Open Source Requesty Alternatives
A curated collection of the 2 best open source alternatives to Requesty.
The best open source alternative to Requesty is LiteLLM. If that doesn't suit you, we've compiled a ranked list of other open source Requesty alternatives to help you find a suitable replacement. Other interesting open source alternative to Requesty is Portkey AI Gateway.
Requesty alternatives are mainly AI Development Platforms. Browse these if you want a narrower list of alternatives or looking for a specific functionality of Requesty.
An open source gateway exposing more than a hundred LLM providers behind one OpenAI-compatible interface, for teams centralising model access.
Every provider brings its own SDK, auth pattern, request format and error types, so code that talks to several models fills up with special cases. LiteLLM collapses all of it into one interface in the OpenAI format, covering more than 100 providers.
It works two ways. As a Python SDK you import a completion function and switch models by changing a string, and as a proxy server you run a gateway the whole team points at, using any OpenAI client with a different base address.
The gateway is where the operational features live.
- Virtual keys: issue per-team credentials without handing out the underlying provider keys.
- Spend tracking: attribute cost across keys and models from the admin dashboard.
- Load balancing: spread traffic across deployments and fall back when a provider fails.
- Guardrails: apply policy checks to requests as they pass through the gateway.
- Broad endpoints: chat completions, responses, embeddings, images, audio, batches and rerank.
Beyond model calls it invokes A2A agents from LangGraph, Vertex AI Agent Engine, Azure AI Foundry, Bedrock AgentCore and Pydantic AI. The project reports 8ms P95 latency at 1k requests per second, publishes deploy templates for Render, Railway, AWS and Google Cloud, and counts Netflix among its adopters.
An API gateway that routes requests to hundreds of model providers, with retries, fallbacks, caching and guardrails in front.
The AI Gateway routes requests to more than sixteen hundred language, vision, audio and image models behind one API, so changing provider stops being a code change. It is small, around 122kb, adds under a millisecond of latency, and the project reports more than ten billion tokens passing through it every day.
Running it locally is a single npx command, after which the gateway serves on a local port and a console shows every request that went through. Calls can come from the Portkey client, the OpenAI SDKs, or framework integrations for LangChain, LlamaIndex, Autogen and CrewAI, all using the familiar OpenAI signature.
Behavior is set with configs, small objects attached to a client.
- Reliable routing: fallbacks to another provider or model on chosen errors, up to five automatic retries with exponential backoff, weighted load balancing and granular timeouts.
- Guardrails: more than forty prebuilt checks on inputs and outputs, with room to bring your own or use partner checks.
- Cost management: simple and semantic caching, plus analytics covering request volume, latency, cost and error rates.
- MCP Gateway: one authentication layer, access control and full logging for Model Context Protocol servers across an organization.
Deployment guides cover Docker, Node.js, Cloudflare Workers and Replit as well as the hosted service, while the enterprise build adds organization management, governance and private deployments on AWS, Azure, GCP, OpenShift and Kubernetes. Gateway 2.0, which merges the enterprise core into the open source project, is available as a pre-release branch.
