[ 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 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 ]
Most migrations take 15-30 minutes. Since Bifrost provides an OpenAI-compatible API, you typically only need to change the base URL in your application. Provider configuration can be done through the web UI without editing config files.
LiteLLM virtual keys need to be recreated in Bifrost, but the concepts map directly. Bifrost virtual keys support the same functionality including team budgets, rate limits, and model restrictions. You can configure them via the web UI or API.
Yes. You can point the LiteLLM Python SDK at Bifrost by setting the api_base to your Bifrost URL. This allows a gradual migration where you swap the backend without changing application code.
Bifrost uses a provider/model format (e.g., openai/gpt-4o) for explicit routing. You can configure fallback chains, load balancing weights, and routing rules through Bifrost's web UI or configuration files.
No. You can run Bifrost alongside LiteLLM during migration and switch traffic gradually. Both gateways can operate simultaneously, allowing you to validate Bifrost performance before fully cutting over.