Performance

Hyperion vs Bifrost vs LiteLLM vs Portkey

This page compares gateway behavior across four stacks using normalized criteria. It is designed for architecture decisions, not leaderboard-style claims across mismatched test harnesses.

How to Compare Fairly

  • Keep test shape fixed: same concurrency, request count, payload size, and cache policy.
  • Separate gateway overhead from upstream model latency.
  • Use p95/p99 and failure rate as decision metrics, not only average RTT.
  • Track memory and CPU utilization at the same load tier.

At-a-Glance Comparison

GatewayOverheadThroughput ProfileTail LatencyMemory ProfileBest Fit
HyperionMicrosecond-class in isolated harnessHigh in compiled direct-executor pathLow when queue pressure is controlledModerate and predictableTeams optimizing end-to-end gateway + policy + caching in one stack
BifrostVery low published gateway overheadHigh with performance-first pathingDepends on feature path and harness parityVaries by deployment shapeTeams prioritizing minimal proxy overhead
LiteLLMHigher than pure-proxy low-level implementationsStrong under practical multi-instance loadGood p95/p99 in published Portkey comparisonHigher footprint in cited benchmarksTeams wanting broad provider abstraction with fast time-to-adoption
PortkeyCompetitive baseline in hosted gateway workflowsStable under tested profileHigher p95/p99 than LiteLLM in cited runLower footprint in cited runTeams prioritizing managed gateway workflows and consistency

Published Signals You Can Reuse

  • LiteLLM vs Portkey published tests suggest stronger LiteLLM p95/p99 under that benchmark setup.
  • Portkey in the same cited run shows lower memory footprint and stable medians.
  • Bifrost publishes very low gateway-overhead-focused numbers in performance mode.
  • Hyperion internal benchmark mode targets similar overhead isolation with direct in-container harness.

Decision Heuristic

  • Pick Hyperion/Bifrost when raw gateway tax is your top constraint.
  • Pick LiteLLM/Portkey when ecosystem or managed workflow speed matters more.
  • For production choice, run your own apples-to-apples benchmark before finalizing.
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Run Benchmarks Locally

Use your own infra and traffic shape for final selection.

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Caching Internals

Understand how cache tiers shape throughput and costs.

Last updated: Feb 22, 2026