LiteLLM vs Bifrost

The question that decides it: Do you value provider breadth and a large operator community, or a Go runtime without a documented throughput ceiling?

Our verdict

Almost always LiteLLM, for coverage and community. Choose Bifrost if your traffic profile is high, steady RPS through a small set of providers and you would rather not engineer around a Python proxy — but do your own load test, because neither vendor's numbers are independent.

Why

This is the closest genuine like-for-like pair in the catalog. Both are free to self-host, both are permissively licensed (MIT for LiteLLM, Apache-2.0 for Bifrost), both support air-gapped deployment, and both ship virtual keys, budgets, semantic caching, guardrails, MCP and config-as-code at no cost. Neither has any compliance certification of its own, because neither is a service — you deploy it, so the attestations are yours to earn.

Coverage is where they separate. LiteLLM lists 140 providers; Bifrost states 20+. Neither publishes a model total - both have been quoted with one, and neither vendor's own pages support it - so the provider gap is the number to judge on, and for most teams it decides the question. The community gap points the same way: 57,500 GitHub stars against 7,600. When you need a Terraform module, a Helm values example, or someone who has already debugged your exact upstream, LiteLLM has an order of magnitude more people to have written it down.

Bifrost's argument is the runtime. It is Go rather than Python, which sidesteps the GIL contention that third-party operators report in LiteLLM past roughly 300 RPS per instance. Bifrost claims 0.99 ms of added latency at 5,000 RPS; LiteLLM claims 0.66 ms at 2,800 RPS. Both of those are vendor-run against mock upstreams, and no independent benchmark exists for either. The latency figures in particular are not comparable in any meaningful way — they were produced under different conditions by parties with an interest in the result.

What is more credible than either benchmark is the structural point: a Go proxy does not have a GIL, and LiteLLM operators have publicly documented hitting a Python concurrency wall. If your workload is sustained high RPS and you would rather not run a fleet of sharded Python instances, that is a reasonable engineering preference. Just note the price: Bifrost gates guardrails, cluster mode, adaptive load balancing, SSO, RBAC and audit logs to an enterprise tier at unpublished prices, and its own provider and model counts are inconsistent across its pages.

Which one, concretely

Choose LiteLLM if

  • You need broad provider coverage — 140 providers against 23
  • You want the largest operator community and the most third-party deployment material
  • You need batch API support, which Bifrost does not list
  • You are at moderate RPS where the Python ceiling is not your constraint

Choose Bifrost if

  • You expect sustained high RPS and want to avoid sharding a Python proxy
  • You want semantic caching, MCP, budgets and a prompt repository in the free tier
  • You prefer a Go binary to a Python service for operational reasons
  • You want a single vendor you can later buy clustering and SSO from

What catches people out

Side by side

5 of 18 fields differ, marked with a dot. Every figure links to the vendor page it came from. Blank values read Not published rather than No — silence from a vendor is not a negative answer.

Field LiteLLM Bifrost
Ease of leaving Derived score, higher is easier 100/100 Easy to leave 100/100 Easy to leave
What kind of product Category Open source Open source
Who runs it Deployment model Self-host only Self-host only
Licence Licence MIT Apache-2.0
Model providers reachable Upstream providers Not published 20–23
Markup on model prices Token markup None None
Fee to add funds Credit purchase fee None None
Monthly cost per person Seat fee None None
GitHub stars GitHub stars 57,500 7,600
Delay it adds Proxy overhead 0.66 ms 0.99 ms
Requests per second ceiling Throughput 2,800 rps 5,000 rps
Runs fully disconnected Air-gapped deployment Yes Yes
Similar-question caching Semantic cache Yes Yes
Content guardrails Content guardrails Yes Yes
Separate keys per team or app Virtual keys Yes Yes
Spending limits Budget controls Yes Yes
MCP support MCP support Yes Yes
Batch processing Batch processing Yes Not published
Settings can live in version control Declarative config-as-code Yes Yes

Verified 3 days ago for LiteLLM and Verified 3 days ago for Bifrost. Want more fields, or a third option in the mix? Open these two in the full comparison tool.

Common questions

Is Bifrost faster than LiteLLM?

Bifrost claims 0.99 ms added latency at 5,000 RPS and LiteLLM claims 0.66 ms at 2,800 RPS, but both figures are vendor-run against mock upstreams under different conditions and no independent benchmark exists for either. The more credible structural point is that Bifrost is written in Go and so avoids the Python GIL contention that LiteLLM operators report past roughly 300 RPS per instance.

Which has more providers?

LiteLLM, by a wide margin: 140 providers against the 20+ Bifrost states. Neither publishes a model count, and figures circulating for both trace back to no vendor page we could find.

Are both really free?

Both have genuinely free self-hosted tiers under permissive licences. But Bifrost gates guardrails, cluster mode, adaptive load balancing, SSO, RBAC and audit logs to an enterprise tier at unpublished prices, and LiteLLM gates SSO, RBAC, audit logs and SCIM behind a commercial licence, also unpublished.