aigw vs LiteLLM

The short version

• LiteLLM is an open-source proxy (and Python library) that you host and operate. Its strengths are provider breadth, flexibility, price (free and open source), and a large community. You run it, you scale it, and you wire up logging and policy yourself.
• aigw is a managed service, hosted in the EU or run at the edge in your own infrastructure, focused on governance, cost attribution, and agent controls on top of routing. You bring your own keys; aigw adds per-key budgets, MCP and agent governance, response scanning, a tamper-evident audit log, and OpenTelemetry-native export, without you operating the stack.

LiteLLM is a strong choice if you want a free router you operate yourself with the widest provider coverage. aigw is built for the other case: governance, per-team cost attribution, and agent controls, as a managed EU service you do not have to run.

What they have in common

Both give you an OpenAI-compatible endpoint, many providers behind one contract, virtual keys with budgets and rate limits, request logging, and a bring-your-own-keys model with no token reselling. The routing basics overlap.

Where aigw is different

The routing is table stakes. What aigw adds is the governance and cost layer that LiteLLM leaves you to build yourself.

The big one is agents. aigw does not just see chat completions, it discovers and governs the MCP tool calls and agent-to-agent calls your agents make. You can put argument-level guardrails on a tool, require a human to approve a call, rate-limit one tool without touching the rest, and scan what comes back for leaked secrets. If you are handing keys to autonomous agents, that is the part that keeps you out of trouble.

Then there is cost. Every call is attributed to a person, team, project, and cost center, and rolled up gross versus net of what aigw saved you, so finance can finally see where the AI money goes and who is driving it, across every provider and tool at once instead of one invoice at a time.

Observability is OpenTelemetry-native. Traces, metrics, and logs flow to your own Datadog or Grafana using the GenAI conventions, metadata only, so your prompts and responses never leave through it.

It runs hosted in the EU, and if that is not close enough you can run the stateless edge gateway in your own region while the control plane stays in the EU. That matters when you cannot route employee identities and prompt metadata through a US service.

Underneath all of it, every call lands in a tamper-evident log: a cryptographically chained record of who called what, which policy fired, and what got blocked. And the savings are real rather than a headline number. Request deduplication, automatic prompt-cache breakpoints, and an exact-match cache cut spend without changing a single output, and you see the gross versus net difference for yourself.

Where LiteLLM is the better pick

• You want free and open source and are happy to operate it.
• You need the widest possible provider list from day one.
• You want a library embedded in your app rather than a hosted service.
• You would rather build your own logging, policy, and observability than adopt an opinionated stack.

To be straight about it: LiteLLM is mature and widely used and does the routing job well. aigw is newer and focused on the governance, cost, and agent layer that sits on top.

Side by side

Try aigw

One endpoint, your keys, governance in a few minutes. Start on the quickstart, or read how the governance and agentic controls work.