[ PERFORMANCE AT A GLANCE ]
[ WHY MIGRATE ]
While LiteLLM works well for prototyping, teams scaling to production need infrastructure that doesn't become a bottleneck.
Built in Go with just 1.68S overhead at 500 RPS compared to 90.72s for Python-based solutions. Your gateway stops being the bottleneck.
99.999% uptime SLA with automatic failover, circuit breakers, and intelligent retry logic. No more 4-minute latency spikes at high load.
Semantic caching reduces costs and latency on repeated queries. Adaptive load balancing ensures efficient resource utilization.
Virtual keys with budgets, RBAC, audit logs, and in-VPC deployments. Full control over your AI infrastructure.
Built-in Prometheus metrics, OpenTelemetry support, and integration with Maxim's evaluation platform. No sidecars needed.
OpenAI-compatible API means zero code changes. Point your existing LiteLLM integration to Bifrost and you're done.
[ PERFORMANCE BENCHMARKS ]
Tested on identical AWS t3.xlarge instances. Bifrost delivers consistent, predictable performance under load.
| Metric | Bifrost | LiteLLM |
|---|---|---|
| Overhead per Request (500 RPS) | 11µs | ~40ms (40.4x slower) |
| P99 Latency at 500 RPS | 1.68s | 90.72s |
| Maximum Sustained RPS | 5,000+ stable | Fails at high load |
[ FEATURE COMPARISON ]
| Feature | Bifrost | LiteLLM |
|---|---|---|
| Performance | ||
| Overhead at 500 RPS | 11µs (Go-native) | 40ms (Python GIL) |
| Concurrent Request Handling | Native Go concurrency | Async overhead |
| Reliability | ||
| Automatic Failover | Zero-config | Manual config |
| Circuit Breakers | Available | N/A |
| Health Monitoring | Real-time | Basic |
| Governance & Security | ||
| Virtual Keys | With budgets & rate limits | Available |
| RBAC | Fine-grained access management | Available |
| Audit Logs | Available | Available |
| Guardrails | Available | Available |
| In-VPC Deployment | Available | Available |
| Observability | ||
| Prometheus Metrics | Native, no sidecars | Via callbacks |
| OpenTelemetry | OTel compatible | OTel compatible |
| Request Logging | Multiple backends | Multiple backends |
| Developer Experience | ||
| Setup Time | 30 seconds (NPX or Docker) | 5-10 minute setup |
| Web UI | Real-time config | Admin panel available |
| Configuration | Web UI, API, or file-based | Web UI, API, or file-based |
| MCP Support | Native gateway | Beta integration |
| Deployment Asset | Single binary, Docker, K8s | Python package, Docker |
| Docker Size | 80 MB | > 700 MB |
| Architecture | ||
| Language | Go | Python |
| Clustering | Available | N/A |
| Adaptive load Balancing | Dynamic weight adjustment | N/A |
| Usage-Based Routing Rules | Yes | N/A |
| Plugin System | Go-based | Python callbacks |
| License | Apache 2.0 | MIT |
[ MIGRATION STEPS ]
The OpenAI-compatible API means most applications require zero code changes. Just update the base URL.
Choose your preferred method. Bifrost starts immediately with zero configuration needed.
Add your LLM provider API keys via the web UI at localhost:8080 or a configuration file.
Change one line in your application. Bifrost's OpenAI-compatible API means zero other code changes.
openai/gpt-4o format for explicit provider control.[ CODE COMPARISON ]
Before (LiteLLM)
import openai
client = openai.OpenAI(
api_key="your-litellm-key",
base_url="http://localhost:4000"
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user",
"content": "Hello!"}]
)After (Bifrost)
import openai
client = openai.OpenAI(
api_key="your-bifrost-key",
base_url="http://localhost:8080"
)
response = client.chat.completions.create(
model="openai/gpt-4o",
messages=[{"role": "user",
"content": "Hello!"}]
)Bifrost uses the provider/model format (e.g., openai/gpt-4o) for explicit routing control.
[ COMMON SCENARIOS ]
LiteLLM virtual keys for team budgets map directly to Bifrost's equivalent functionality.
curl -X POST http://localhost:8080/api/keys \
-H "Content-Type: application/json" \
-d '{
"name": "team-engineering",
"budget": 1000,
"rate_limit": 100,
"models": ["openai/gpt-4o",
"anthropic/claude-sonnet-4-20250514"]
}'Use the standard OpenAI SDK pointed at Bifrost.
import openai
client = openai.OpenAI(
base_url="http://localhost:8080",
api_key="your-key"
)Use the LiteLLM Python SDK with Bifrost as the proxy backend.
import litellm
litellm.api_base = "http://localhost:8080/litellm"
response = litellm.completion(
model="openai/gpt-4o",
messages=[{"role": "user",
"content": "Hello!"}]
)[ WHEN TO MIGRATE ]
[ BIFROST FEATURES ]
Everything you need to run AI in production, from free open source to enterprise-grade features.
01 Model Catalog
Access 8+ providers and 1000+ AI models from multiple providers through a unified interface. Also support custom deployed models!
02 Budgeting
Set spending limits and track costs across teams, projects, and models.
03 Provider Fallback
Automatic failover between providers ensures 99.99% uptime for your applications.
04 MCP Gateway
Centralize all MCP tool connections, governance, security, and auth. Your AI can safely use MCP tools with centralized policy enforcement. Bye bye chaos!
05 Virtual Key Management
Create different virtual keys for different use-cases with independent budgets and access control.
06 Unified Interface
One consistent API for all providers. Switch models without changing code.
07 Drop-in Replacement
Replace your existing SDK with just one line change. Compatible with OpenAI, Anthropic, LiteLLM, Google Genai, Langchain and more.
08 Built-in Observability
Out-of-the-box OpenTelemetry support for observability. Built-in dashboard for quick glances without any complex setup.
09 Community Support
Active Discord community with responsive support and regular updates.
Change just one line of code. Works with OpenAI, Anthropic, Vercel AI SDK, LangChain, and more.