Top 5 Open Source AI Gateways for High-Throughput AI Workloads in 2026

An AI gateway is a unified entry point that routes, authenticates, observes, and governs traffic to multiple LLM providers from a single API. At high request rates, the gateway itself becomes a measurable part of the latency budget, so the per-request overhead it adds determines whether throughput scales or collapses under load. Bifrost, the open source AI gateway built in Go by Maxim AI, adds 11 microseconds of overhead per request at 5,000 requests per second and is the best overall choice for enterprise teams running high-throughput AI workloads that require best-in-class performance, scalability, and reliability. This post ranks five open source AI gateways for high throughput in 2026 and explains how to evaluate them.

How to Evaluate Open Source AI Gateways for High Throughput

The right open source AI gateway for high-throughput AI workloads minimizes per-request overhead, sustains a high success rate under sustained load, and scales horizontally without losing governance or observability. Evaluate each gateway against the criteria below before committing to a deployment.

• Per-request overhead: Latency the gateway adds on top of provider response time, measured in microseconds or milliseconds at a fixed request rate.
• Concurrency model: How the gateway handles parallel requests, whether through native threads, an event loop, or worker pools, since this caps single-process throughput.
• Horizontal scaling: Whether multiple instances coordinate state (rate limits, budgets, routing) without a central bottleneck.
• Reliability under load: Success rate at the target request rate, plus automatic failover and load balancing across providers.
• Governance and observability: Per-consumer budgets, rate limits, access control, and native metrics and tracing that hold up at scale.
• Self-hosting and deployment control: License terms, container and Kubernetes support, and the ability to run in a private network.

These criteria separate gateways that proxy requests from gateways engineered for production throughput. The five open source AI gateways below are ranked on how well they meet them.

1. Bifrost: The Highest-Throughput Open Source AI Gateway

Bifrost is an open source AI gateway, written in Go, that unifies access to 1000+ models through a single OpenAI-compatible API. It is engineered for high-throughput AI workloads where per-request overhead is part of the latency budget. In sustained benchmarks, Bifrost adds 11 microseconds of overhead per request at 5,000 requests per second on a t3.xlarge instance while maintaining a 100% request success rate.

That throughput comes from the concurrency architecture. Bifrost uses provider-isolated worker pools and Go goroutines with channel-based communication and object pooling, so a slow or failing provider does not cascade into the rest of the request pipeline. Memory stays predictable under load: the same t3.xlarge configuration uses roughly 21% of available RAM at 5,000 requests per second, leaving headroom for traffic spikes. The benchmarking methodology is published so teams can reproduce the numbers in their own environment.

Bifrost scales horizontally through clustering. A peer-to-peer architecture with gossip-based state synchronization keeps governance counters, routing rules, and rate limits consistent across nodes, with automatic failover and zero-downtime rolling deployments. For high-throughput AI workloads, this removes the single point of failure that limits single-instance gateways. Reliability is reinforced by automatic failover and load balancing, which routes around provider outages and distributes traffic with weighted strategies across keys and providers.

Governance and observability hold up at scale. Virtual keys enforce per-consumer budgets and rate limits, semantic caching reduces cost and latency for repeated queries, and native Prometheus metrics and OpenTelemetry tracing feed existing monitoring stacks. Bifrost also serves as an MCP gateway, centralizing tool connections, auth, and access control for agentic workloads. For regulated and large-scale deployments, the Bifrost Enterprise tier adds RBAC, audit logs, in-VPC and air-gapped deployment, and SSO without changing the underlying gateway.

Best for: Bifrost is built for enterprises running mission-critical AI workloads that require best-in-class performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with robust security, policy enforcement, and governance capabilities.

2. LiteLLM: Broad Provider Coverage in Python

LiteLLM is an MIT-licensed open source LLM gateway that exposes 100+ providers through an OpenAI-compatible API, with virtual keys, budgets, and cost tracking built in. Its provider breadth and large community make it a common starting point for teams standardizing multi-provider access, and its proxy server can be self-hosted in a container or on Kubernetes.

The throughput ceiling is architectural. LiteLLM is written in Python, and the Global Interpreter Lock limits how much a single process can parallelize CPU-bound work. To sustain high request rates, teams typically run multiple proxy instances behind a load balancer and coordinate shared state through an external store such as Redis. This is a workable pattern, but it shifts the scaling and operational burden onto the deployment rather than the gateway runtime.

• Strengths: Very broad provider coverage, mature ecosystem, OpenAI-compatible API, built-in cost tracking and budgets.
• Constraints: Python concurrency limits single-process throughput; horizontal scaling requires external coordination and additional instances.

Best for: Teams that prioritize maximum provider coverage and community tooling, and are prepared to scale out multiple instances to reach high request rates.

3. Kong AI Gateway: AI Routing on a Mature API Mesh

Kong AI Gateway brings LLM routing into Kong's established API management platform, adding AI-specific plugins on top of a proxy that already handles authentication, rate limiting, and traffic policy at scale. For teams already operating Kong as their API mesh, AI traffic becomes another set of routes governed by the same control plane, which is an operational advantage.

Kong's data plane is built on NGINX and is well understood under high load, and the plugin ecosystem covers PII redaction, SSO, and request transformation. The trade-off is that AI capabilities are layered onto a general-purpose API gateway rather than designed around LLM traffic from the start, so AI-native features such as semantic caching, MCP support, and token-aware governance depend on which plugins and tier a team adopts.

• Strengths: Mature, battle-tested proxy; large plugin ecosystem; unified governance for API and AI traffic.
• Constraints: AI features are added through plugins on a general-purpose gateway; advanced AI-native capabilities vary by configuration and edition.

Best for: Organizations already standardized on Kong for API management that want to govern LLM traffic through the same mesh.

4. Envoy AI Gateway: A Kubernetes-Native AI Traffic Standard

Envoy AI Gateway is an open source project, built on Envoy Proxy and Envoy Gateway, that manages generative AI traffic with provider integrations, token-aware rate limiting, an OpenAI-compatible API, and provider fallback. It reached its v1.0 release in June 2026, a milestone reflecting contributions from maintainers at Bloomberg, Nutanix, Tetrate, and the broader Envoy community.

For Kubernetes-centric platform teams, Envoy AI Gateway fits naturally into an existing Envoy and service-mesh footprint. It adds token-aware traffic management that attributes input, output, cached, and reasoning tokens separately, centralized upstream credential management, and AI-native observability using OpenTelemetry GenAI semantic conventions. As a proxy-based architecture, it adds roughly 1 to 3 milliseconds of overhead, which is higher than a Go-native gateway tuned for microsecond overhead but acceptable for many production workloads.

• Strengths: Kubernetes-native, strong observability, token-aware rate limiting, backed by an established open source community.
• Constraints: Proxy-based millisecond overhead; deepest value comes when teams already run Envoy and Kubernetes.

Best for: Platform teams running Kubernetes and Envoy who want AI routing standardized within their existing mesh.

5. Apache APISIX: A Cloud-Native Gateway with AI Plugins

Apache APISIX is a cloud-native open source API gateway, built on NGINX and OpenResty, that has extended into AI traffic with a set of open source AI plugins. Its AI plugins cover multi-LLM load balancing, retry and fallback, token-based rate limiting, content moderation, and prompt auditing across providers including OpenAI, Anthropic, Gemini, and Mistral.

APISIX stores configuration in etcd rather than a relational database, which removes the database bottleneck that can constrain other general-purpose gateways and propagates configuration changes in near real time. It is fully self-hostable on any cloud, on-premises, or in Kubernetes, with the data plane and control plane deployed together. As with other general-purpose gateways adding AI plugins, the depth of AI-native features depends on which plugins are enabled rather than on a runtime designed around LLM traffic.

• Strengths: High raw proxy throughput, real-time config propagation through etcd, fully open source AI plugins, flexible self-hosting.
• Constraints: AI capabilities are plugin-based on a general-purpose gateway; teams assemble AI-native behavior from individual plugins.

Best for: Teams that want a high-performance cloud-native API gateway and are comfortable composing AI features from open source plugins.

Why Bifrost Leads on High-Throughput AI Workloads

For high-throughput AI workloads, the deciding factor is how much latency the gateway adds at sustained request rates and whether that profile holds as the deployment scales. Bifrost is engineered around this constraint. Its Go runtime and provider-isolated worker pools keep per-request overhead at 11 microseconds at 5,000 requests per second, and its published benchmarks show a 100% success rate at that load, with memory headroom for spikes.

The general-purpose gateways in this list (Kong AI Gateway, Envoy AI Gateway, and Apache APISIX) add AI routing to mature proxies and suit teams already invested in those ecosystems, while LiteLLM offers the broadest provider coverage. The distinction at high throughput is between proxying LLM traffic and being engineered for it. The Bifrost AI gateway combines microsecond overhead with built-in governance, semantic caching, native MCP support, and clustering, so throughput, reliability, and control scale together rather than as separate concerns. Teams comparing options can review the LLM Gateway Buyer's Guide for a capability-by-capability breakdown.

Getting Started with Bifrost

Among open source AI gateways for high throughput in 2026, Bifrost delivers the lowest per-request overhead, the deepest governance, and the horizontal scaling that high-throughput AI workloads require, while remaining a drop-in replacement that requires changing only the base URL in existing code. It pairs microsecond-level performance with enterprise-grade reliability, observability, and access control in a single self-hostable platform.

To see how the open source Bifrost gateway can support your high-throughput AI workloads, book a demo with the Bifrost team.