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AI Gateway Comparison
AI Gateway Comparison for LLMs and AI Agents
A practical guide for teams extending API gateway infrastructure to LLM APIs, AI agents, AI proxying, LLM load balancing, token rate limiting, retry and fallback, MCP support, security, and observability.
Use this page as a decision guide, not a universal ranking. Validate any recommendation with a proof of concept that uses your own routes, policies, identity providers, logs, and deployment workflow.
Evaluation criteria
What to evaluate
The right gateway depends on runtime behavior, operational model, extensibility, and how much of the stack your team wants to own.
Runtime fit
Start with what runs in the request path. A gateway runtime, API management suite, ingress controller, service mesh proxy, and reverse proxy have different jobs.
Request path ownership
Control plane model
Failure behavior
Operational blast radius
Open-source boundary
For an Apache project website, the most important question is what a team can evaluate, deploy, modify, and operate from open source.
License
Governance
Plugin availability
Self-hosted operations
Platform integration
Compare how each option fits Kubernetes, identity providers, observability, CI/CD, GitOps, and cloud networking.
Kubernetes
Authentication
Observability
Deployment workflow
Comparison matrix
Feature and operating model comparison
This matrix summarizes practical differences using official documentation and publicly available project pages.
Criterion
Apache APISIX
Other AI gateway options
Primary goal
Use APISIX AI Gateway to manage traffic between applications, AI agents, and LLM providers through an open-source gateway layer.
Managed API management platforms may package AI gateway capabilities with cloud identity, portal, analytics, and policy workflows.
LLM traffic concerns
Evaluate AI proxying, LLM load balancing, retry and fallback, token rate limiting, MCP support, authentication, logging, and upstream resilience.
Each platform should be tested for model provider support, policy granularity, observability, and operational ownership.
Open-source fit
APISIX keeps the evaluation centered on open-source gateway infrastructure.
Other options may be cloud-managed, separately packaged, or broader API management suites.
Alternative shortlist
AI gateway options to evaluate
AI gateway evaluation is still evolving. Use official documentation and a proof of concept with real LLM providers, token limits, fallback behavior, MCP tools, logs, and security controls.
1Apache APISIX
Apache APISIX is an Apache 2.0 open-source API gateway for dynamic routing, plugin-based policy, observability, and APISIX AI Gateway capabilities.
Apache Software Foundation governance
100+ plugins across gateway policy categories
Apache APISIX Ingress Controller for Kubernetes workflows
Good fit for open-source gateway infrastructure.
2Google Cloud Apigee
Google Cloud Apigee is commonly evaluated as a Google Cloud API management platform.
Apigee centers on API proxies, policies, environments, organizations, provisioning, and Google Cloud management workflows.
Apigee can fit when the requirement is a broad API management suite tightly connected to Google Cloud rather than an open-source gateway runtime.
Often a fit for organizations standardizing API programs around Google Cloud, API proxies, policies, analytics, and developer-facing API management.
3Azure API Management
Azure API Management is commonly evaluated as a Microsoft Azure API management service.
Azure API Management provides managed API publishing, gateway, policy, portal, monitoring, and Azure integration workflows.
Azure API Management can fit when managed Azure API program workflows matter more than open-source gateway ownership.
Often a fit for teams publishing APIs securely at scale to external, partner, and employee developers in Azure-centered environments.
4Kong Gateway
Kong Gateway is commonly evaluated as a cloud-native API gateway and API platform ecosystem.
Kong supports multiple deployment and configuration modes, including self-managed and platform workflows.
Kong can remain a good fit when existing tooling, training, plugins, and control-plane workflows are already standardized around Kong.
Often a fit for teams already invested in Kong Gateway, Kong-specific plugins, or Kong-oriented platform workflows.
5Tyk
Tyk is commonly evaluated as a API gateway and API management platform.
Tyk documentation emphasizes API management, API publishing, AI management, governance, and cloud or self-managed evaluation paths.
Tyk can fit when API lifecycle and management workflows matter more than open-source gateway runtime control.
Often a fit for teams prioritizing packaged API management, publishing, governance, and portal workflows.
Decision guide
How to decide
Start with use case fit, then test the highest-risk policies and rollout path before committing to a production change.
Choose Apache APISIX when
You need an Apache 2.0 open-source API gateway with dynamic routing, a broad plugin hub, Apache APISIX Ingress Controller, and APISIX AI Gateway capabilities.
Open-source-first evaluation
Dynamic gateway policy
Kubernetes controller workflows
Plugin-driven security and observability
Choose another option when
Another product may fit better when your main requirement is managed cloud convenience, a full API management suite, ingress-only routing, or service mesh data-plane control.
Cloud-managed gateway
Developer portal suite
Existing platform standard
Service mesh proxy requirements
Open-source next steps
Continue with Apache APISIX resources
Move from evaluation to hands-on testing with documentation, plugins, Kubernetes resources, GitHub, and community channels.
This page uses official product documentation and project pages where possible. It avoids vendor benchmark claims unless a reader can verify them independently.
Does this page compare non-open-source APISIX offerings?
No. The content is written for the Apache APISIX open-source website and focuses on public documentation, open-source fit, and practical evaluation criteria.
Should a team decide from a feature table alone?
No. Use the table to shortlist options, then test real routes, authentication, rate limits, logs, failure handling, and deployment workflows.