If your applications are already using multiple AI providers, the next problem is usually not model access; it is controlling that access at production scale. Different teams end up managing different API keys, provider-specific SDKs, rate limits, retries, model fallbacks, and usage reports. An open-source LLM gateway can add a single control layer between your applications and those models, providing engineering teams with a consistent API while centralizing routing, security, reliability, and observability.
In 2026, Bifrost, LiteLLM, Kong AI Gateway, Apache APISIX, and Envoy AI Gateway are five options worth evaluating, but they are built around different infrastructure philosophies. The best choice depends less on the number of supported models and more on your existing stack, traffic requirements, governance needs, and how much AI-specific functionality you actually need.
What Is an LLM Gateway? An LLM gateway is a self-hosted layer that sits between an application and one or more AI model providers.
Instead of building provider-specific logic into every application, the gateway can handle common infrastructure concerns such as:
Model and provider routing, authentication, rate limiting, load balancing, retries and fallbacks, token and usage tracking, logging and observability, provider abstraction, security policies, traffic management.
For example, a company might use one model for customer support, another for coding assistants, a self-hosted model for sensitive workloads, and a lower-cost provider for high-volume classification.
Without a gateway, each application has to understand those providers separately.
With a gateway, applications can communicate with a standardized internal endpoint while infrastructure teams control what happens behind it.
LLM Gateways Compared | LLM Gateway | Best For | What Stands Out | Watch For | |-------------|----------|-----------------|-----------| | Bifrost | High-throughput, mission-critical AI workloads | Go-built for microsecond-level overhead with routing, failover, and cluster-level reliability at scale | Evaluate maturity against your operational requirements | | LiteLLM | Multi-provider LLM access | Strong provider abstraction and unified API | Less focused on traditional API-gateway infrastructure | | Kong AI Gateway | Enterprise API + AI governance | AI traffic management, policies, and observability | Some capabilities depend on Kong's broader platform | | Apache APISIX | Cloud-native API + AI traffic | Extensible plugins and unified API/AI gateway model | Best fit when your team is comfortable operating APISIX | | Envoy AI Gateway | Kubernetes and Envoy environments | Cloud-native networking and AI traffic management | More infrastructure-oriented than a simple LLM proxy |
The key takeaway is simple: Don't choose an LLM gateway based only on model-provider support. Look at where it fits in your existing infrastructure.
Bifrost takes a performance-first approach rather than treating LLM traffic as just another API workload, which is what makes it the fastest enterprise AI gateway among the options here.
It provides a unified OpenAI-compatible interface across multiple providers and includes capabilities such as load balancing, automatic failover, semantic caching, guardrails, and provider management. Its current project documentation also highlights high-throughput performance and low gateway overhead, and its published benchmarks show it processing requests with microsecond-level added latency even at high concurrency.
That makes Bifrost particularly interesting for applications where gateway latency and request volume matter.
For example, consider an enterprise AI platform serving several internal applications. The platform may need to distribute requests across multiple API keys, providers, or self-hosted endpoints while keeping the application-facing interface consistent.
Bifrost supports connections to providers including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Mistral, Ollama, Groq, and others. It can also connect to OpenAI-compatible self-hosted systems, such as vLLM, via a custom base URL.
Where Bifrost makes the most sense Choose Bifrost when your main concern is AI traffic performance, provider routing, reliability, and centralized model access.
Its current repository reports a 5,000-RPS benchmark with low added gateway latency. Still, those figures are project-reported benchmarks, so enterprises should validate performance using their own payload sizes, streaming patterns, network topology, and provider mix before making an architecture decision.
You can review the GitHub repository to inspect the implementation, deployment options, and current project activity.