Top 5 Enterprise AI Gateways

Most companies did not plan their AI infrastructure. It grew as usage across teams increased. Someone wired up the OpenAI API for a prototype, another added Anthropic because it was better at a specific task, and six months later there are a dozen applications, each with its own API keys, its own retry logic, and no shared view of who is spending what.

An enterprise AI gateway is the thing that stops sprawl from becoming a liability: one layer, in front of every model call, that knows who is calling, what they are allowed to do, and what it costs.

This list is not about which gateway has the most features. It is about which ones actually hold up once security, platform, and finance teams are all asking it questions at the same time.

TL;DR

• Bifrost is the enterprise AI gateway to use if you want enterprise controls (identity, budgets, guardrails, audit logs) without giving up the open-source core. It is written in Go, licensed Apache 2.0, and adds about 11 microseconds of overhead at 5,000 requests per second, the kind of number that matters once a gateway sits in front of every request your company makes. It also ships Bifrost Edge, an agent that extends the same governance to AI traffic on employee laptops, which none of the other four gateways in this list currently offer.

• Portkey has the broadest adoption story in the open-source gateway space, a large community, and a deep enterprise tier. It is a reasonable pick for teams that want a mature product with a long feature list.

• Tyk AI Gateway is the choice for teams that are already running Tyk, or any API gateway, for everything else, and want LLM traffic to live under the same policy engine instead of a separate system.

• F5 AI Gateway fits organizations that already run F5 for application delivery and security, and want AI traffic folded into that same console rather than standing up a new one.

• Cloudflare AI Gateway is the natural fit for teams already running on Cloudflare, with caching, fallback routing, and usage analytics built into infrastructure they are already using.

What enterprises are actually looking for in an AI gateway

The term "AI gateway" covers a wide range of products now, from simple request routers to full governance platforms. Before you compare any of the five below, it helps to know what questions each team in your organization is going to ask once the gateway is actually in production.

• Security wants identity on every request. Not an API key shared across a team, but a way to know which application, which service account, or which person sent a given request, and to revoke that access without redeploying anything.

• Platform wants the gateway to not be the thing that goes down. That means load balancing across providers and keys, automatic failover when a provider has an outage, and low enough overhead that adding a gateway does not show up as a latency regression in a dashboard somewhere.

• Finance wants to know what a model is costing, broken down by team, by project, or by application, before the invoice arrives rather than after. Budgets that can be set in advance, not just logs you can grep through at the end of the month.

• Compliance wants an audit trail that holds up. Every request, every response, every policy decision, recorded somewhere that cannot be quietly edited.

• Everyone wants deployment flexibility. A gateway that only runs as someone else's SaaS is a hard sell for a company with data residency requirements, a regulated industry, or an internal policy against sending production traffic through a third party's infrastructure. The ability to self-host, including fully air-gapped, is what separates a gateway you can put in front of all traffic from one you can only use for a side project.

Here is how the five stack up at a glance, and where each one actually sits in your request path.

A gateway is the one place security, platform, and finance all get to ask their questions, on every request, regardless of which team sent it or which model answered it.

Comparison across the AI Gateways

Gateway	Core	License	Self-hosted / air-gapped	Best for
Bifrost	Go	Apache 2.0 (enterprise tier)	Yes, including air-gapped	Teams that want full governance without losing the open-source core
Portkey	Open source	MIT (enterprise tier)	Yes, including hybrid and air-gapped	Teams that want a mature, widely adopted gateway with a long feature list
Tyk AI Gateway	Go	Open source (enterprise tier)	Yes, multi-cloud and on-prem	Teams already standardized on Tyk or another API gateway
F5 AI Gateway	Part of F5's platform	Commercial	SaaS and hybrid now; air-gapped planned	Teams already running F5 for application delivery and security
Cloudflare AI Gateway	Cloudflare's global network	Commercial	SaaS only	Teams already running on Cloudflare

1. Bifrost

Bifrost is an open-source AI gateway built in Go, with an Apache 2.0 core and a paid enterprise tier for identity, high availability, and support. It gives you one OpenAI-compatible interface to over 10,000 models across more than 25 providers, including OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Gemini, Groq, Mistral, Cohere, and xAI, plus self-hosted options like vLLM, SGLang, and Ollama. Switching providers is a base URL change, not a rewrite.

What makes Bifrost an enterprise gateway rather than just a fast router is how identity and governance are built in rather than bolted on.

Every caller gets a Virtual Key, and that key is where budgets, rate limits, and model permissions actually live, scoped by team, customer, or individual model, and enforced hierarchically so a sub-team's budget cannot quietly exceed its parent's. A key that runs out returns a clear 402 rather than silently failing or, worse, silently succeeding on someone else's bill.

Bifrost also treats agent tooling as a first-class part of the gateway rather than a separate system. Its MCP Gateway sits in front of tool calls over STDIO, HTTP, and SSE, the same way the rest of Bifrost sits in front of model calls, with filtering at the client, request, and virtual-key level so an agent only ever sees the tools it is allowed to use.

Its Code Mode feature has the model write a short script to orchestrate tools instead of loading every tool definition into context, and in a case study Maxim published, a 508-tool run across 16 MCP servers dropped from $377 to $29, a cut of about 92 percent, while keeping a 100 percent task pass rate. For any team running agents in production, that is the difference between a tool-calling bill that scales with usage and one that does not.

On the operations side: adaptive load balancing with circuit breakers that recalculate provider weights from live latency and error rates, automatic failover configured as a fallbacks array in the request payload rather than a separate piece of infrastructure, semantic caching backed by Redis, Qdrant, Weaviate, or Pinecone, and complexity-based routing that can send a simple query to a cheap model and a harder one to a stronger model automatically.

For multi-region deployments, Bifrost's cluster mode runs as peer-to-peer nodes with no single leader, sharing traffic patterns, rate-limit state, and provider health over a gossip protocol, so there is no coordinator node to become a new single point of failure.

On the compliance side: fine-grained RBAC with custom roles, SSO and SCIM 2.0 support for Okta, Microsoft Entra, Keycloak, Zitadel, Auth0, Google Workspace, and any generic OIDC provider, audit logs that are signed and tamper-resistant with export to S3 or GCS, and in-VPC or fully air-gapped deployment for teams that cannot send traffic, prompts, or telemetry anywhere outside their own network.

Guardrails come built in for content safety, PII, and secrets detection, and extend out to Presidio, Azure AI Language, AWS Bedrock Guardrails, Azure Content Safety, Google Model Armor, CrowdStrike AIDR, Check Point, Lakera Guard, and several others. Secrets themselves can live in AWS Secrets Manager, GCP Secret Manager, or HashiCorp Vault rather than sitting in Bifrost's own database, and budget or rate-limit breaches can alert Slack, Microsoft Teams, or PagerDuty. SOC 2 Type II, ISO 27001, HIPAA, and GDPR are all covered.

One piece that is distinct from every other gateway on this list: Bifrost Edge, an agent that runs on employee laptops and routes AI traffic from desktop apps, browser AI, and coding agents through the same Gateway, so the governance does not stop at the server boundary. It is an add-on to the enterprise tier, priced per endpoint, but it addresses a real gap, the AI usage that never touches your backend at all.

Key capabilities

• Unified OpenAI-compatible interface to 10,000+ models across 25+ providers, plus self-hosted vLLM, SGLang, and Ollama.
• Virtual Keys for identity, with hierarchical budgets, rate limits, and model scoping.
• MCP Gateway with tool filtering and Code Mode for agent tool calls.
• Adaptive load balancing with circuit breakers, automatic failover, and complexity-based routing
• Peer-to-peer cluster mode for multi-region high availability, with no single coordinator. . Semantic caching and reasoning budget normalization across providers.
• RBAC, SSO and SCIM 2.0 (Okta, Entra, Keycloak, Zitadel, Auth0, Google Workspace, generic OIDC).
• Signed, tamper-resistant audit logs, exportable to S3 or GCS Bifrost Edge for governing AI traffic on employee devices, not just the backend.
• In-VPC and fully air-gapped deployment, SOC 2 Type II, ISO 27001, HIPAA, GDPR.

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. Portkey

Portkey is an open-source gateway , with its core project sitting at close to 12,000 stars on GitHub. It started as a lightweight unified API across model providers and has large enterprise feature set: SSO through SAML 2.0, Okta, and Azure AD, role-based access control, PII anonymization, and certifications including SOC 2, ISO 27001, GDPR, and HIPAA.

Deployment is flexible across SaaS, hybrid, and fully air-gapped tiers, and the product line now includes its own MCP Gateway and an Agent Gateway aimed at the same agent-tooling problem Bifrost's MCP layer addresses.

Portkey's recent "Gateway 2.0" release pushed more enterprise-grade functionality back into the open-source core rather than keeping it exclusive to the paid tier, which is a meaningful shift for teams that want to start on the free version and grow into it.

Key capabilities

• Unified API across a wide range of model providers, MIT-licensed core.
• SSO (SAML 2.0, Okta, Azure AD), RBAC, PII anonymization.
• SOC 2, ISO 27001, GDPR, and HIPAA certifications.
• MCP Gateway and Agent Gateway for agent tool traffic.
• Semantic caching.
• SaaS, hybrid, and air-gapped deployment tiers.

Best for: teams that want a mature, broadly adopted gateway with a long enterprise feature list and an active community.

3. Tyk AI Gateway

Tyk is an API and AI gateway paired with Tyk AI Studio. It works with the same policy engine, access control, and multi-cloud deployment model that Tyk customers already use for REST, GraphQL, and gRPC APIs, extended to cover model providers too.

A platform team that already runs Tyk for its API layer does not want a second, unrelated system just for LLM calls, with its own policy language and its own way of defining who can call what. Tyk's AI Gateway folds model traffic into the same Kubernetes-native deployment and policy engine, which is a real simplification for teams in that position. Enterprise users referenced in Tyk's own case studies include organizations like Zeiss, Domino's, and Deloitte, which gives some sense of the scale it is built to run at.

Tyk supports multi-cloud, hybrid, and fully on-premises deployment, and because it grew out of a general-purpose API gateway, it handles a wider range of protocols than most AI-specific gateways, which matters if your LLM traffic is only one part of a broader API surface you need to govern consistently.

Key capabilities

• Unified policy engine shared with Tyk's broader API gateway product
• Kubernetes-native, with multi-cloud, hybrid, and on-premises deployment.
• REST, GraphQL, gRPC, and async protocol support alongside LLM traffic.
• Tyk AI Studio for building and managing AI-specific policies.
• Enterprise customers at meaningful scale (Zeiss, Domino's, Deloitte referenced in Tyk's own materials).

Best for: teams already running Tyk, or evaluating a general-purpose API gateway, that want LLM traffic governed under the same system rather than a separate one.

4. F5 AI Gateway

F5 is best known for application delivery and security, the layer that sits in front of a company's web and API traffic generally. Its AI Gateway extends that same platform, now branded as F5's Application Delivery and Security Platform, to cover LLM traffic, combining a Model Gateway, an MCP Gateway, and AI-specific guardrails into one console.

The guardrails are where F5's existing security background shows up directly: detection for prompt injection and sensitive data leaks, built on the same inspection capabilities F5 already uses for web application traffic. On cost, F5 claims up to 60 percent reduction in token spend through a combination of routing, model tiering, and semantic caching, though as with any vendor-reported figure, it is worth asking what workload that number was measured against before treating it as a baseline for your own.

Key capabilities

• Model Gateway, MCP Gateway, and AI Guardrails unified in one console.
• Prompt injection and sensitive data leak detection, built on F5's existing security stack.
• Claimed up to 60 percent token spend reduction via routing, tiering, and semantic caching.
• SaaS, hybrid SaaS, and hybrid multicloud deployment

Best for: organizations already running F5 for application delivery and security that want AI traffic governed in the same console rather than a new one.

5. Cloudflare AI Gateway

Cloudflare AI Gateway takes the same approach Cloudflare takes to everything else: put it on their global network and let teams configure it through a dashboard or API rather than infrastructure they have to run themselves. It sits in front of model providers as a control plane, with request caching to cut down on repeated calls, fallback routing so a request can move to a backup provider automatically, and unified billing that rolls every provider's cost into one bill instead of several.

The appeal here is proximity to infrastructure a team already has. Routing rules, including which provider handles a request based on latency, cost, or availability, can be changed from the dashboard without a redeploy, and logs come with token counts, cost, duration, and request status attached, so usage analytics are available without wiring up a separate observability stack. Built-in guardrails aim to catch sensitive data before it reaches a provider, without additional setup.

What it does not offer is a self-hosted or air-gapped deployment option. Cloudflare AI Gateway runs on Cloudflare's infrastructure, not yours, which is a real constraint for teams with strict data residency requirements, but less of one for teams already comfortable running production traffic through Cloudflare.

Key capabilities

• Request caching with configurable TTL to cut redundant provider calls.
• Fallback routing by latency, cost, or availability.
• Unified billing across every connected provider.
• Logs and usage analytics with token counts, cost, and duration per request.
• Built-in guardrails against sensitive data leakage, no extra setup required.

Best for: teams already running on Cloudflare who want an AI gateway that lives in the same dashboard as the rest of their infrastructure.

Which one is right for you

If you are choosing based on what your team already runs, the decision mostly If you are choosing based on what your team already runs, the decision mostly makes itself: Tyk if you already run Tyk, F5 if you already run F5, Cloudflare if your traffic already sits behind Cloudflare.

Those three gateways earn their place by not asking you to operate a second, unrelated system.

If you are choosing without that constraint, the real question is what you are optimizing for. Portkey gives you the most mature, most broadly adopted option, with a long feature list and a large community behind it, though its pending acquisition is worth a direct conversation with the vendor.

Bifrost gives you that same depth of governance (identity, budgets, guardrails, audit), fully self-hostable including air-gapped, without the lock-in that comes from a product tied to one company's broader platform. For a team that wants to own its enterprise AI gateway the way it owns the rest of its stack, rather than renting a slice of someone else's, that is the harder thing to find, and it is what Bifrost is built around.

Frequently asked questions

What is an AI gateway, and how is it different from a model API?
A model API is how you send a request to one provider. An AI gateway sits in front of all of your model providers, handling identity, routing, budgets, and governance for every request, regardless of which model is actually serving it. You call the gateway once and it decides where the request actually goes.

Do I need an AI gateway if I only use one model provider?
Even with a single provider, a gateway gives you identity on every request, rate limits and budgets per team or application, and an audit trail, none of which a provider's own API gives you by default. It also means you are not rewriting application code the day you decide to add a second provider or a fallback.

What does "air-gapped deployment" actually mean for an AI gateway?
It means the gateway can run entirely inside your own network, with no outbound dependency on the vendor's infrastructure to function. For regulated industries or anyone with strict data residency requirements, this is often a hard requirement rather than a preference, which is why it is worth checking whether a vendor offers it today versus on a roadmap.

How much latency does an AI gateway add?
It varies by implementation, but a well-built gateway should add a negligible amount. Bifrost, for example, is benchmarked at about 11 microseconds of overhead at 5,000 requests per second, in Maxim's own published testing. At that level, the gateway is not the thing slowing down your application; the model call itself is. Whatever gateway you evaluate, ask for the benchmark methodology, not just the headline number.

Can an AI gateway reduce my model costs?
Yes, through a few mechanisms: semantic caching avoids paying for a model call when a near-identical one was already answered, complexity-based routing sends simpler queries to cheaper models automatically, and having visibility into spend per team or application in the first place tends to surface waste that was previously invisible.

Do AI gateways work with agents and MCP tools, or just chat completions?
The better ones do. Agent tool calls are a growing share of LLM traffic, and treating them as ungoverned is the same mistake as treating early chat completions as ungoverned. Bifrost's MCP Gateway, Portkey's Agent Gateway, and F5's MCP Gateway are all explicit answers to that, filtering which tools an agent can reach and keeping tool traffic inside the same identity and audit model as everything else.

Next steps

Bifrost is open source and self-hostable, with a quickstart that gets an enterprise AI gateway running in front of your existing model traffic in minutes. Explore the documentation and the GitHub repo, or book a Bifrost demo to see it running in front of your own model traffic.