FastRouter vs. LiteLLM

"Free" vs "managed" is the wrong framing.

LiteLLM (MIT-licensed) is a battle-tested Python proxy with the largest provider catalog in the category — over 2,600 models across 140+ providers. If you need full code visibility, air-gapped deployment, custom-provider extensibility, or sovereign infrastructure, almost nothing else competes.

FastRouter is a managed gateway that ships routing, evaluations, prompt optimization, MCP credential vaulting, and workspace governance as a single product. There's nothing for your team to deploy, monitor, or patch — and nothing to wake up at 3am for when Postgres connection-pool exhaustion takes the proxy down.

The decision usually isn't about features. It's about who owns the pager. If you have an SRE budget and a hard requirement for code-level control, LiteLLM is the better answer. If you'd rather your AI engineers ship product instead of running a stateful Kubernetes deployment, FastRouter is.

The four dimensions teams actually weigh

1) Deployment model

FastRouter: Fully Managed

LiteLLM: Self-hosted (Postgres + Redis)

2) Built-in evals

FastRouter: Smart + Auto + GEPA + Video

LiteLLM: None (Langfuse integration)

3) Provider catalog

FastRouter: Major frontier + open

LiteLLM: 2,600+ models, 140+ providers

4) Air-gapped option

FastRouter: Managed only

LiteLLM: Yes

They're not in the same category — that's the comparison

LiteLLM ships in two forms. The litellm Python SDK is a thin abstraction layer that normalizes OpenAI, Anthropic, Bedrock, Azure, Vertex, and dozens more behind one signature. The litellm-proxy is a separate process you run that gives you a true gateway: a virtual key system, budgets, rate limits, model fallbacks, logging, and an admin UI. The proxy is what you're comparing to FastRouter — the SDK is closer to a normalization library.

FastRouter is gateway-only. There is no SDK to embed; you point your existing OpenAI-compatible client at FastRouter's endpoint and you're done. The product surface is the gateway plus the management plane around it — workspaces, audits, evals, prompt optimization, MCP credential vaulting.

The honest comparison is self-hosted LiteLLM Proxy vs managed FastRouter. That's the framing for the rest of this page.

Feature matrix

Where the two diverge today. ✓ supported, ✗ not supported, ◑ partial

What "self-hosted" actually means in production

The LiteLLM Proxy is a Python application backed by Postgres (mandatory for spend tracking, virtual keys, and the admin UI) and Redis (effectively required for rate-limit and budget enforcement at multi-instance scale). To run it reliably you also need:

• Container orchestration — usually Kubernetes, with the official Helm chart (currently in beta) or your own manifests.
• Database operations — schema migrations on upgrade, query tuning when log volume crosses ~1M requests, backup/restore, replicas if you care about uptime.
• Memory and worker tuning — Gunicorn worker count vs. CPU cores, the Python GIL pinching throughput at high concurrency, OOM avoidance on long-running streams.
• On-call — somebody pages when the proxy stops accepting connections at 2:30am because Postgres ran out of connections during a spike.

None of this is a knock on LiteLLM. It's the cost of owning your own infrastructure — the same cost you'd pay for any stateful platform you self-host. The question is whether your team has the bandwidth, and whether the "free OSS" line item makes sense once you account for it.

FastRouter pushes that entire cost to us. There is no Postgres to tune, no Helm chart to upgrade, no worker count to right-size. The trade-off is exactly the trade-off — you give up the control surface for the operational burden.

Self-hosted is "free" the same way a swimming pool is free — until you account for the chemicals, the inspection, and the kid who fell in.

Both routers are good. They optimize for different things.

LiteLLM's routing is mature and well-tested: simple-shuffle, least-busy, latency-based, cost-based, usage-based, and weighted strategies, with fallback chains and timeouts configurable per virtual key or model group. It is among the best open-source routing implementations in production.

FastRouter's seven strategies overlap with LiteLLM's six on most axes. The two routing primitives that don't have direct LiteLLM equivalents are category-based routing (map prompt classes to model groups without application changes) and the AI Auto Model Router (per-request model selection based on real-time cost, latency, and quality signals fed by the eval layer).

If you're optimizing for routing flexibility and are happy to write the policy in YAML, LiteLLM gets you most of the way. If you want the gateway to make per-request model decisions automatically, that's where FastRouter pulls ahead — and it's tightly coupled to having Smart and Automatic Evaluations running in the background, which LiteLLM doesn't ship.

Evaluations

The capability gap that's hard to close with integrations

LiteLLM has no built-in evaluation layer. The standard practice is to integrate Langfuse (most common pairing), Braintrust, Helicone (now in maintenance mode), or a custom pipeline. The integrations are clean — the LiteLLM SDK and Proxy both export traces to Langfuse with one config block — and for many teams that's enough.

FastRouter ships three eval primitives directly, with the routing layer wired into them:

• Smart Evaluations — AI quality scoring on live production calls. The model that's actually delivering the best output for your use case rises to the top automatically.
• Automatic Evaluations — background sampler that benchmarks competing models on a slice of your real traffic.
• GEPA — Generative Evolutionary Prompt Architecture searches across prompt and model combinations to find a Pareto-optimal prompt for your workload.

The practical difference: with LiteLLM + Langfuse, eval results sit in a separate dashboard that informs your decisions. With FastRouter, eval results are inputs to the AI Auto Model Router that picks per request. Both are valid; they're different operating models.

Total cost

Honest TCO at three scales

OSS is free in license, not in operations. Below is a rough breakdown of monthly carrying cost for self-hosted LiteLLM Proxy at three traffic tiers, assuming you're running it correctly with high availability. FastRouter's number is the managed-platform fee. Inference cost is identical and excluded from both.

Two different threat models

LiteLLM is open source, which means you can audit every line of code that handles your traffic. That's a real security property — for some teams it's the only acceptable answer. The corresponding trade-off is a much larger supply-chain surface: the Python package, its transitive dependencies, your container image, your Kubernetes operators. In March 2026 the LiteLLM PyPI package was briefly compromised for roughly 40 minutes before the maintainers responded; teams that pinned to latest in their CI pulled the bad release. The fix was fast and the postmortem was good — but the incident is a useful reminder that "open source" and "secure by default" aren't the same thing.

FastRouter is closed-source SaaS, which means you can't audit the code, but the supply chain is ours to defend. Patching, key rotation, dependency review, and SOC 2 evidence are operations we run, not work you inherit.

Both can be the right answer. The question is which set of trade-offs your security team prefers to own.

When LiteLLM is the right call

Air-gapped or sovereign deployment is a hard requirement

• Defense, intelligence, regulated EU, on-prem-only enterprises
• SaaS gateway is non-starter for compliance reasons
• You need to run in your own VPC end-to-end

You need code-level visibility or custom providers

• You want to read the gateway code path your traffic takes
• You need to add a custom provider (private inference, niche endpoint)
• You're okay shipping PRs upstream

You already have a strong DevOps function

• Postgres + Redis + Kubernetes is normal weekday work
• You'd rather pay engineers than vendors
• You want every operational lever exposed

You need the broadest provider catalog in the category

• 2,600+ models, 140+ providers
• Niche models often appear here first
• Easy to add a provider yourself if it's missing

When FastRouter is the right call

You don't want to own a stateful platform

• No Postgres, Redis, or Helm chart to babysit
• No on-call rotation for the gateway itself
• Your AI engineers ship product, not infra

You want evals and routing in one product

• Smart + Automatic Evaluations on live traffic
• GEPA prompt optimization runs continuously
• Eval signals drive AI Auto routing decisions

SSO, RBAC, audit logs without a paid tier

• Workspace governance included by default
• No upgrade-path negotiation when finance asks
• Audit trail across multi-step agent calls

You're running agentic workloads with MCP

• MCP credential vaulting — agents never see raw keys
• Per-tool budget caps and rate limits
• Trail across multi-step tool calls

Pick the right tool for your situation

1) You have a hard requirement for air-gapped, sovereign, or on-prem deployment ->

Use LiteLLM. There isn't a competitive managed alternative for this constraint.

2) You have a strong DevOps team and want every operational lever exposed ->

Use LiteLLM. Pair it with Langfuse for evals. Budget for the Postgres/Redis/Kubernetes operational overhead honestly.

3) You want governance, evals, and routing in one product without standing up infra ->

Use FastRouter. The 7-day audit will tell you what your routing efficiency actually is on your existing traffic.

4) You're already running LiteLLM and the operational tax is becoming uncomfortable ->

Talk to FastRouter. Both are OpenAI-compatible; migrations from LiteLLM are mostly endpoint and key changes.

5) You need the broadest possible provider catalog or a custom provider PR ->

Use LiteLLM. The 2,600+ catalog and "ship a provider in a PR" pattern are unique strengths

Common questions

1) Can FastRouter and LiteLLM coexist in the same stack?

Yes. A common pattern is LiteLLM SDK in application code (for normalization across providers) with FastRouter as the gateway. LiteLLM Proxy as the gateway and FastRouter as the gateway are mutually exclusive — pick one for the gateway role.

2) Is LiteLLM really free?

The OSS license is MIT. The runtime cost is whatever you pay for the Postgres, Redis, container orchestration, and engineering time to keep it healthy. LiteLLM Enterprise (SSO, team RBAC, audit logs, dedicated support) is paid — typical contracts we've seen land in the ~$30K/year range, but exact pricing isn't public.

3) How hard is it to migrate from LiteLLM Proxy to FastRouter?

Not very. Both expose an OpenAI-compatible /v1/chat/completions endpoint. Migration is typically a base URL change, a key swap, and porting your routing config from LiteLLM's YAML to FastRouter's strategies. Virtual keys map to FastRouter API keys with budgets and rate limits attached. We help with the cutover for production workloads.

4) What about the March 2026 LiteLLM PyPI incident?

The package was compromised for roughly 40 minutes before the maintainers responded and pulled the bad release. Teams that pinned to latest in CI pulled the malicious version. It's a useful reminder to pin versions and audit lockfiles for any OSS dependency that handles credentials — not a unique LiteLLM problem, but a real security cost of any self-hosted Python proxy.

5) Does LiteLLM have built-in evals?

No. LiteLLM ships logging and request tracking, but no eval framework of its own. The standard pairing is Langfuse for traces, scores, datasets, and experiments. FastRouter ships Smart Evaluations, Automatic Evaluations, and GEPA prompt optimization natively.

6) Can FastRouter run in our VPC?

FastRouter is a managed SaaS today. Single-tenant and dedicated regional deployments are available for enterprise customers — talk to us. If you have a hard air-gapped requirement, LiteLLM is genuinely the better answer.

7) Which has better routing?

Both are good. LiteLLM's six strategies are mature and battle-tested. FastRouter's seven include category-based routing and the AI Auto Model Router, neither of which have direct LiteLLM equivalents. The bigger differentiator isn't the count — it's that FastRouter's routing is wired into the eval layer, which LiteLLM doesn't ship.