Run frontier LLMs on sovereign EU infrastructure

Your code never leaves Europe 20% faster inference, with no accuracy tradeoff Compliant, verified, and secure, down to the GPU Cut AI coding spend up to 10x No operational control Inference location, security, and underlying changes sit with the provider - not with you. One-vendor lock-in Prompts and workflows harden around a single API. Unwinding costs more than building did. Costs you can't forecast Rates and limits move on their schedule, so capability gets rationed to budget. Foreign jurisdiction Contract and access terms can shift with a political decision, not a commercial one. Path 01 Agentic coding in the terminal Point OpenCode, ForgeCode, Crush, or Pi at Corti Models with the Corti CLI. Keep the workflow you already have. # run the setup wizard npx @corti/cli init models # load credentials, then launch your agent set -a; source ~/.env; set +a opencode Path 02 Direct API in your product OpenAI-compatible. Existing app code works with a base URL change and a new key. client = OpenAI( base_url="https://ai.eu.corti.app/v1", api_key="") r = client.chat.completions.create( model="corti-s1", messages=msgs ) Model Best for Reasoning Cost per 1M tokens corti-s1 Recommended Complex agentic coding and repo-wide refactors Yes $2.00 in · $8.00 out$0.20 cached input corti-s1-instant Fast interactive coding and inline completion No $2.00 in · $8.00 out$0.20 cached input corti-s1-mini High-volume review, tests, and refactors at lower cost Yes $1.00 in · $4.00 out$0.10 cached input corti-s1-mini-instant Cost-sensitive completion at scale No $1.00 in · $4.00 out$0.10 cached input corti-s1-embedding Codebase search, retrieval, and indexing n/a $0.03 inno output charge A working prototype Someone on your team builds the feature against OpenAI or Anthropic. It demos well. The business case is obvious. Security, legal, procurement Data residency cannot be answered. The transfer cannot be justified. The cost at scale cannot be approved. The feature sits in review and the quarter ends. The same code, cleared Change the endpoint and the credentials. Keep your application logic, your prompts, and your evaluation set. Run it on infrastructure that passes review the first time. Verifiable Verifiable at every layer Most vendors claim sovereignty in the contract, then rent the stack. Corti Models runs on Kommodity, an open source infrastructure layer, so the entire stack, including encryption, isolation, and security, is public and auditable, not just claimed. Attestation before inference - isolation enforced by hardware, not policy No US cloud provider in the request path Infrastructure code open source on GitHub for anyone to review Explore Kommodity on Github Request traceEU-CPH 10:24:07.004request receivedeu-copenhagen 10:24:07.006attestation verifiedquote ok 10:24:07.009routed to nodegefion / n-04 10:24:07.011model loadedcorti-s1 10:24:07.788completion returnedeu-copenhagen 10:24:07.789prompt retainednone 10:24:07.789used for trainingnever Private Private by architecture Prompts and completions process in memory and end with the request. Nothing is retained, nothing trains a model, nothing is visible to another tenant. Central control over access, data handling, and per-team budget Sovereign cloud or on-premises - same platform as clinical AI in production ISO 27001, ISO 42001, GDPR, NIS2, DORA, and EU AI Act posture by default GovernanceDefault Prompt retention none Training on your data never Data residency your region Deployment cloud / on-prem Usage reporting per team Capacity management Monitoring and observability Failover and resilience Tool calling Structured outputs Prompt caching Access controls Auditability Model evaluation Managed model upgrades Predictable pricing Enterprise support Today’s leader isn't tomorrow’s Corti evaluates and operates the best available models as the market moves - without you rebuilding integrations every leaderboard cycle. Economics Pay for tokens, not seats Per-seat coding assistants charge whether developers use them or not. Governed consumption charges for what runs, at European infrastructure cost. One credit balance across Corti APIs Same credits for development, testing, and production Per-team limits and request-level reporting EstimateDirectional Developers with AI tooling 500 Cost per seat, per month, today $39 Assumed efficiency vs per seat 5x Per-seat licences$234,000 / yr Corti Models$46,800 / yr Difference of $187,200 a year, before the procurement and compliance overhead you stop paying for. Directional model, not a quote. “Our customers want digital independence, access to the world's best AI without compromising on security, compliance, or control over their own data.” Jesper Carøe , CEO, Trifork Digital Health Early adopter and implementation partner Quickstart First request in under five minutes Create a key, change the base URL, make the call. OpenAI-compatible - any existing SDK works without a rewrite. Generate an API key in the console, start with $50 of free credits Point any OpenAI SDK or curl at ai.eu.corti.app Or install the Corti CLI and connect your terminal agent Read the quickstart Get API Key First requestcurl curl https://ai.eu.corti.app/v1/chat/completions \ -H "Authorization: Bearer $CORTI_API_KEY" \ -H "Content-Type: application/json" \ -d '{ "model": "corti-s1", "messages": [ {"role": "user", "content": "Refactor this function"} ] }' Processed in eu-copenhagen Frequently asked questions Meet with an Expert How is this different from a European company reselling US models? A reseller changes who invoices you. The inference still runs on infrastructure under US jurisdiction and the transfer still happens. Corti operates the hardware, so your request is processed on machines we run, in a European facility, under European law. What happens if we want to leave? The API is OpenAI-compatible, so moving off Corti Models is the same base URL change as moving on. We do not hold your prompts, completions, or tuning data. The compatibility that makes adoption cheap is what makes exit cheap. Can we run this on our own hardware? Yes. Corti Models is available in sovereign cloud and on-premises deployments, on the same platform used for on-premises clinical AI today. On-premises changes the commercial and deployment timeline, so raise it early. How do you handle model updates and deprecation? Model versions are pinned. You choose when to move, with advance notice and an overlap window on the previous version. Nothing changes underneath a running workload because a vendor shipped a new default. What is the migration effort? For direct API use, a base URL and a key. For terminal coding agents, installing the Corti CLI and pointing an existing agent at it. Teams typically have a working integration in an afternoon and a governance model agreed in a few weeks. Some of the strongest open-weight models were developed in China. How is that sovereign? Sovereignty is about who operates the infrastructure and whose law applies, not where the research was done. Open weights are a static artefact. Corti deploys them on hardware we operate in Europe, and no data reaches the original developer. You get the capability without the dependency, which is not possible with a closed model you can only reach through its owner's API. We are not in healthcare. Is this for us? Corti built this for healthcare because healthcare is the hardest compliance environment there is. The requirements that follow, data residency, auditability, and strict privacy controls, are the same ones facing financial services, energy, public administration, defence, and aviation. Run your next coding task on European hardware Install the CLI, keep the agent your team already uses, and point it at models hosted in the EU. Book a technical review Explore docs