The Architecture of a Sovereign LLM Gateway

A robust LLM API gateway is more than a simple reverse proxy. It serves as the orchestration layer that manages the lifecycle of every request, from initial authentication to the final token delivery. In a self-hosted environment, the architecture typically consists of three primary layers: the Proxy Interface, the Logic Engine, and the Provider Registry.

The Proxy Interface is the entry point for your applications. By adopting the OpenAI-compatible API standard, you ensure that your existing SDKs and libraries continue to function without modification. This layer handles request validation and initial rate limiting to prevent upstream provider saturation. API compatibility has become a standard procurement criterion for scale-ups that want to avoid vendor lock-in during the rapid model release cycles we are currently witnessing.

  • Request Routing

    Directs traffic based on model availability, latency requirements, or cost constraints.

  • Load Balancing

    Distributes requests across multiple GPU clusters to prevent bottlenecks.

  • Fallback Logic

    Automatically switches to a secondary model if the primary endpoint returns a 5xx error or hits a rate limit.

The Logic Engine is where the most critical operations occur. This is where you implement PII masking and semantic caching. Semantic caching, in particular, can take a meaningful share of traffic off the GPU by serving previously generated responses for similar queries, provided the similarity threshold is correctly tuned. For teams running on Lyceum, this logic engine can be deployed as a lightweight container alongside your inference endpoints, ensuring minimal internal latency.

Finally, the Provider Registry maintains the connection details for your various backends. This includes your dedicated inference nodes on Lyceum, local vLLM instances, and any legacy managed APIs. By centralizing these credentials in a self-hosted gateway, you eliminate the need to distribute sensitive API keys across your entire application stack.

Tooling Landscape: LiteLLM, Kong, and Apache APISIX

Choosing the right tool for your gateway depends on your team's existing infrastructure and the specific features required for your production environment. While many teams start with custom Python wrappers, specialized gateway tools offer production-grade reliability and observability out of the box.

LiteLLM has emerged as the preferred choice for ML engineers who require a lightweight, Python-native proxy. It supports over 100 model providers and provides a unified OpenAI-compatible format. Its primary advantage is the ease of integration with existing Python workflows and its built-in support for budget tracking at the user or team level. For a startup with 15-50 employees, LiteLLM offers the fastest path to a centralized control plane.

Kong AI Gateway, on the other hand, is designed for infrastructure leads who need to integrate LLM management into a broader enterprise API strategy. Kong's AI plugins allow for advanced features like prompt engineering templates and automated PII scrubbing before the request ever leaves your VPC. Kong's AI Proxy plugin also documents streaming support, which is critical for interactive LLM applications.

Apache APISIX provides a high-performance alternative for teams dealing with massive request volumes. Its plugin-based architecture allows for deep customization of the request-response lifecycle. For example, you can write custom Lua scripts to implement complex routing logic based on the token count of the input prompt, ensuring that larger requests are always routed to high-memory GPUs like the NVIDIA H100.

The choice comes down to who will operate the gateway: LiteLLM for ML engineers, Kong for platform teams with an existing Kong estate, and Apache APISIX for teams tuning for raw request throughput.

Security and Compliance in the EU AI Act Era

For European AI teams, the gateway is the primary enforcement point for compliance. With the EU AI Act now in effect, the ability to audit and log every request matters, and European buyers increasingly ask for the data path to stay in the EU even though no EU instrument requires residency. A self-hosted gateway allows you to keep the entire data path within the European Union, provided your underlying GPU infrastructure is also EU-sovereign.

One of the most common mistakes is using a US-based managed gateway that claims to be GDPR-compliant but still routes metadata or request logs through non-EU servers. This creates a legal gray area that many enterprise customers in manufacturing or healthcare will not accept. By hosting your gateway on Lyceum, both the orchestration layer and the inference endpoints sit in European data centers in Spain, Paris and the Nordics. Lyceum runs no German data center.

  1. PII Redaction

    Use the gateway to identify and mask names, addresses, or financial data before the prompt is sent to the model.

  2. Audit Logging

    Maintain a complete, encrypted log of all interactions for compliance reviews without exposing the data to third-party monitoring tools.

  3. Access Control

    Implement fine-grained RBAC (Role-Based Access Control) to ensure that only authorized services can access specific high-cost or high-capability models.

Infrastructure Requirements and Cost Optimization

The performance of your gateway is heavily dependent on the underlying hardware. While the gateway itself is often a CPU-bound process, the inference endpoints it manages require high-performance GPUs with significant VRAM. For models like Llama 3 or Mistral Large, VRAM capacity is the primary bottleneck for concurrency and throughput.

When provisioning infrastructure for your gateway and models, consider the following hardware profiles:

  • NVIDIA H100 (80GB)

    The standard for high-throughput production inference. Its FP8 support and high memory bandwidth make it ideal for serving large models to hundreds of concurrent users.

  • NVIDIA A100 (80GB)

    A reliable workhorse for sustained workloads where the absolute peak performance of the H100 is not required but high VRAM is still essential.

  • NVIDIA L40S

    An excellent cost-effective option for smaller models or fine-tuned variants that do not require the extreme interconnect speeds of the H-series.

Lyceum provides a structural cost advantage here. Our H100 list price is 2.79 USD per GPU-hour on an on-demand VM and 3.59 USD on a dedicated inference or serverless training endpoint, both published on the Lyceum pricing page. For comparison, an AWS p5.48xlarge on-demand Linux instance in US East (N. Virginia) listed at 55.04 USD per hour on 4 August 2026, and that instance carries eight H100 GPUs, so the per-GPU-hour rate is 6.88 USD. GPU compute on Lyceum bills per second with no base fee, so your gateway pays only for the backend capacity it consumes during a traffic surge.

Cost optimization at the gateway level also involves Scale to Zero logic. For internal tools or non-critical services, the gateway can spin down inference nodes during periods of inactivity. While this introduces a cold start while the model loads back into VRAM, the cost savings for a startup can be substantial. Per-second billing ensures that you are never paying for idle GPU time between request batches.

Common Implementation Pitfalls

Even with the right tools, several common mistakes can undermine the effectiveness of a self-hosted gateway. The most frequent issue is ignoring egress fees. Many cloud providers charge heavily for data leaving their network. By keeping your gateway and your inference nodes on the same European network, and using S3-compatible storage that carries no ingress or egress charges, you eliminate these hidden costs.

Another pitfall is the lack of VRAM prediction. Without an intelligent scheduler, a gateway might route a large request to a GPU that is already near its memory limit, resulting in an Out-of-Memory (OOM) error. Lyceum's scheduling layer addresses this by predicting runtime and memory footprint before a job starts and selecting the most appropriate GPU, which lowers cost per job through better utilization.

Finally, teams often fail to implement streaming timeouts. LLM requests can be long-running, and a gateway that does not correctly handle persistent connections will often drop requests prematurely, leading to a poor user experience. Ensure your gateway configuration accounts for the unique long-polling nature of generative AI traffic.

Sources

[1] LiteLLM Proxy Documentation; [2] Kong AI Gateway Product Overview; [3] Kubernetes: Gateway API Inference Extension