Cortecs.ai: Europe's LLM router as a GDPR-native alternative to OpenRouter

As an AI engineer in Europe, sooner or later, you hit the same wall. You build a working prototype, the performance of the selected language model exceeds expectations, and then the project is put on hold by the compliance department. Since the final roll-out of the AI Act and the tightened scrutiny around the GDPR, clients and internal security teams increasingly require watertight guarantees regarding data residency. Data Protection Impact Assessments (DPIAs) ruthlessly expose where trade secrets or personal data leave European territory or threaten to end up as training data.

Until recently, Dutch engineering teams sought refuge in complex, custom-built infrastructure across public cloud providers or accepted massive lock-in with a single vendor. Shared API gateways and routers, such as the popular OpenRouter, offered a solution for the fragmented market of Foundation Models, but were often dismissed for enterprise applications due to US jurisdiction and a lack of compliance structures for the underlying providers. Cortecs.ai has recently entered the market to fill this specific vacuum. They position themselves as Europe's LLM Router with a strong emphasis on control, GDPR conformity, and European hosting.

Exactly what Cortecs.ai is

The Austrian company Cortecs.ai functions as a gateway to over 150 model endpoints, without you, as an engineer, having to manage separate contracts, keys, and architectural integrations for each model provider. Where it gets technically interesting is their routing architecture. All inference traffic runs exclusively via providers within an EU sovereign cloud. Specifically, this means that the computing power is delivered by servers in Spain, Germany, France, Finland, and Poland.

Behind the scenes, Cortecs uses a method they call filter-and-rank routing. With every request, the router first checks your preset requirements. Models and providers that do not meet these compliance or technical demands are filtered out. The remaining route options are then dynamically ranked based on price and the performance available at that exact moment. According to their own figures, this mechanism results in a 99.99% uptime guarantee through seamless switching between back-up providers, and an average cost reduction of 32% per API call.

The platform is fundamentally built so as not to get in the way of your existing stack. It provides an API that functions as a direct drop-in replacement for OpenAI. Furthermore, the vast majority of multimodal functionalities are retained: alongside standard text generation (chat), this connection also supports image input (vision), embeddings, and audio transcription.

Cortecs vs OpenRouter: the GDPR difference

For individual developers or projects without strict privacy requirements, an established party like OpenRouter is often the standard way to go, as it unlocks an enormous catalogue of models including Bring Your Own Key (BYOK) functionality. As soon as you operate in a professional Dutch engineering context, however, you hit a legal wall. OpenRouter is headquartered in the United States and offers no native EU-sovereign route. Even more problematic for compliance officers: with public routers, you are often personally responsible for compliance regarding all individual downstream providers (the parties that actually supply the computing power).

This is where Cortecs fundamentally takes a different path. Legally, the service acts as the primary Data Processor. This means that Cortecs handles the contractual responsibility and compliance agreements towards all underlying cloud providers. You sign a single Data Processing Agreement (DPA), which applies to the entire chain. This significantly reduces administrative overhead during vendor onboarding.

"As an engineer, you don't have to worry about which server in which European country the model is running on. The guarantee is that the payload does not leave Europe and is not stored anywhere."

To back up these claims under the AI Act and GDPR, Cortecs implements strict data rules. Payloads are processed exclusively in temporary memory and are intuitively deleted immediately after the request is completed. The platform does not train on your data, and—crucially—underlying providers are contractually excluded from doing so as well. Should your organisation's policies be even stricter, Cortecs offers the option to manually exclude specific providers from the routing.

How it works in code

The architecture is aimed at minimising migration time. Because the endpoint is fully OpenAI-compatible, you hardly need to modify existing codebases that use the official SDKs. By exclusively overwriting the Base URL and the API key, you instantly route all traffic via the European network.

A standard implementation using the Python SDK looks as follows:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cortecs.ai/v1/",
    api_key="jouw_cortecs_api_key"
)

# Je kunt nu de reguliere OpenAI methodes gebruiken
response = client.chat.completions.create(
    model="meta-llama/llama-3.1-70b-instruct",
    messages=[
        {"role": "user", "content": "Analyseer deze dataset..."}
    ]
)

Beyond the direct API connection, this standard drop-in integrates effortlessly into the broader engineering ecosystem. Existing integrations with orchestration and observability tools like LangChain, Langfuse, and LibreChat are supported out-of-the-box. The platform also forms a transparent layer for coding assistants and IDE plugins such as Cline, Kilo Code, Open WebUI, and OpenCode.

Pricing and lock-in

Inherent to gateways is the fear of opaque hidden costs and vendor lock-in. Most commercial API gateways charge a fee per transmitted token (markup) or require monthly enterprise subscriptions that eat heavily into the budget upfront.

Cortecs explicitly positions itself with a pure pass-through cost structure. There is no markup on the actual tokens; you pay the raw cost price for the inference via the selected language model. The revenue model rests entirely on a transparent flat fee of 5% applied when you add credits to your account (top-up).

• No subscription fees (no subscriptions)
• No markup on token prices (pure pass-through)
• Enterprise control without artificial rate limits

For applications designed to be infrastructure-independent, this reduces financial friction and offers a scalable structure that moves with the size of the workload, without the unpredictability of dynamic token markups.

When to use it, and when not to

At The Automation Group (TAG), we always evaluate tools based on their pragmatic value. Cortecs.ai fills a substantial gap in the European AI infrastructure, but it is crucial to identify for which use cases it is the right tool.

This architecture is ideal for established parties in regulated sectors. If you are building for healthcare, finance, the legal domain, or (semi-)government, the "GDPR-native as Data Processor" approach ticks many compliance boxes with a single stroke of the pen. The fact that parties like Raiffeisen-Landesbank Steiermark, Verbund, the Austrian Ministry of the Interior, Austrian Wirtschaftsservice (AWS), FFG, WKO, and even the ESA (European Space Agency) run on the platform as clients provides the necessary social proof to chief information security officers.

When is Cortecs less relevant? Are you a team that already has an intensive partnership and tightly aligned infrastructure directly with Azure OpenAI or Google Vertex AI in European hosting regions with comprehensive DPAs? Then the added value of this intermediary layer is limited to a small price advantage. Furthermore, we always advise a rigid testing phase if you are working with complex agentic workflows, such as Model Context Protocol (MCP) integrations. With such advanced architecture, you must specifically test beforehand whether functionalities like seamless streaming and complex tool calling structures are continuously preserved through the routing for your desired models.

What this means for Dutch engineering teams

For the Dutch market, the arrival of specialised European LLM routers signifies an important shift in how we design production-ready AI applications. Platforms like Cortecs lower the barrier to deploying a diversity of Foundation Models, open weights models, and in the future even emerging specific reasoning engines, quickly, efficiently, and above all compliantly for enterprise use.

Nevertheless, it is important to realise that infrastructure is not the sole determining factor. From a TAG perspective, we believe that robust failovers, well-configured evaluations (evals), and in-depth observability on the output remain the true differentiators in production-oriented AI. A router merely delegates the pipeline and the compliance surrounding it.

Moreover, a router forms the boundary layer of the "public" cloud solution. Should the nature of your project or customer data be so critical that even processing in temporary memory on a European server is not permitted, you vanish into the realm of true on-premise deployments — where running solutions like OpenClaw locally within your own physical or private cloud remains the only genuinely compliant option. For the broad mid-segment of enterprise applications, where agility and cost management carry just as much weight as GDPR compliance, Cortecs.ai currently offers one of the most pragmatic solutions on the European market.