Comparison · Last reviewed June 2026
routeur.ai vs LiteLLM
LiteLLM is an open-source proxy: 100+ providers behind an OpenAI-style API, deployed and operated by you. It ships a toolkit. What a company needs is a governed platform — running today, with someone else carrying the pager. Below: the full capability matrix, the three questions that decide it, and what switching takes.
The short version
routeur.ai or LiteLLM: which fits your team?
Both sit between you and the model providers — but they answer different questions. If yours sounds like the left column, you're done looking.
routeur.ai is the answer when…
- You want DLP, shields, moderation, caps and audit working this afternoon — no deployment project, no new tier-1 service to own.
- Governance should be a tested product with one vendor accountable for it, not a guardrail stack your team assembles and owns the gaps in.
- Data residency and an employee chat workspace should be features you switch on, not further projects on the roadmap.
LiteLLM fits when…
- A hard requirement says the gateway must run inside your own VPC — and you have platform engineers to own it in production.
- You want to read and modify the gateway's source, and you're prepared to build and maintain the guardrail stack around it.
Side by side
routeur.ai vs LiteLLM: the full matrix
Capability by capability, summarised from LiteLLM's own documentation — so you can check every cell yourself.
LiteLLM's column is summarised from
their public documentation,
last reviewed June 2026.
Spotted something out of date?
Tell us and we'll fix it.
The difference
What LiteLLM doesn't give you
LiteLLM hands you the parts. routeur.ai hands you the finished doorway — and the gap between those is every engineer-month your platform team would spend building, securing and babysitting it.
Governance as product, not plugins
DLP, prompt shields and output moderation are first-party features tested as one system — not a stack of guardrail integrations your team selects, wires together, and owns the false negatives for.
Zero infrastructure to run
No containers to patch, no proxy to scale, no 2am pages when the thing in front of every AI feature falls over. The gateway is our production system — <50ms overhead, failover built in.
The whole workforce, not just apps
A proxy governs API traffic. routeur.ai also ships a governed chat workspace, so the AI use happening outside your codebase goes through the same doorway, policies and audit trail.
Decision framework
Three questions that decide it
Put these to any gateway you're evaluating — LiteLLM included. They're the ones your security, finance and compliance teams will ask anyway.
What does "free" cost by month three?
Deploying, scaling, patching and securing a gateway is an ongoing engineering project — and SSO, audit logs and support sit in LiteLLM's paid enterprise tier anyway.
Seats that include all of it, managed — while routing returns ~20% of model spend. Most teams find the maths is not close.
Who gets paged when the gateway falls over?
A proxy in front of every AI feature is tier-1 infrastructure from day one. Self-hosting means that pager is yours.
The hot path is our production system, with failover, monitoring and an SLA on Enterprise. Your engineers ship features instead of operating middleware.
Does governance arrive assembled?
Guardrails-by-plugin means your team chooses, configures and maintains each control — and owns whatever slips between them.
DLP, prompt shields and output moderation ship as one tested system, switched on per route from the dashboard — policy, not plumbing.
Switching
Switching from LiteLLM takes an afternoon
LiteLLM and routeur.ai both speak the OpenAI API, so your applications don't change — you re-point the base URL and retire the infrastructure instead of upgrading it.
Bring your provider keys
Add your OpenAI, Gemini, DeepSeek and Anthropic keys in the dashboard. They're encrypted at rest and never appear in your application code again.
Change two lines
Point your existing OpenAI client at api.routeur.ai/v1. No new SDK, no rewrite — your business logic stays byte-for-byte identical.
from openai import OpenAI
client = OpenAI(
api_key="sk-your-openai-key"
)from openai import OpenAI
client = OpenAI(
api_key="rtr-your-routeur-key",
base_url="https://api.routeur.ai/v1"
)Route a slice, read the receipts
Send 5% of traffic through, switch on DLP and prompt shields per route, and watch the dashboard report savings versus going direct — in real time, per request.