Best AI Gateways 2026

What is an AI Gateway?

An AI gateway is a headless runtime intermediary that governs traffic between AI applications or agents and downstream model, tool, and context services. The category includes model gateways, which proxy requests to model providers or inference endpoints, and Model Context Protocol (MCP) gateways, which mediate MCP sessions and protocol messages between MCP clients and MCP servers. Common gateway functions include authentication and credential brokering, routing, policy enforcement, rate limiting, and observability.

Also called an LLM gateway, generative AI gateway, or LLM proxy when focused on model traffic, this software is used by developers, AI and platform engineers, security teams, and FinOps teams. Common uses include one approved path to many providers, failover, organization-wide access policy, spend allocation, and governed agent access to MCP tools and resources.

Model Gateways and MCP Gateways

AI gateways can govern two related types of runtime traffic:

• Model gateways - Provider normalization, model routing, failover, optional response caching, token quotas, and prompt or response controls between applications or agents and model providers or inference endpoints.
• MCP gateways - Mediate connections between clients and one or more MCP servers; control which downstream servers are reachable and which exposed tools may be listed or invoked; broker credentials; propagate caller identity; enforce authorization; and audit MCP activity.

Catalogs, SDK helpers, and documentation indexes are not gateways unless they also proxy runtime traffic and enforce policy.

AI Gateways vs. Adjacent Software

API management handles general application programming interface traffic. AI gateways address generative AI needs such as token metering, streaming, multi-provider model APIs, and MCP traffic, including tool and resource requests. Some products extend an existing API gateway with AI controls.

Gateways govern runtime traffic. LLMOps and observability tools primarily trace, evaluate, and release AI apps—though some products span both. Gateways do not normally host model weights (unlike inference/hosting platforms) and differ from in-app AI SDKs.

Multi-Agent Orchestration: AI Gateways provide headless policy enforcement for model traffic or MCP traffic, including tool and resource requests; Multi-Agent Orchestration provides the headed workspace where operators direct a heterogeneous fleet of agents. Those agents may access models and MCP tools or resources through an AI gateway.

AI Gateway Features

• Unified APIs and transformation - Normalizes provider formats for generation, embeddings, multimodal inputs, streaming, and related requests; may expose model aliases.
• Routing and failover - Load balancing, fallbacks, retries, and timeouts by cost, latency, region, or availability. Optional response caching on model gateways can reduce repeated model calls; stateful or side-effecting MCP operations are not normally cached.
• Authentication and credential brokering - Virtual keys and secret isolation so apps and agents do not embed raw provider or downstream MCP credentials.
• Quotas, rate limits, and access policy - Enforces limits by team, application, user, model, provider, server, or tool.
• MCP compatibility and governance - Supports compatible MCP clients, servers, and transports; controls discovery; propagates identity; enforces server- or tool-level authorization; and isolates downstream credentials.
• Prompt, response, and data controls - Optional inspection, redaction, blocking, or external guardrail hooks on governed payloads.
• Observability and correlated logging - Audit logs and metrics; products or gateways that support both traffic types may correlate model calls with MCP activity, including tool and resource requests, on shared traces.
• Deployment options - Managed software-as-a-service, virtual private cloud, self-hosted gateway deployments, or air-gapped environments.

How to Choose AI Gateway Software

• Runtime traffic types required - Model traffic, MCP traffic, or both. Do not assume every listed product supports MCP; verify session mediation and governance, not ordinary provider-native tool calling alone.
• Model and provider coverage - External and self-hosted endpoints in use, plus routing and failover depth.
• MCP clients, servers, and transports - Required compatibility, aggregation needs, and server/tool access controls.
• Identity and secrets - Identity propagation, credential brokering and isolation, and support for bring your own key (BYOK) requirements.
• Policy and compliance - Authorization granularity, audit retention, SOC 2 or equivalent evidence, and data-handling terms.
• Observability - Logging completeness and, where both traffic types apply, correlation across model calls and MCP activity.
• Performance and resilience - Added latency, streaming behavior, throughput, and failure modes if the gateway is unavailable.
• Deployment model - Managed, hybrid, or self-hosted fit for operating needs and residency.

Pricing Information

Gateway platform fees are distinct from model-inference charges. Many products use BYOK so model usage is billed directly to the organization's provider account; others pass through or bundle credits. Hosted gateways commonly meter requests, logged events, retained data, projects, seats, or gateway instances. Open-source gateways avoid license fees but still incur hosting and operating costs; vendors may sell enterprise support separately.

MCP capabilities may be bundled into broader platform tiers or priced separately. Where no separately published pricing unit exists for MCP activity, confirm how logging, retention, and MCP session or message volume affect total cost beyond model traffic.