FastRoutervs.LiteLLM
A managed gateway versus the most popular open-source LLM proxy. They solve overlapping problems, with very different trade-offs around operational overhead, evaluations, and supply-chain risk.
By Siv Souvam~14 min readShort version
The quick decision
| If you are... | Use... | Why? |
|---|---|---|
| Running air-gapped or sovereign deployments | LiteLLM | OSS, MIT-licensed, runs in any VPC or on-prem environment. |
| Want a managed gateway without ops burden | FastRouter | No Postgres, Redis, or Helm chart to babysit. SSO + RBAC included. |
| Need the broadest provider catalog | LiteLLM | 2,600+ models, 140+ providers, easy to add a custom one via PR. |
| Want evals and routing in one product | FastRouter | Smart + Automatic Evaluations and GEPA prompt optimization built in. |
At a glance: Key metrics
| Metric | FastRouter | LiteLLM |
|---|---|---|
| Deployment model | Managed SaaS | Self-hosted (Postgres + Redis required) |
| License / cost | Managed-service fee, 0% markup | MIT (free) + infra + engineering time |
| Provider catalog | Major frontier + open providers | 2,600+ models, 140+ providers |
| Built-in evals | Smart + Auto + GEPA | None (typically paired with Langfuse) |
Feature matrix
| Capability | FastRouter | LiteLLM |
|---|---|---|
| Deployment | Fully managed | Self-hosted (Postgres + Redis) |
| Air-gapped / sovereign | Not supported | Run in your VPC or on-prem |
| Custom provider extensibility | Via support request | Add provider in a PR |
| Built-in evals | Smart + Auto + GEPA | Pair with Langfuse / Braintrust |
| SSO + RBAC + audit logs | Included | Enterprise tier (~$30K/yr) |
| MCP credential vaulting | Agents never see raw keys | MCP server support, vault less mature |
| Source-code visibility | Closed source | MIT-licensed on GitHub |
Deep dive: Operations & evals
1. Operational reality
LiteLLM is a self-hosted Python proxy. Postgres is mandatory and Redis is the practical default for production rate-limits and budget enforcement, which means you own uptime, DB migrations, scaling, and patching. FastRouter is fully managed, so there is nothing to deploy and no on-call rotation for the gateway itself.
2. The eval gap
LiteLLM has no native eval framework. The standard pattern is to pair it with Langfuse for traces and scoring. FastRouter ships Smart Evaluations, Automatic Evaluations, and GEPA (Generative Evolutionary Prompt Architecture) on production traffic, and those signals feed routing decisions automatically.
Total cost: free isn't free
LiteLLM's license is MIT (free). Real cost is infrastructure plus engineering hours to keep it healthy. At about $2K/month in inference, expect roughly $200 of infra and 5 engineering hours, which works out to a $950/month carrying cost. At $10K, it's around $600 infra and 15 hours (about $2,850). At $100K, it's around $2,500 infra and 40 hours (about $8,500).
Engineering at $150/hr fully loaded. LiteLLM Enterprise (SSO, RBAC, audit logs) is separate, typically ~$30K/year. FastRouter's flat managed-service fee covers all of that.
Security & supply chain
OSS gives you code visibility — for some teams it's the only acceptable answer. The trade-off is a larger supply-chain surface. In March 2026 the LiteLLM PyPI package was briefly compromised for ~40 minutes; teams pinned to latest pulled the bad release. Not a unique LiteLLM problem, but a real cost of any self-hosted Python proxy that handles credentials.
Final decision tree
Air-gapped or sovereign required? → LiteLLM. There isn't a competitive managed alternative for this constraint.
Strong DevOps team and want every operational lever exposed? → LiteLLM. Pair with Langfuse for evals.
Want governance, evals, and routing in one product without standing up infra? → FastRouter.
Need the broadest possible provider catalog? → LiteLLM. 2,600+ models and a "ship a provider in a PR" pattern.
Side-by-side
The full feature breakdown
✓ supported, ✗ not supported, ◑ partial.
| Capability | FastRouterManaged gateway | LiteLLMSelf-hosted proxy |
|---|---|---|
| Deployment model | Fully managed. Nothing to deploy. | Self-hosted. Postgres mandatory; Redis recommended for production rate-limit and budget enforcement. |
| License / cost shape | Flat managed-service fee, 0% markup with BYOK | MIT (free) + your infra + engineering time |
| Model catalog | Major frontier + most-used open providers | 2,600+ models, 140+ providers — broadest in the category |
| Custom provider extensibility | ◑Request via support | Add a provider in code, ship a PR |
| Routing strategies | 7: category, priority, lowest latency, lowest price, highest throughput, weighted, AI Auto | 6: simple-shuffle, least-busy, latency-based, cost-based, usage-based, weighted |
| AI Auto Model Router | Picks per request from cost, latency, and your eval scores | Not supported |
| Smart / Automatic Evaluations | Built in, on production traffic | Integrate Langfuse / Braintrust separately |
| GEPA prompt optimization | Proprietary evolutionary optimizer | Not supported |
| Video evaluations | Compare models on video inputs | Not supported |
| MCP credential vaulting | Agents never see raw provider keys | ◑MCP server support (v1.80.18+); credential vault less mature |
| Virtual API keys / budgets | Per-workspace, per-team, per-key | Per-key, per-team, per-model — well-tested |
| SSO (SAML, OIDC) | Included | ◑Enterprise tier only |
| Team RBAC + audit logs | Included | ◑Enterprise tier only |
| PII redaction / guardrails | Built in | Presidio, Pillar, CrowdStrike integrations |
| OpenAI-compatible endpoint | Supported | Supported |
| Air-gapped / sovereign deployment | Not supported | Run anywhere — single-tenant, on-prem, isolated VPC |
| Source-code visibility | Closed source | MIT-licensed, full source on GitHub |
| Operational ownership | FastRouter owns uptime, scaling, patching | You own uptime, DB migrations, scaling, patching |
Air-gapped deployment, full code visibility, custom-provider PRs. If those are hard requirements for compliance, sovereignty, or research workflows, FastRouter is the wrong tool and we'd say so to your face.
Operational reality
What "self-hosted" actually costs in production
The LiteLLM Proxy is a Python application backed by Postgres (mandatory for spend tracking, virtual keys, the admin UI) and Redis (effectively required for rate-limit and budget enforcement across multiple instances). To run it reliably you also need:
Container orchestration — usually Kubernetes, via the official Helm chart (currently in beta) or your own manifests.
Database operations — schema migrations on upgrade, query tuning past ~1M request log rows, backup/restore, replicas for HA.
Worker and memory tuning — Gunicorn worker count vs. CPU cores, Python GIL pinching 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 any stateful platform. FastRouter takes that operational layer off your plate; you give up the control surface in exchange.
~1M log rows in roughly 10 days at moderate volume requiring DB partitioning; ~500µs per-request overhead that compounds with high QPS; OOM events under bursty load (late 2025); Python GIL ceiling around 1–2M requests/day per node forcing horizontal scaling. All solvable. None automatic.
Routing
6 strategies vs 7
LiteLLM's routing
Six strategies, all mature and well-tested: simple-shuffle, least-busy, latency-based, cost-based, usage-based, and weighted. Fallback chains and per-request timeouts are configurable per virtual key or model group. Among the best open-source routing implementations in production.
FastRouter's seven
The first five overlap. Two routing primitives don't have direct LiteLLM equivalents: category-based routing (map prompt classes to model groups without changing app code) and the AI Auto Model Router (per-request model selection from real-time cost, latency, and quality signals fed by FastRouter's eval layer).
If routing flexibility via config is enough and you're happy writing policy in YAML, LiteLLM covers it. If you want the gateway to make per-request decisions automatically — and the eval layer to influence those decisions — that's where FastRouter pulls ahead.
Both ship solid routing. Only FastRouter's routing is wired into a built-in eval layer.
Evaluations
The eval gap
LiteLLM has no native eval layer
The standard pattern is to integrate Langfuse (most common pairing), Braintrust, or a custom pipeline. The integrations are clean — both the LiteLLM SDK and Proxy export traces to Langfuse with one config block. For many teams that's enough.
FastRouter ships three eval primitives
Smart Evaluations — AI quality scoring on live production calls. The model that's actually delivering on your use case rises to the top automatically.
Automatic Evaluations — background sampler that benchmarks competing models on a slice of real traffic.
GEPA — Generative Evolutionary Prompt Architecture searches across prompt × model combinations toward Pareto-optimal pairs.
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.
LiteLLM + Langfuse = two platforms with manual feedback loop. FastRouter = one platform where eval signals drive routing automatically.
Total cost
Honest TCO at three scales
OSS is free in license, not in operations. Rough monthly carrying cost for self-hosted LiteLLM Proxy at three traffic tiers, assuming HA configuration. FastRouter's number is the flat managed-service fee. Inference cost is identical and excluded from both.
Engineering assumed at $150/hr fully loaded. LiteLLM Enterprise (SSO, RBAC, audit logs) is a separate paid tier — typical contracts we've seen land in the ~$30K/year range.
"Free" OSS at $100K/month inference scale = roughly $8,500/month in infra + engineering hours, plus a separate ~$30K/year for the enterprise governance features.
Honest TCO at three scales table
| Scale | LiteLLM infra (HA) | LiteLLM engineering | LiteLLM total | FastRouter |
|---|---|---|---|---|
| Small (~$2K/mo inference) | ~$200 | ~5 hrs · $750 | ~$950 | Audit free · then flat platform fee |
| Mid (~$10K/mo inference) | ~$600 | ~15 hrs · $2,250 | ~$2,850 | Flat platform fee — typically lower |
| Large (~$100K/mo inference) | ~$2,500 | ~40 hrs · $6,000 | ~$8,500 | Flat platform fee + 40–60% inference savings via routing |
Security & supply chain
Two different threat models
LiteLLM is open source — you can audit every line of code handling your traffic. For some teams that's the only acceptable answer. The trade-off is a larger supply-chain surface: the Python package, transitive dependencies, container image, Kubernetes operators.
In March 2026 the LiteLLM PyPI package was briefly compromised for roughly 40 minutes before the maintainers responded and pulled the bad release. Teams pinned to latest in CI pulled the malicious version. The fix was fast and the postmortem was good — not a unique LiteLLM problem, but a useful reminder that OSS handling credentials is a supply-chain risk surface.
FastRouter is closed-source SaaS — no code audit, but the supply chain is ours to defend. Patching, key rotation, dependency review, SOC 2 evidence are operations we run, not work you inherit.
Honest take
When each one wins
When LiteLLM is the better pick
Air-gapped or sovereign deployment is required
- Defense, intelligence, regulated EU, on-prem-only enterprises
- SaaS gateway is a non-starter for compliance reasons
- You need to run end-to-end in your own VPC
You need code-level visibility or custom providers
- Audit every line of the gateway code path
- Add a custom provider (private inference, niche endpoint)
- Comfortable shipping PRs upstream
You already run a strong DevOps function
- Postgres + Redis + Kubernetes is normal weekday work
- Pay engineers, not vendors
- Want every operational lever exposed
Broadest provider catalog matters most
- 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 better pick
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
- 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
How to choose
The decision tree
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.
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.
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.
You're already on LiteLLM and the operational tax is becoming uncomfortable
Talk to FastRouter. Both are OpenAI-compatible; migrations from LiteLLM are mostly endpoint and key changes.
You need the broadest possible provider catalog or a custom provider PR
Use LiteLLM. 2,600+ catalog and "ship a provider in a PR" pattern are unique strengths.
Things people ask before they switch
Yes. A common pattern is LiteLLM SDK in application code (for normalizing 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.
The OSS license is MIT. The runtime cost is whatever you pay for 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.
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 cutover for production workloads.
The package was compromised for roughly 40 minutes before the maintainers responded and pulled the bad release. Teams pinned to latest in CI pulled the malicious version. 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.
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.
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.
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.