Open source alternative to Portkey
Last verified
Connect any AI SDK to 23+ LLM providers through a unified gateway, with automatic failover, semantic caching, MCP support, and 11µs overhead at 5,000 RPS.
Bifrost gives platform teams a single control plane for all LLM traffic, with virtual keys per team, per-team budgets, and a unified dashboard. Instead of each team managing their own API keys and billing, costs are tracked and governed centrally.
If your organization uses a single team's LLM account with no cross-team billing or governance requirements, the control-plane layer is overhead. A direct SDK call is simpler.
Automatic failover means an agent workflow does not fail when a single provider goes down or hits a rate limit. Load balancing across multiple API keys raises the effective throughput ceiling without rewriting the agent's LLM calls.
If you're running a single-provider proof of concept or a low-volume project where downtime is acceptable, the gateway is unnecessary infrastructure.
Bifrost is a drop-in OpenAI-compatible alternative to LiteLLM's proxy mode. The benchmark data (54x lower P99 latency, 68% less memory at 500 RPS) is directly relevant to teams running LiteLLM at volume and hitting performance or memory issues.
If your existing LiteLLM deployment is stable and you have no performance complaints, migration adds risk with limited benefit.
The built-in MCP gateway centralizes tool connections for AI agents. Instead of each agent maintaining its own MCP client configuration, tools are registered once in Bifrost and available to all models passing through the gateway.
If you're building a single-agent app with a few tools and no multi-agent coordination, configuring Bifrost adds infrastructure with little payoff compared to a direct MCP client.
Building production AI systems on a single LLM provider is fragile. Provider outages, rate limits, and sudden pricing changes leave teams with no fallback, and adding provider redundancy manually means writing bespoke routing logic per provider API. That code becomes a maintenance burden as providers update their SDKs.
Governance adds a second layer of work. When multiple teams or agents share API keys, costs accumulate invisibly until the bill arrives. Setting per-team budgets and audit logs requires custom instrumentation that most teams build slowly and inconsistently, if at all. Adding Model Context Protocol tool access across a fleet of agents adds a third coordination problem with no standard answer in the provider SDKs.
Routes requests across 23+ providers (OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, and more) through a single OpenAI-compatible endpoint. Switching models or providers requires changing only the model string in the request body, not any SDK configuration.
Automatically reroutes to a backup provider when one goes down, with intelligent load distribution across multiple API keys for the same provider. In benchmarks, the gateway sustained 5,000 RPS with a 100% success rate on a t3.xlarge instance.
Caches LLM responses based on semantic similarity rather than exact string matches. Functionally equivalent questions return the cached answer, cutting redundant provider calls and reducing costs across a team's shared AI requests without changes to application code.
Acts as a centralized Model Context Protocol gateway, giving AI models access to external tools (filesystems, web search, databases) with centralized policy enforcement. All MCP tool connections route through one managed layer rather than being configured per agent.
Issues separate virtual API keys per team, project, or use case, each with independent spending limits and access controls. Usage costs are tracked at key granularity, making it possible to attribute costs to specific teams or stop a runaway model before it hits the monthly limit.
Includes native OpenTelemetry support and a built-in dashboard for monitoring request volumes, latency, and error rates without additional setup. Prometheus metrics are available for teams already running a monitoring stack.
Both Bifrost and Portkey sit between your application and LLM providers, handling routing, key management, and observability. The core decision is whether to run that layer yourself or pay Portkey to operate it.
| Feature | Bifrost | Portkey |
|---|---|---|
| License | Apache-2.0 | Proprietary |
| Self-hosting | Yes | Limited (enterprise only) |
| Pricing | Free (OSS core) | Usage-based |
| Providers | 23+ (OSS) | 250+ (managed) |
| Data residency | Full in-VPC control | Routes through Portkey |
| MCP gateway | Yes | Available |
| Performance overhead | 11µs at 5,000 RPS | Not published |
Bifrost is the stronger choice when data residency is a requirement. A full in-VPC deployment routes no traffic through external servers, which matters for healthcare, financial, and government workloads operating under data sovereignty rules. Apache-2.0 licensing also eliminates per-request costs: you pay only for the infrastructure you already run.
Portkey is the better choice when you want zero infrastructure to manage. Its managed service connects to 250+ providers out of the box, includes a polished UI for prompt management and distributed tracing, and requires no operational capacity for a Go service. If your team has no bandwidth for self-hosted infrastructure or needs Portkey's integrations without configuration work, the managed tier is the faster path.
Performance at scale favors Bifrost. Published benchmarks show 11µs of overhead per request at 5,000 RPS with no failed requests; Portkey does not publish equivalent latency numbers for its managed service. For latency-sensitive production workloads, the self-hosted Bifrost deployment gives deterministic performance a third-party managed service cannot guarantee.
Deploy Bifrost as a local server using Docker or NPX.
```bash
npx -y @maximhq/bifrost
docker run -p 8080:8080 maximhq/bifrost
docker run -p 8080:8080 -v $(pwd)/data:/app/data maximhq/bifrost
```Yes. The open-source version is Apache-2.0 licensed and free to self-host. Enterprise features (clustering, adaptive load balancing, guardrails, and advanced MCP gateway options) require a commercial license from Maxim; contact them for pricing.
Bifrost covers the same core use case as LiteLLM's proxy: a self-hosted gateway that provides a unified OpenAI-compatible API across multiple LLM providers. Benchmark comparisons at 500 RPS show 54x lower P99 latency and 68% less memory usage. Migration requires changing one base_url line in your existing SDK.
Not in a self-hosted deployment. Bifrost can be deployed entirely within your own VPC; no request data passes through Maxim or Bifrost servers. The built-in telemetry and observability data stay within your own monitoring stack.
The open-source version supports 23+ providers, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cerebras, Cohere, Mistral, Ollama, and Groq. The repo description references support for 10,000+ models across these providers.
The open-source tier includes multi-provider routing, load balancing, failover, semantic caching, virtual keys, budget management, and OpenTelemetry observability. Enterprise adds clustering for multi-node deployments, adaptive load balancing, guardrails for content policy enforcement, and advanced MCP gateway features.
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