Markdown version of this page: https://www.getmaxim.ai/bifrost/resources/benchmarks.md
Agent-readable HTML version of this page: https://www.getmaxim.ai/bifrost/resources/benchmarks?mode=agent
Enterprise-grade performance comparison. Built in Go for maximum throughput and minimal latency. See the numbers that matter.
[ PERFORMANCE AT A GLANCE ]
[ LIVE SIMULATION ]
All values from actual benchmark at 500 RPS on AWS t3.medium (2 vCPU, 4GB RAM). Simulated samples reflect measured P50/P99/Max distributions. Full benchmark report
[ DETAILED METRICS ]
Primary performance metrics under sustained load
Percentage of requests completed successfully
Median response time
99th percentile response time
Maximum observed response time
Requests processed per second
Maximum memory consumption
Internal latency overhead (60ms mock OpenAI response)
Median end-to-end latency
Internal processing time (excluding 60ms mock OpenAI call)
Maximum sustainable requests per second
[ HIGH-THROUGHPUT STRESS TEST ]
Bifrost-only stress test at 5000 RPS with ~10KB response payloads. Gateway overhead excludes upstream response time. Full benchmark report
[ WHY BIFROST IS FASTER ]
| Feature | Bifrost | LiteLLM |
|---|---|---|
| Language | Go | Python |
| Async Runtime | Goroutines | asyncio |
| HTTP Server | Fast http | FastAPI/Uvicorn |
| Memory Model | Efficient GC | GC-managed |
| Concurrency | Native goroutines | GIL-limited |
| Binary Size | ~80MB | ~500MB+ (with deps) |
| Open Source | Yes (Apache 2.0) | Yes (MIT) |
Bifrost's Go implementation uses efficient parsing and memory-optimized data structures, minimizing allocations and leveraging Go's highly efficient garbage collector.
Built with Go's goroutines, Bifrost handles thousands of concurrent connections efficiently without the Python GIL bottleneck that limits LiteLLM's parallelism.
With Go's low-latency garbage collector and efficient memory management, Bifrost maintains consistent performance under load while using 68% less memory.
[ BIFROST FEATURES ]
Everything you need to run AI in production, from free open source to enterprise-grade features.
01 Governance
SAML support for SSO and Role-based access control and policy enforcement for team collaboration.
02 Adaptive Load Balancing
Automatically optimizes traffic distribution across provider keys and models based on real-time performance metrics.
03 Cluster Mode
High availability deployment with automatic failover and load balancing. Peer-to-peer clustering where every instance is equal.
04 Alerts
Real-time notifications for budget limits, failures, and performance issues on Email, Slack, PagerDuty, Teams, Webhook and more.
05 Log Exports
Export and analyze request logs, traces, and telemetry data from Bifrost with enterprise-grade data export capabilities for compliance, monitoring, and analytics.
06 Audit Logs
Comprehensive logging and audit trails for compliance and debugging.
07 Vault Support
Secure API key management with HashiCorp Vault, AWS Secrets Manager, Google Secret Manager, and Azure Key Vault integration.
08 VPC Deployment
Deploy Bifrost within your private cloud infrastructure with VPC isolation, custom networking, and enhanced security controls.
09 Guardrails
Automatically detect and block unsafe model outputs with real-time policy enforcement and content moderation across all agents.
[ SHIP RELIABLE AI ]
Change just one line of code. Works with OpenAI, Anthropic, Vercel AI SDK, LangChain, and more.
[ FAQ ]
The 500 RPS load test used AWS EC2 t3.medium instances in us-east-1 with 2 vCPU, 4GB RAM, Tier 5 OpenAI access, a 60-second test duration, and 500 concurrent virtual users. Gateway overhead tests used a 60ms mock OpenAI response. The 5,000 RPS stress test reports Bifrost internal overhead on t3.medium and t3.xlarge instances.
Bifrost is built in Go, which compiles to native machine code and uses goroutines for lightweight concurrency. LiteLLM is Python-based, which means it's subject to the Global Interpreter Lock (GIL), asyncio overhead, and higher memory consumption from dynamic typing and garbage collection.
No. Bifrost provides an OpenAI-compatible API, so you only need to change the base URL in your application. The same SDKs, request formats, and response structures work without modification.
Gateway overhead measures the additional latency the proxy adds on top of the actual LLM provider response time. In the gateway overhead test with a 60ms mock OpenAI response, Bifrost measured 0.99ms overhead. In the 5,000 RPS stress test on t3.xlarge, Bifrost measured 11µs internal overhead.
The published benchmark data covers a 500 RPS Bifrost vs LiteLLM load test and Bifrost-only 5,000 RPS stress tests on t3.medium and t3.xlarge instances. Bifrost maintains a 100% success rate in the published 5,000 RPS stress tests. Teams should validate higher targets on their own instance sizes, provider mix, and payload profile.