LiteLLM vs Bifrost: I Tested Both in Production

I ran LiteLLM and Bifrost side by side for two weeks.

I used the same traffic, same models, and same infra. I needed to pick one gateway for my team. I wanted real data instead of marketing claims.

Here are my findings.

The Test Setup I used c5.xlarge instances with 4 vCPUs and 8GB RAM. I did not use small test instances. I used real user requests from our agent platform at 200 to 400 requests per second.

Provider Coverage

  • LiteLLM supports over 100 providers.
  • Bifrost supports around 23 providers.

LiteLLM handles OpenAI, Anthropic, Bedrock, Vertex, Groq, and Deepseek using a simple config. Bifrost lacked some of our required providers. This made it a dealbreaker for us.

Performance Bifrost is faster on raw gateway overhead because it uses Go. I measured around 0.08ms overhead. LiteLLM's Python proxy added about 7ms to 8ms per request.

However, an LLM call takes 500ms to 30 seconds. A 7ms delay is almost invisible compared to the model response time.

Also, LiteLLM just released a Rust-based gateway. This brings overhead down to 0.05ms. This closes the performance gap.

Spend Tracking This is where LiteLLM wins. It tracks spend automatically across every key and every team.

  • You get per-key budgets.
  • You get per-team budgets.
  • You get daily spend reports.

Bifrost has budget limits, but LiteLLM provides deep cost attribution. When you run 10 million calls a month, your CTO will ask exactly how much each team spent on each model. LiteLLM gives you that answer immediately.

Routing Strategies LiteLLM offers five routing strategies:

  • Simple shuffle
  • Least busy
  • Latency-based
  • Cost-based
  • Usage-based

Bifrost has weighted and adaptive routing, but it lacks cost-based routing. LiteLLM can automatically pick the cheapest model for a request.

Verdict I chose LiteLLM.

The provider list and spend tracking were the main reasons. Bifrost is great engineering for small teams using only OpenAI or Anthropic. But for scale and variety, LiteLLM is more practical.

Source: https://dev.to/paultwist/litellm-vs-bifrost-i-tested-both-in-production-heres-what-actually-matters-c9b

Optional learning community: https://t.me/GyaanSetuAi