Sovereign AI stack advisor
If you can’t send data to a third-party API — for regulatory, privacy, or data-residency reasons — you can usually replace a proprietary AI tool with a self-hostable or open-weight alternative. This advisor maps the tool you use today to viable sovereign options and tells you what running them actually costs in effort and hardware.
How it works
Choose the tool category you want to replace (chat LLM, image generation, speech-to-text, or embeddings) and your hosting constraint (air-gapped on-prem, private cloud, or EU/region-pinned managed). The tool returns matching open or self-hostable projects, each with a one-line description, the deployment effort, and the main trade-off to expect — so you can weigh control against convenience before committing.
Notes and tips
- Start with the smallest model that passes your evals: a quantized 7-8B LLM covers a surprising range of production tasks at a fraction of the hardware cost.
- “Sovereign” is a spectrum. Fully air-gapped on-prem gives maximum control; region-pinned managed hosting (EU data residency) is far easier and may satisfy your actual requirement.
- Budget for the unglamorous parts: model updates, monitoring, scaling, and security patching are now your responsibility, not the vendor’s.
What drives people toward a sovereign stack
Organisations reach for self-hosted or privacy-preserving AI for several distinct reasons, and the reason matters because it changes which alternative is actually right.
Regulatory data residency — some jurisdictions or sectors require that data not leave a specific geography. EU-based financial services firms, healthcare providers subject to national health data laws, and some government contractors face genuine legal constraints. For these organisations, the question is not whether to self-host but which alternatives satisfy the residency requirement — and a managed service with a contractual EU-only data processing guarantee may be sufficient without the operational burden of self-hosting.
IP and confidentiality — organisations handling proprietary source code, unreleased product designs, M&A discussions, or sensitive client data need confidence that their inputs are not retained, logged, or used for model training. Enterprise tiers of major providers typically offer no-training commitments and data processing agreements, which may satisfy this concern without self-hosting. For the highest-sensitivity work — defence, intelligence, critical national infrastructure — only air-gapped on-premises deployment provides the required assurance.
Predictable cost at scale — API pricing is convenient at low volume but linear with use. At very high call volume, the per-token cost of a managed API can exceed the amortised cost of running a self-hosted model on dedicated hardware. This is a volume threshold that varies by use case, but it is real, and for some workloads self-hosting is the economically rational choice.
Choosing the right open model
The open-weight LLM landscape has matured significantly. For most text tasks — summarisation, extraction, classification, RAG, code completion — quantized models at 7–13 billion parameters run on a single GPU or a high-end laptop with Apple Silicon and are competitive with mid-tier proprietary models from two years ago. For harder reasoning and long-context tasks, 70B-class models narrow the gap further but require substantially more hardware.
For deployment, tools like Ollama and llama.cpp make running quantized models locally straightforward. For production server deployment, vLLM and llama-server offer high-throughput inference. The additional operational surface includes monitoring, model updates (new releases may have different behaviour), and security patching — none of which a managed API provider requires you to handle.
Speech-to-text and embeddings on a sovereign stack
For transcription, Whisper (from OpenAI, released under MIT licence) can be run entirely locally and performs at or near the level of managed transcription APIs for most languages. This is often the easiest win on the path to a sovereign stack — transcription workloads tend to involve audio that is highly sensitive (meeting recordings, interviews, clinical notes), and running Whisper locally with no data leaving your network is straightforward on modern hardware.
For embeddings (the vector representations used in semantic search and RAG systems), a wide range of open models run efficiently on CPU-only hardware. The performance characteristics differ from managed embedding APIs, so run your retrieval evals before committing, but the cost and privacy advantages at scale are significant.