This gateway suits teams managing access to multiple AI models and providers.
- Home
- Developer Tools
- LLM Gateway Software
Best LLM Gateway Software 2026
A gateway can sit between applications and language-model providers, so it is worth comparing both its operational controls and its deployment options. Compared on provider count, self-hosted deployment, fallback routing, budget controls, usage analytics, and virtual API keys, the entries surface different considerations for managing access and usage. Free-plan availability and paid-from pricing add cost context. Kong Gateway, Manifest, and OpenRouter are among the options shown, alongside Requesty and TrueFoundry AI Gateway. As you evaluate them, consider whether provider choice, routing behavior, or usage visibility matters most to your workflow, and whether self-hosting is relevant. The listed fields give you a grounded way to compare control and cost.
Bifrost
The OSS edition suits developers and small teams seeking a self-managed gateway.
WebWindowsMac+3Manifest
Manifest is aimed at AI builders, teams and startups, as well as platforms or providers managing inference consumption.
WebLinuxAPI+1WSO2 AI Gateway
WebWindowsMac+3LangSmith
LangSmith suits teams developing AI agents that need evaluation, prompt tools, tracing, and production monitoring.
WebWindowsMac+3LLM Gateway suits product and platform teams routing model requests across providers.
It suits teams building or operating AI applications that need provider routing, request tracing, or cost visibility.
OpenRouter suits developers and teams that want one API endpoint for access to multiple AI models and providers.
Routerly is aimed at developers, including SaaS teams and local-first development use cases.
Vercel suits teams deploying web applications from Git repositories and working with its listed frameworks.
Requesty suits developers and teams that want to connect applications to multiple models through one API and manage routing, usage and governance centrally.
It suits teams that share models or tools and want central credentials, quotas, usage attribution or provider failover.
SGLang suits technical teams serving language, vision-language, or diffusion models, including teams working on agentic workloads or reinforcement-learning rollouts.
TensorZero may suit developers who want a self-hosted LLM gateway and can manage its deployment and data storage.
LiteLLM suits platform teams that need a shared API for multiple model providers and controls for usage, routing, and spend.