AI Gateway Comparison: Top Solutions in 2026

A single LLM call can cost quite little or cost a lot depending if someone uploads a fifty-page document for summarization instead of typing a greeting. This quietly burns through a chunk of your monthly budget in one request. That volatility is exactly what an AI gateway exists to handle.

Running multiple LLMs in production is quite complex as you have to manage different API formats, handle provider outages, keep latency low and control spend that moves unpredictably. An AI gateway sits between your applications and your model providers acting as an intelligent control point for routing, security, cost and observability. As agentic workflows and MCP-based tool calling move from experiment to production, that control point is increasingly being asked to govern not just prompts, but the tools and internal systems an agent can reach.

The category has moved fast. What started as simple LLM proxies now spans everything from lightweight open-source routers to full governance platforms that manage models, agents and MCP servers under one roof. The right fit depends heavily on where your team sits on that spectrum. This guide breaks down the leading AI gateways across deployment model, cost control, security and observability. Starting with WSO2 AI Gateway and covering where each competitor genuinely leads so you can match a gateway to your actual constraints rather than a feature checklist.

AI Gateway Comparison at a Glance​

The table below compares the leading AI gateways across the dimensions that matter most when you move from a prototype to production-scale AI. The "best" choice depends on your governance needs, deployment model and team.

Gateway	Deployment	Open source	Routing and failover	Cost control	Security and guardrails	Observability	Best for
WSO2 AI Gateway	Self-hosted, hybrid, SaaS	Yes	Multi-model and multi-provider routing, round-robin load balancing	Token- and LLM cost-based rate limiting, department chargeback	Semantic prompt validation, PII masking, plus Azure and Bedrock guardrails	Built into the API Platform control plane	Enterprises needing unified API, AI, and MCP governance
Kong AI Gateway	Self-hosted, cloud	Core is open source	Multi-LLM routing via plugins	Plugin-based limits	Plugin-based policies	Via Konnect Observability	Teams already standardized on Kong
Portkey	SaaS-first	Yes	Provider switching, fallbacks	Usage tracking	Guardrails, prompt tooling	Strong request-level tracing	Prompt engineering and quick prototyping
LiteLLM	Self-hosted	Yes	OpenAI-compatible routing, fallbacks	Budgets and key limits	Basic	Callback and logging hooks	Developers wanting a lightweight, self-hosted proxy
Cloudflare AI Gateway	Cloud-native (edge)	No	Provider routing	Caching to cut spend	Platform controls	Edge analytics and logging	Apps already on Cloudflare's edge
Helicone	SaaS and self-hosted	Yes	Load balancing, fallbacks	Cost tracking	Basic	Observability-first, high throughput	Discovering AI spend and usage at high scale

WSO2 AI Gateway stands out through its scope. It governs outbound LLM traffic and agent-to-tool traffic in both directions from one control plane, with self-hosted options that can run offline with no outbound internet connection required. Where a competitor leads on a specific axis is stated below.

How to Choose an AI Gateway (Evaluation Criteria)​

Feature counts are a poor way to pick a gateway. A tool with fifty features you never use loses to one that nails the six dimensions your workloads actually depend on. Here is the evaluation framework we recommend for any enterprise AI gateway vendors comparison.

• Model coverage and routing: Can it reach the AI providers you use today and the ones you will add next? Look for model-aware routing, health-aware load balancing, and automatic failover so a single provider outage does not take your product down with it. A gateway that only supports two or three providers well will start to constrain you the moment a new model outperforms your default on cost or quality.
• Cost control: Token-level metering, budget-based rate limiting keep spend predictable. This matters more than it sounds because the cost of a single request can differ depending on whether someone typed a greeting or asked for a full document summary. So request-based rate limiting alone does not protect your budget the way token-based limiting does. Organization-wide and department-level chargeback matters once more than one team shares the same gateway.
• Security and governance: This is where a traditional API gateway adapted for AI often falls short on AI gateway security. Prompt-injection defenses, PII masking and content moderation are AI governance primitives that classic proxies lack.
• Observability: You cannot optimize what you cannot see. Token usage, latency, error rates, and full audit trails are the baseline for AI gateway observability in production. As agentic and multi-step workflows become more common, session-level tracing across an entire chain of tool calls becomes just as important as single-request logging.
• Deployment model: Self-hosted, hybrid, and SaaS each carry different data-residency and control trade-offs. Regulated teams often need on-prem options that can run fully disconnected from the internet, because prompts and responses cannot legally leave a controlled network boundary which is something SaaS-only tools cannot offer. Particularly in banking, healthcare and government contexts where prompts and responses cannot legally leave a controlled network boundary.
• Governance maturity: Lifecycle management, versioning and policy enforcement separate a request-level proxy from a platform that governs AI systems across an organization. This is also where MCP and agent governance increasingly belong. As agents start calling internal tools rather than just querying a model, who can register a tool, who can call it and under what authorization becomes as important as which model answered the prompt.

Score each candidate against these six, weighted for your context. A fintech under SOC2, HIPAA, and GDPR obligations will weight security and deployment far higher than a startup racing through prototypes, while a developer-tools company iterating rapidly on prompts may weight observability and developer experience higher than deployment flexibility.

The Leading AI Gateways Compared​

Below is an honest per-vendor look. We keep third-party descriptions factual and note where each tool is the strongest fit.

WSO2 AI Gateway​

WSO2 AI Gateway is the umbrella over two capabilities in the WSO2 API Platform: an LLM Proxy for outbound calls to model providers, an MCP Proxy that secures both inbound agent access to your own tools and outbound governance of external MCP servers you consume, presented as a single control plane for all AI traffic. WSO2 was named a Leader in the Forrester Wave™: API Management Software (Q3 2024), reflecting a mature governance heritage rather than a point AI tool.

Core features:

1. LLM Proxy handles multi-model routing across OpenAI, Azure AI, AWS Bedrock, Anthropic, Google Gemini and Mistral AI, with round-robin balancing and failover.
2. Guardrails covering semantic prompt validation, PII masking, URL and JSON Schema checks and semantic caching. Along with Azure Content Safety and AWS Bedrock Guardrails integrations.
3. Token-based rate limiting and department-level chargeback for cost control.
4. MCP Proxy lets platform teams build managed MCP Proxies on top of MCP servers, giving developers a controlled, secure endpoint to expose tool capabilities to AI agents.
5. MCP Proxy manages both internally created and externally consumed MCP servers under the same governance layer.
6. Deploys self-hosted, hybrid or as SaaS, which includes fully disconnected environments with no outbound internet access and data-sovereignty requirements.

Best for: Enterprises that want one platform to govern APIs, AI and MCP together, with hybrid or on-prem deployment.

Kong AI Gateway​

Kong AI Gateway extends the widely used Kong API gateway with plugins that route and manage traffic to multiple LLM providers. It builds on an established, cloud-native proxy. Teams already standardized on Kong get AI routing, rate limiting, and policy enforcement inside tooling they already trust, rather than adopting an entirely new data plane just to handle LLM traffic.

Core features:

1. Multi-LLM routing and traffic management delivered as plugins on Kong Gateway.
2. For organizations already running Kong for REST and GraphQL, adding AI traffic through the same operational model lowers the switching cost of AI governance.
3. Core AI proxy capability is open source, consistent with Kong's broader plugin model.
4. Policy enforcement and rate limiting inside familiar Kong tooling.

Limitations: Full AI governance typically means adopting additional Kong plugins on top of the core proxy and evaluating each one for open-source versus enterprise-tier scope.

Best for: Organizations with an existing Kong footprint that want to add AI traffic management without introducing a new data plane.

Portkey​

Portkey is a SaaS-first AI gateway with a strong focus on prompt tooling, guardrails and request-level observability.

Core features:

1. Provider switching and fallbacks that decouple applications from any one provider's API format.
2. Strong prompt management and request-level tracing for teams iterating heavily on prompts.
3. As of March 2026, the full Gateway including governance, observability and authentication, is now open source.
4. Fast, hosted setup with minimal infrastructure overhead.

Limitations: Its SaaS-first model means self-hosting is a newer capability rather than the primary way most teams run it.

Best for: Prompt engineering, experimentation, and quick prototyping where hosted convenience beats self-hosting.

LiteLLM​

LiteLLM is a popular open-source LLM proxy that exposes an OpenAI-compatible interface in front of many providers.

Core features:

1. OpenAI-compatible interface across many providers, with fallbacks.
2. Budgets and per-key limits for cost control.
3. Lightweight, self-hosted, and fast to stand up.
4. Callback and logging hooks for observability integrations.

Limitations: Governance and security features are comparatively basic out of the box. Teams typically add tooling as multi-team budgets or compliance needs grow. See our guide to LiteLLM alternatives to understand it better.

Best for: Developers who want a simple, self-hosted, OpenAI-compatible router they fully control.

Cloudflare AI Gateway​

Cloudflare AI Gateway is a cloud-native option that runs at Cloudflare's edge, adding caching, rate limiting and analytics in front of model providers.

Core features:

1. Edge-based caching that can cut both spend and latency for apps already on Cloudflare.
2. Rate limiting and provider routing.
3. Platform-level controls and analytics/logging built into Cloudflare's existing dashboard.

Limitations: Not open-source or self-hostable. It's a hosted service tied to the Cloudflare platform.

Best for: Teams whose stack already lives on Cloudflare and who want low-friction routing and observability at the edge.

TrueFoundry, Helicone and others​

TrueFoundry positions itself as a broader ML and AI platform and publishes a well-known evaluation framework for choosing gateways that many teams reference during vendor selection. A good fit for teams that want gateway capability bundled with a wider ML platform.

Helicone is an observability-first, open-source LLM gateway built for high scale, with a Rust core that gives it a speed advantage and strong tooling to discover AI spend across models, users and features. Worth knowing before you commit: Helicone was acquired by Mintlify in March 2026 and is now in maintenance mode with security updates and bug fixes continuing.

Agenta and NeuralTrust round out the field with analytics, deep listicle-style comparisons and benchmarking respectively. Each of these is a reasonable pick when its specialty matches your top priority, whether that is observability, evaluation depth or benchmarking data.

Open-Source vs SaaS vs Cloud-Native Gateways​

The comparison above splits along a deployment axis that deserves its own section, because it drives data residency, cost and control more than any single feature does.

Open-source gateways: LiteLLM, Helicone and WSO2 give you full control and self-hosted deployment. You run the data plane, so prompts and responses never have to leave your network. This is the model regulated environments including ones with no external network access often need and it avoids passthrough billing markups that can appear when a hosted intermediary sits between you and the model provider. The trade-off is that you operate it. Patching, scaling and monitoring the gateway itself becomes your team's responsibility rather than a vendor's.

SaaS gateways: Portkey removes that operational burden and gets you to value fast. The trade-off is data residency, your traffic transits a third party which can be a non-starter under strict compliance regimes and predictable long-term costs can be harder to model since pricing is often tied to logged requests or tokens.

Cloud-native AI platforms: Cloudflare AI Gateway and the native offerings around AWS Bedrock and Azure AI Foundry, are deeply integrated with one cloud. That integration is convenient if you are all-in on that provider and limited if you are multi-cloud or want to avoid deeper dependency on a single vendor's infrastructure.

WSO2 sits across this spectrum rather than in one box. It is open source, yet it also offers hybrid deployment, where the data plane runs in your network and the control plane runs as SaaS. It even offers a fully managed cloud option for teams that want neither the operational burden of full self-hosting nor the data-residency trade-offs of a pure SaaS product. That flexibility, combined with multi-gateway federation that manages WSO2, Kong, AWS, Azure and Envoy gateways from one place, is the main reason it appears in an open-source-versus-SaaS discussion at all.

Which AI Gateway Is Right for You?​

Most teams find one row that clearly fits so match your primary constraint to the shortlist given below.

• Do you need one control plane governing APIs, AI models, and MCP tools together, with policies that cascade from admin-set guardrails down to every team automatically? Look for a platform built around unified governance rather than a collection of separate tools bolted together. Check out our documentation on the AI Gateway overview.
• Do you require masking of confidential data in requests and responses? In this case, pick a platform that supports guardrails that can implement PII redaction and other custom mapping logic. Check out our documentation on PII masking with regex.
• Do you iterate constantly on prompts and need built-in versioning, templating, and request-level tracing? Look for a gateway with native prompt management inside the request pipeline, not a logging layer bolted on afterward. Check out our documentation on prompt templates and decorators.
• Do you need a lightweight, self-hosted, OpenAI-compatible proxy you can stand up quickly with minimal operational overhead? Look for a platform that lets you start small and add governance, guardrails, and MCP support later, without switching platforms. Check out our documentation for more deployment options.
• Are observability and discovering AI spend at high scale your top priority? Look for a platform with native token-usage analytics, cost-attribution dashboards, and per-model spend tracking built into the gateway itself. Check out our documentation on rate limiting and cost control.

If your main need is a fast request-level router, several tools here do that well. If you need to govern AI systems and agents alongside your existing APIs under real compliance pressure, a unified platform with self-hosted and air-gapped options is worth the added scope.

Conclusion​

The right AI gateway depends on governance needs, deployment model, and scale, not on who has the longest feature list. Use the six-dimension framework to shortlist, then pressure-test your top two against a real workload before committing. Token costs, latency under load and how each guardrail actually behaves against your own prompts will tell you more than any comparison table.

If you need routing convenience, this field is rich as LiteLLM, Portkey and Cloudflare AI Gateway each do that job well for different constraints. If you need to ship, govern and monetize APIs, AI and MCP together, with minimal lock-in and the option to run everything in your own network, that is the problem WSO2's AI Gateway is built to address. Explore WSO2 AI Gateway to see how a unified control plane handles outbound LLM traffic and agent-to-tool traffic in both directions.

Frequently Asked Questions​

What is the best AI gateway in 2026? The best AI gateway depends on your governance needs, deployment model and scale. Score candidates on model coverage, cost control, security, observability, deployment and governance maturity, then choose against your weighting.

What is the difference between an AI gateway and a traditional API gateway? A traditional API gateway is built for request routing, authentication and rate limiting on generic APIs. An AI gateway adds capabilities a classic proxy doesn't have: model-aware routing, token-level cost control, prompt-injection defenses, PII masking, and semantic caching. Some vendors ship an API gateway adapted for AI while others build AI-native.

How does an AI gateway work? An AI gateway sits between your applications and LLM providers, handling routing, authentication, guardrails and usage tracking from one control point. WSO2's version routes across providers like OpenAI, Azure AI, AWS Bedrock, Anthropic, Google Gemini and Mistral with round-robin and automatic failover, while enforcing token-based rate limits and running semantic and PII guardrails on every request.

OpenRouter vs Vercel AI Gateway: which should I use? Both are convenience-focused routers that give one interface across many providers, aimed at developers who want fast provider switching without hosting infrastructure. If your priority is enterprise governance, data residency or unified API and agent control, neither is a substitute for a self-hosted or hybrid platform gateway.

How do enterprise AI gateways compare on security? This gap is widest in this category. Enterprise-grade options add semantic prompt validation, PII masking, content moderation, full audit trails and support for standards such as SOC2, HIPAA and GDPR. Lightweight proxies typically leave these to you to build or bolt on separately. Weight this dimension heavily if you handle sensitive data.

What is the best open-source AI gateway? LiteLLM is a widely used open-source option, valued for being lightweight, OpenAI-compatible, and self-hostable. WSO2 AI Gateway is also fully open source, but goes further architecturally. It unifies an LLM Proxy and MCP Proxy under one control plane, with self-hosted, hybrid, and SaaS deployment options, including the ability to run offline in disconnected environments.