How Does Bifrost Compare to LiteLLM in Performance?

Bifrost (by Maxim AI) publishes benchmarks claiming substantially better performance than LiteLLM under load: roughly 50x faster P99 latency, about 9.5x higher throughput, around 68% less memory usage, and an 11.22% higher success rate. These numbers come from Bifrost's own site and describe a specific test — both gateways running at 500 requests per second (RPS) on identical hardware — so treat them as vendor-reported results for that scenario rather than a universal guarantee.

The published benchmark numbers

Bifrost's site presents a head-to-head comparison against LiteLLM at 500 RPS on the same hardware. The headline figures:

Bifrost also states that beyond 500 RPS, LiteLLM "breaks with latency going up to 4 minutes." That is a strong claim about LiteLLM's behavior at sustained load, and it is the vendor's characterization, not an independent measurement.

What the benchmark actually tests

The comparison is scoped to one condition: 500 RPS on identical hardware. That framing matters for interpretation:

• It is a load test, not a feature comparison. The numbers describe latency, throughput, memory, and success rate under concurrent traffic — not routing quality, governance, or guardrail behavior.
• "Identical hardware" is asserted, not detailed. The site does not publish the machine specs, request mix, or model endpoints used, so you cannot fully reproduce the setup from the page alone.
• P99 latency is a tail metric. A 50x P99 improvement means the slowest 1% of requests are far faster under load. That is the metric most relevant to user-facing timeouts and retries, which is why it is the headline.

Bifrost's own performance profile

Separate from the LiteLLM comparison, Bifrost lists its own operating figures:

• Added latency: 20 microseconds
• Throughput: 5,000 req/s
• Peak memory: 3.3 GB

These describe Bifrost in isolation. The 5,000 req/s throughput figure is roughly 10x the 500 RPS benchmark point, which suggests the LiteLLM comparison was run well below Bifrost's stated ceiling.

How to decide between them

The benchmark answers one question — which gateway holds up better under sustained load — and Bifrost's answer is "Bifrost, by a wide margin." Whether that matters for you depends on your situation:

• Choose based on your load profile. If you are running hundreds of RPS through a gateway and seeing latency spikes or failures, the tail-latency and success-rate gaps are the relevant numbers. If your traffic is low, the difference is less likely to be visible.
• Verify before committing. Vendor benchmarks use their own hardware and request mix. Run your own load test with your models, prompt sizes, and concurrency before treating the 50x figure as predictive of your setup.
• Weigh non-performance factors too. Bifrost positions itself around governance (per-team or per-virtual-key budgets, audit logs, SSO), an MCP gateway, and guardrails. LiteLLM has its own ecosystem and adoption. Performance is one axis among several.

Trying it yourself

Bifrost is open source and the site shows a one-command start:

npx @maximhq/bifrost

It also advertises a drop-in replacement pattern — changing one line of code to route through the gateway — with compatibility for OpenAI, Anthropic, Vercel AI SDK, LangChain, and others. The site offers a 14-day free trial of Bifrost Enterprise and a "Request access" path; pricing details live on a separate pricing page, so check there for current terms rather than assuming a free tier.

To validate the performance claims against your own workload, the practical path is: stand up Bifrost, point a representative load test at it, and compare P99 latency, throughput, memory, and error rate against your current LiteLLM setup on the same hardware. That reproduces the vendor's methodology with your own traffic and gives you numbers you can act on.