Model files, adapters and conversion
- VRAM arithmetic for local models