Best of · AI
Top 8 LLM Gateway and Management Platforms for 2026
As engineering teams integrate multiple large language models (LLMs) from providers like OpenAI, Google, and Anthropic, managing API keys, routing requests, and controlling costs becomes a complex challenge. LLM gateways act as a unified interface, providing crucial features like load balancing, fallbacks, caching, and observability. This list ranks the top platforms based on their feature set, provider support, and ease of integration for production AI applications.
- 1
LiteLLM
An open-source library that provides a unified interface to call over 100 LLM APIs, including models from OpenAI, Azure, Gemini, and Hugging Face. It standardizes inputs and outputs, simplifying model switching and testing.
Why it stands out: Its extensive provider support and simple, universal API format make it the top choice for heterogeneous LLM environments and avoiding vendor lock-in.
- 2
Portkey.ai
A managed AI gateway designed for production applications, offering features like an observability suite, semantic caching, automatic retries, and fallbacks. It provides a robust control plane for managing LLM-powered features.
Why it stands out: It's a powerful managed solution for teams that need enterprise-grade observability and reliability features out-of-the-box without managing infrastructure.
- 3
AWS Bedrock
Amazon's fully managed service that offers access to a range of foundation models from providers like Anthropic, Cohere, and Amazon itself through a single API. It integrates deeply with the AWS ecosystem for security and scaling.
Why it stands out: Choose Bedrock for deep integration with existing AWS infrastructure and access to a curated set of high-performance models under one billing and governance umbrella.
- 4
Azure AI Gateway
Part of Microsoft's Azure AI Studio, this service allows teams to deploy and manage various open-source and proprietary models with a unified endpoint. It includes enterprise-grade security, governance, and content safety features.
Why it stands out: It is the ideal choice for enterprises already invested in the Azure cloud and seeking integrated security, compliance, and governance for their AI applications.
- 5
Google Vertex AI Model Garden
Google's platform for accessing and deploying a vast collection of foundation models, including its own Gemini family. It provides unified endpoints, MLOps tooling, and integration with the broader Google Cloud Platform (GCP).
Why it stands out: Best for teams leveraging Google's powerful Gemini models and the comprehensive data, analytics, and infrastructure tools within GCP.
- 6
Martian
A specialized LLM router that can intelligently route prompts to the best model based on developer-defined criteria like cost, latency, or quality. It acts as a meta-model to optimize performance and spending.
Why it stands out: Pick Martian when you need dynamic, intelligent routing to automatically optimize for performance or cost across a pool of different models.
- 7
OpenLLM
An open-source platform for operating and self-hosting LLMs in production. It allows for fine-grained control over deployment, serving, and scaling of models on your own infrastructure.
Why it stands out: It's designed for teams that want to self-host models for security or cost reasons and require maximum control over their LLM serving infrastructure.
- 8
Vercel AI SDK
While not a full gateway, this open-source SDK provides provider-agnostic helpers and first-class streaming UI support for building AI applications. It simplifies front-end integration with various LLM backends.
Why it stands out: It is the go-to for front-end developers building chat interfaces and other AI-powered UIs, especially when deploying on the Vercel platform.
Frequently asked questions
What is an LLM Gateway?
An LLM Gateway is a proxy server that sits between your application and various LLM APIs. It provides a single, unified endpoint to manage requests, API keys, caching, rate limiting, and routing to different models from providers like OpenAI, Anthropic, or Google. This simplifies development and improves the reliability and cost-effectiveness of AI applications.
Why not just call the LLM APIs directly?
Direct API calls work for simple projects, but in production, you quickly face challenges. You might want to switch models without rewriting code, add a fallback if one provider is down, cache common requests to save money, or track usage and costs across your entire application. An LLM gateway solves these operational problems centrally.
How do I choose between a managed platform and an open-source tool?
Choose a managed platform like Portkey or AWS Bedrock if you want to offload infrastructure management and get advanced features like observability dashboards and enterprise support quickly. Opt for an open-source tool like LiteLLM or OpenLLM if you require maximum customization, want to avoid vendor lock-in, and have the engineering resources to deploy and manage the gateway yourself.