LLM Proxy vs OpenTelemetry: Do You Need Your Own AI Gateway?

LLM proxies or gateways, managed AI gateways, and OpenTelemetry address different needs in observing and controlling AI workloads: telemetry and application logs reveal what happened, while gateways can centrally enforce access policies, budgets, credential controls, routing, fallbacks, and request-level logging before traffic reaches model providers. A self-hosted gateway offers customization, direct infrastructure control, and multi-provider abstraction, but introduces operational burdens including availability, latency, secret management, patching, supply-chain security, and the risk of becoming a single point of failure, illustrated by the 2026 malicious LiteLLM package incident. Managed gateways shift infrastructure maintenance to a vendor while requiring trust in its reliability, security, and data practices, whereas an OpenTelemetry-first setup is often sufficient for developers or teams primarily seeking observability. The appropriate architecture depends on workload complexity, required controls, internal operational capacity, and data requirements, with hybrid deployments often combining gateway policies with application and agent telemetry. Eden AI is presented as one managed option offering an OpenAI-compatible interface, multi-provider routing, fallback, caching, budgets, cost tracking, and an EU-focused endpoint, but organizations must still assess its behavior and provider dependencies against their own requirements.

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