Open-source Portkey alternatives

LiteLLM is an AI gateway, open source, offering one unified interface for calling more than 100 LLM providers, including OpenAI, Anthropic, Gemini, Bedrock and Azure, using the OpenAI request format. It can be used as a Python SDK inside an application or deployed as a proxy server that serves a whole team or organization. The core is Python with a Rust component, and GitHub lists the license as 'Other'.

The project aims to remove the hassle of handling a separate SDK, login scheme, request format and set of errors for each model. On top of that the gateway provides load balancing, guardrails, spend tracking, virtual keys and an admin dashboard, and it exposes endpoints for chat completions, responses, embeddings, images, audio, batches and reranking.

Because requests keep the OpenAI format, teams can swap providers without rewriting application code. It is self-hosted, with hosted-proxy and enterprise tier options linked in the README, and its topics also reference an MCP gateway and LLMOps. It suits platform and AI engineering teams that need central control over model access, budgets and logging.

New API is an AI gateway you host yourself, built for apps, agents and teams. It lets you plug in upstream model services, present one consistent API to clients, and handle routing, access, usage and cost management centrally. It is written in Go and released under the AGPL-3.0 license.

It can convert between formats so that various LLMs are reachable through OpenAI-compatible, Claude-compatible or Gemini-compatible interfaces. Supported upstreams range from OpenAI, Anthropic and Gemini to Azure OpenAI, AWS Bedrock, Vertex AI, DeepSeek and Qwen, plus other compatible services, and a web console is used for administration.

Typical uses are sharing authorized model access across a team, switching providers without reconfiguring every client, and operating a private multi-model service. The README stresses lawful, authorized use: users must obtain upstream keys legitimately, follow upstream terms, and meet any regulatory obligations if they offer AI services to the public. It suits teams and organizations that need centralized model management.

Helicone is an open-source platform for AI engineers that combines LLM observability with an AI gateway. It records the requests your application sends to model providers so you can trace agents, chatbots and document-processing pipelines, debug sessions, and see how much each call costs and how long it takes.

Integration is meant to be quick: a single line of code can log requests made through OpenAI, Anthropic, LangChain, Gemini, the Vercel AI SDK and similar tools. The gateway side offers access to many models through the OpenAI API format, with intelligent routing and automatic fallbacks. Other features listed in the README and repository topics include cost and latency tracking, datasets and fine-tuning support, prompt management, evaluation and a playground.

Helicone is licensed under Apache-2.0 and written in TypeScript. It can be self-hosted, and the vendor also runs a hosted cloud version. It suits teams that want visibility into LLM spend and quality without building their own logging pipeline.

One API is an LLM API management and key redistribution system that lets you reach many large language models through the standard OpenAI API format. It is distributed as a single executable and a Docker image, and the repository description is written in both Chinese and English with an English interface available.

It supports a long list of providers, including OpenAI and Azure OpenAI, Anthropic Claude, Google Gemini, Mistral, DeepSeek, Groq, Ollama, Cohere, xAI and many Chinese model services. Administrators can load balance across several channels, use stream mode, deploy on multiple machines, manage tokens with expiry, quotas, IP ranges and allowed models, generate redemption codes, group users and channels with different rates, retry failed requests and customize branding. A management API allows extension without code changes.

One API is licensed under MIT. The README warns that users must follow OpenAI's terms of use and applicable laws, and must not offer unregistered generative AI services to the public in China. It also instructs administrators to change the default root password after the first login.

Gateway is Portkey's open-source AI gateway, a lightweight service for routing requests to a large catalog of language, vision, audio and image models through one API. It is designed to be fast, reliable and enterprise-ready, and the project says you can integrate with any language model quickly. It also bundles AI guardrails and, per its topics, supports MCP gateway use. It is written in TypeScript under the MIT license.

A quickstart runs the gateway locally, serving the API on port 8787 along with a console. Deployment guides cover Portkey Cloud, Docker, Node.js, Cloudflare, Replit and other targets. Libraries and SDKs listed include JavaScript, Python, REST, the OpenAI SDKs, LangChain and LlamaIndex, so existing applications can point at the gateway with small changes.

The README announces a Gateway 2.0 pre-release in which Portkey's core enterprise gateway merges into open source, and it mentions an open-source model pricing dataset called Portkey Models. A hosted gateway and enterprise offering exist from the vendor, so the project suits both teams who want to self-host routing and those who prefer a managed service.

LLM Gateway is an open-source API gateway for large language models. It sits between your applications and multiple providers such as OpenAI, Anthropic and Google Vertex AI, so your code talks to one interface while the gateway decides where each request goes. Its API follows the OpenAI format, which keeps migration from existing code straightforward.

Beyond routing, it centralizes the management of provider API keys and tracks requests, token usage, response times and costs so teams can see what their LLM usage is doing. Performance monitoring lets you compare models on speed and cost-effectiveness. Topics on the repository also mention guardrails, rate limiting, observability and enterprise use.

You can use LLM Gateway in two ways: a hosted version at llmgateway.io where you create an account and get an API key, or a self-hosted deployment on your own infrastructure using a unified Docker image with PostgreSQL. The code is written in TypeScript and its licence is listed as other, so check the repository terms. It suits developers and platform teams who juggle several model vendors.