--- title: "How to Choose the Best AI Gateway (2026 Buyer's Guide)" description: "Compare AI gateways for LLM routing, cost control, and MCP support. Evaluation criteria, feature comparison matrix, and use case recommendations for 2026." canonicalUrl: "https://zuplo.com/learning-center/best-ai-gateway-buyers-guide" pageType: "learning-center" authors: "nate" tags: "AI, API Management" image: "https://zuplo.com/og?text=How%20to%20Choose%20the%20Best%20AI%20Gateway%20(2026%20Buyer's%20Guide)" --- Every company is shipping AI features. Whether you are adding a chat assistant to your SaaS product, building an agent-powered workflow, or exposing LLM capabilities through an API, you have one thing in common with everyone else: your application talks to one or more AI model providers, and that traffic needs to be managed. AI gateways are the infrastructure layer that sits between your application and the model providers. They handle routing, cost control, authentication, caching, and observability for AI traffic. Pick the wrong one and you are dealing with runaway costs, blind spots in production, and painful migrations down the road. Pick the right one and you get a control plane that scales with you from prototype to production. This guide breaks down the category, walks through evaluation criteria, compares the leading platforms, and gives you a decision framework so you can choose with confidence. ## What Is an AI Gateway? An AI gateway is a proxy layer that intercepts and manages traffic between your application and AI model providers such as OpenAI, Anthropic, Google, Azure OpenAI, and open-source model hosts. Think of it as a specialized API gateway built for the unique requirements of LLM and generative AI workloads. ### How It Differs from a Traditional API Gateway Traditional API gateways manage REST and GraphQL traffic. They handle authentication, rate limiting, and routing for conventional APIs. AI gateways share some of that DNA but add capabilities specific to AI workloads: - **Model routing and abstraction** -- A single endpoint that can route to different model providers based on cost, latency, or capability. Your application calls one API; the gateway decides which provider handles the request. - **Prompt management** -- Some gateways let you version, template, and transform prompts at the gateway layer rather than in application code. - **Cost controls** -- Token-level metering, spend caps, and budget alerts that prevent a single runaway prompt chain from blowing through your monthly budget. - **Token-aware rate limiting** -- Unlike traditional rate limiting that counts requests, AI gateways can limit by tokens consumed, giving you granular control over cost and capacity. - **Semantic caching** -- Intelligent caching that recognizes semantically similar prompts and returns cached responses, cutting both cost and latency. - **Fallback and retry** -- Automatic failover between providers when one is down or throttling. If OpenAI returns a 429, the gateway retries against Anthropic without your application knowing. - **Guardrails** -- Protection against prompt injection, PII leakage, and content policy violations at the gateway layer. The best AI gateways combine these AI-specific capabilities with the fundamentals you expect from any production API gateway: authentication, access control, logging, and programmability. Some platforms treat AI gateway functionality as a standalone product. Others -- like Zuplo -- integrate it into a full API gateway so you get a single control plane for all your API traffic, AI or otherwise. ## Evaluation Criteria Not all AI gateways are built the same. Here are the dimensions that matter when comparing platforms. ### Model Provider Support The minimum bar is support for OpenAI, Anthropic, and Google. But production stacks often include Azure OpenAI deployments, AWS Bedrock, Mistral, Cohere, and self-hosted open-source models. Check whether the gateway supports your current providers and gives you flexibility to add new ones without code changes. A gateway with a unified API format (typically OpenAI-compatible) lets you swap providers by changing configuration, not application code. ### MCP (Model Context Protocol) Support The [Model Context Protocol](https://modelcontextprotocol.io) is becoming the standard interface for AI agents to discover and call tools. If you are building agentic workflows or exposing your APIs to AI assistants, MCP support at the gateway level means you can turn your existing API endpoints into MCP tools without building a separate server. This is still an emerging capability. Some gateways have full MCP support, some have partial support, and many have none at all. If agents are part of your roadmap, weight this criterion heavily. ### Cost Controls AI costs are unpredictable. A single conversation can consume thousands of tokens, and a bug in a retry loop can burn through your budget in minutes. Look for: - **Token-level rate limiting** -- Cap tokens per user, per app, or per time window. - **Spend caps and budgets** -- Set daily, weekly, or monthly spend limits with automatic cutoffs or alerts. - **Hierarchical controls** -- Organization-level budgets that trickle down to teams and individual applications. - **Caching** -- Semantic caching reduces redundant calls, directly lowering cost. ### Authentication and Access Control Your AI gateway is the front door to expensive model provider credentials. Strong auth is non-negotiable: - **API key management** -- Issue, rotate, and revoke keys without redeploying. - **Per-consumer access policies** -- Different users or applications get different models, rate limits, and permissions. - **Key vaulting** -- Provider API keys never need to leave the gateway. Your developers interact with gateway-issued keys, not raw provider credentials. - **OAuth and JWT support** -- Integration with your existing identity provider. ### Observability You cannot optimize what you cannot measure. AI observability goes beyond request counts: - **Token tracking** -- Input tokens, output tokens, and total tokens per request, per user, per model. - **Cost attribution** -- Real-time cost per request, per consumer, per model provider. - **Latency metrics** -- Time to first token, total response time, and provider comparison. - **Prompt and response logging** -- Configurable logging for debugging and compliance, with PII redaction options. - **Session and trace support** -- For agentic workflows, trace an entire multi-step session across multiple model calls. ### Programmability Off-the-shelf policies cover common use cases, but production AI workloads always have custom requirements. Can you: - Write custom middleware or policies in a real programming language? - Transform requests and responses programmatically? - Implement custom routing logic based on prompt content, user attributes, or model capabilities? - Integrate with your own services for guardrails, logging, or billing? Platforms that limit you to configuration-only will eventually hit a wall. TypeScript-based programmability (as offered by Zuplo) gives you the full expressiveness of a programming language with the managed infrastructure of a gateway. ### Edge Deployment AI latency matters. Every millisecond between your user and the gateway adds to the perceived response time. Gateways deployed at the edge -- on global CDN networks close to your users -- minimize that overhead. Edge deployment also matters for caching. If your semantic cache is on the edge, cached responses are served from the nearest point of presence, dramatically reducing latency for repeated queries. ### Pricing Model AI gateway pricing varies widely. Some platforms charge per request, some charge per logged event, and some offer generous free tiers with paid upgrades. Watch out for: - **Per-log pricing** -- If you are logging every AI request for observability (and you should), per-log fees add up fast at scale. - **Feature gating** -- SSO, RBAC, and advanced rate limiting locked behind enterprise tiers. - **Hidden compute costs** -- Gateways that run on serverless platforms may trigger additional compute charges under heavy load. ## Feature Comparison Matrix The following table compares six leading AI gateway platforms across the evaluation criteria outlined above. | Feature | Zuplo | Portkey | LiteLLM | Helicone | Cloudflare AI Gateway | AWS Bedrock | | ------------------------- | ---------------------------------------------------------------------------- | ----------------------------------------------------------------------- | ----------------------------------------------- | ------------------------------------------------------------------ | --------------------------------------------------- | ------------------------------------------------------------ | | **Multi-Model Support** | OpenAI, Anthropic, Google, Azure, open-source | 250+ models across all major providers | 100+ models, OpenAI-compatible format | 100+ models via proxy | OpenAI, Anthropic, Google, HuggingFace, more | AWS-hosted models (Anthropic, Meta, Mistral, Cohere, Amazon) | | **MCP Support** | Full (MCP Server Handler, remote MCP servers, MCP Gateway) | Yes (MCP Gateway GA as of Jan 2026) | Community integrations | No native MCP support | No native MCP support | AgentCore Gateway with MCP translation | | **Cost Controls** | Token rate limiting, hierarchical spend caps, semantic caching | Budget limits, cost tracking per virtual key | Budget tracking per project/user, spend alerts | Cost tracking and analytics, caching | Rate limiting, caching | Budget controls through AWS Budgets | | **Auth & Access Control** | Built-in API key management, JWT, OAuth, key vaulting, per-consumer policies | Virtual keys, team-based access | Virtual keys, RBAC (paid tier) | API key-based, team access | Cloudflare Access integration | IAM roles, fine-grained permissions | | **Observability** | Request logging, token tracking, cost attribution, real-time analytics | Advanced: prompt logging, traces, sessions, cost analytics, evaluations | Logging with Postgres, integrates with Langfuse | Best-in-class: traces, sessions, user analytics, custom dashboards | Request logging, analytics dashboard, cost tracking | CloudWatch metrics and logging | | **Edge Deployment** | Yes (300+ edge locations globally) | Cloud-hosted, not edge-native | Self-hosted (your infrastructure) | Cloud-hosted, edge caching available | Yes (Cloudflare global network) | AWS regions only | | **Programmability** | Full TypeScript policies, custom handlers, middleware | Configuration-based, guardrails SDK | Python SDK, custom handlers | Configuration-based, webhooks | Workers integration (JavaScript) | Lambda integration, Python/Java SDKs | | **Pricing** | Generous free tier, usage-based scaling | Free tier (10k logs/mo), usage-based from $499/mo | Open-source (self-hosted free), enterprise paid | Free tier (10k requests/mo), usage-based | Free core features, Workers billing at scale | Pay-per-use (model inference costs + AWS service fees) | A few things stand out in this comparison. No single platform dominates every category. Portkey and Helicone lead on AI-specific observability. LiteLLM wins on self-hosted flexibility. Cloudflare has the edge network advantage. AWS Bedrock is the natural fit for AWS-centric organizations. Zuplo is the only platform that combines full API gateway capabilities with AI gateway features, MCP support, and true edge deployment in a single product. ## Platform Deep Dives ### Zuplo [Zuplo](https://zuplo.com) is a programmable API gateway that has expanded into AI gateway territory with a comprehensive feature set. What sets it apart is that it is not just an AI proxy -- it is a full API management platform with AI capabilities built in. **Strengths:** - **Unified gateway** -- Manage both traditional API traffic and AI traffic from a single platform. No need to run separate infrastructure for your REST APIs and your LLM calls. - **TypeScript programmability** -- Write custom policies, handlers, and middleware in TypeScript. This is not configuration-driven templating; it is real code running at the edge. - **MCP support** -- Zuplo's [MCP Server Handler](https://zuplo.com/docs/handlers/mcp-server) automatically exposes your API endpoints as MCP tools. You can also build [remote MCP servers](https://zuplo.com/blog/2025/06/10/introducing-remote-mcp-servers) that AI agents connect to over the network. - **Built-in auth** -- API key management, JWT validation, OAuth, and per-consumer access policies are first-class features, not add-ons. - **Edge deployment** -- Runs on a global edge network with 300+ points of presence. Your gateway logic, caching, and auth all execute close to your users. - **AI-specific security** -- Prompt injection detection and secret masking policies designed for AI workloads. - **Free tier** -- Generous free tier that lets you get started without a credit card. **Best for:** Teams that want a single platform for API management and AI gateway functionality, with the flexibility to write custom logic when needed. ### Portkey [Portkey](https://portkey.ai) is a purpose-built AI infrastructure platform focused on getting LLM applications to production. It provides a unified interface to 250+ models and wraps them with observability, governance, and prompt management. **Strengths:** - **Deep observability** -- Traces, sessions, prompt logging, cost analytics, and evaluation tools give you detailed visibility into every AI interaction. - **Prompt management** -- Version, test, and deploy prompts independently from application code. - **Guardrails** -- Built-in guardrail framework for content filtering, PII detection, and custom validation. - **MCP Gateway** -- Generally available as of January 2026, providing MCP protocol support for agent workflows. - **Wide model support** -- 250+ models from all major providers with a unified API. **Considerations:** Portkey is AI-focused, which means it does not replace your existing API gateway for non-AI traffic. Pricing is based on recorded logs, which can scale up at high volumes. The platform is cloud-hosted rather than edge-deployed. **Best for:** Teams that need deep AI observability and prompt management and already have a separate API gateway for traditional traffic. ### LiteLLM [LiteLLM](https://www.litellm.ai/) is an open-source Python SDK and proxy server that provides a unified OpenAI-compatible API across 100+ model providers. It is the most popular self-hosted option in the category. **Strengths:** - **Open source** -- The core proxy is free and open source. You can inspect the code, contribute, and customize without vendor lock-in. - **Self-hosted** -- Deploy on your own infrastructure for full control over data residency and network topology. - **Model abstraction** -- A clean, OpenAI-compatible API that works across all supported providers. Switch models by changing config. - **Cost tracking** -- Per-project and per-user cost tracking with Postgres integration for custom dashboards. - **Community ecosystem** -- Active community with integrations for Langfuse, Langchain, and other AI tooling. **Considerations:** LiteLLM follows an open-core model. Features like SSO, RBAC, and team-level budget enforcement require the paid enterprise version. Self-hosting means you own the infrastructure, monitoring, and scaling. There is no edge deployment unless you build it yourself. **Best for:** Teams with strong DevOps capabilities that need self-hosted deployment and are comfortable managing their own infrastructure. ### Helicone [Helicone](https://www.helicone.ai/) is an open-source LLM observability platform with gateway capabilities. It started as a logging and analytics tool and has expanded into a full AI gateway built in Rust for performance. **Strengths:** - **Best-in-class observability** -- Comprehensive tracing, session tracking, user analytics, and custom dashboards. If you need to understand how your AI application is being used in production, Helicone is hard to beat. - **One-line integration** -- Change your base URL and you are routing through Helicone. No SDK required. - **Performance** -- Built in Rust with sub-millisecond overhead on the proxy path. - **Cost analytics** -- Detailed cost breakdowns by model, user, feature, and custom dimensions. - **Edge caching** -- Cache layer that reduces cost and latency for repeated queries. **Considerations:** Helicone's gateway features (routing, fallback, rate limiting) are functional but less mature than its observability stack. No native MCP support. Auth and access control are basic compared to a full API gateway. **Best for:** Teams whose primary concern is understanding and optimizing their AI usage in production. Pairs well with a separate API gateway. ### Cloudflare AI Gateway [Cloudflare AI Gateway](https://developers.cloudflare.com/ai-gateway/) is part of Cloudflare's developer platform. It provides a proxy layer between your application and AI providers with caching, rate limiting, and analytics. **Strengths:** - **Edge network** -- Runs on Cloudflare's global network with points of presence in 300+ cities. The latency advantage is real. - **Caching** -- Aggressive caching layer that can dramatically reduce costs for applications with repetitive queries. - **Free core features** -- The base gateway features are free. You only pay when you scale into Workers compute territory. - **Workers integration** -- If you are already on the Cloudflare stack, the AI Gateway integrates with Workers, KV, and the rest of the platform. - **Simple setup** -- One line of code to start routing through the gateway. **Considerations:** Cloudflare AI Gateway is relatively basic compared to purpose-built AI gateway platforms. No native MCP support. Limited programmability beyond what Workers provides. Observability is functional but not as deep as Helicone or Portkey. Auth is handled through Cloudflare Access, which works but is a separate product. Log limits on the free tier (100,000 logs/month) can be restrictive for production workloads. **Best for:** Teams already on the Cloudflare platform that need basic AI gateway features with excellent caching and edge performance. ### AWS Bedrock [Amazon Bedrock](https://aws.amazon.com/bedrock/) is AWS's managed service for building generative AI applications. It provides access to foundation models from Anthropic, Meta, Mistral, Cohere, and Amazon through a unified API with AWS-native integrations. **Strengths:** - **AWS-native** -- Tight integration with IAM, CloudWatch, Lambda, S3, and the rest of the AWS ecosystem. If your stack is on AWS, Bedrock fits naturally. - **Intelligent Prompt Routing** -- Automatically routes prompts to the cost-optimal model within a model family, reducing costs by up to 30% without sacrificing quality. - **AgentCore Gateway** -- Converts APIs and Lambda functions into MCP-compatible tools with built-in authentication and credential management. - **Security and compliance** -- Data never leaves your AWS account. SOC2/HIPAA/FedRAMP compliance through AWS's existing certifications. - **Model customization** -- Fine-tune and customize models directly within the platform. **Considerations:** Bedrock is locked into the AWS ecosystem. Model selection is limited to what AWS hosts (no direct OpenAI GPT access, for example). Pricing is complex -- you pay for model inference, provisioned throughput, and various AWS service fees. The developer experience is AWS-typical: powerful but verbose, with a steep learning curve if you are not already fluent in AWS services. **Best for:** Organizations with deep AWS investments that want to keep their AI workloads within the AWS ecosystem. ## Use Case Recommendations Different teams have different needs. Here is a quick guide based on common scenarios. ### Startup Building AI Features **Recommended: Zuplo** You need to move fast, keep costs low, and avoid managing infrastructure. Zuplo gives you a free tier, sub-minute setup, and a single platform for both your traditional API endpoints and your AI routes. As you grow, the same gateway scales with you -- no migration needed. ### Enterprise with an Existing API Gateway **Recommended: Zuplo** If you are running Kong, Apigee, or AWS API Gateway for your traditional APIs and now adding AI features, you face a choice: bolt on a separate AI proxy or consolidate. Zuplo lets you consolidate onto a single programmable gateway that handles everything, reducing operational complexity and eliminating the need to coordinate policies across two systems. ### Need Deep AI Observability **Recommended: Helicone or Portkey** If your primary concern is understanding how your AI features behave in production -- tracing agent workflows, analyzing prompt effectiveness, attributing costs to specific features -- Helicone and Portkey are purpose-built for this. Helicone excels at analytics and performance tracking. Portkey adds prompt management and evaluation frameworks. ### AWS-Heavy Stack **Recommended: AWS Bedrock** If your organization has standardized on AWS, your compliance requirements mandate keeping data within AWS, and your team is fluent in AWS services, Bedrock is the path of least resistance. The IAM integration, CloudWatch observability, and Lambda extensibility fit your existing operational model. ### Self-Hosted Requirement **Recommended: LiteLLM** If data residency, network isolation, or regulatory requirements mean you must run everything on your own infrastructure, LiteLLM is the proven open-source option. You get model abstraction and cost tracking out of the box, and you can extend it with Python. Budget for the operational overhead of running and scaling the proxy yourself. ### Need Edge Performance with Basic Controls **Recommended: Cloudflare AI Gateway** If you are already on the Cloudflare platform and need low-latency caching and basic rate limiting for AI traffic, Cloudflare AI Gateway is a natural fit. The free tier is generous for getting started, and the edge network performance is excellent. ## Decision Checklist Before you commit to an AI gateway, work through these ten questions with your team. 1. **What model providers do you use today, and which might you add in the next 12 months?** Make sure the gateway supports your current and likely future providers. 2. **Are AI agents or MCP part of your roadmap?** If yes, prioritize gateways with native MCP support. Retrofitting MCP later is painful. 3. **What are your cost control requirements?** Do you need per-user token limits, organizational spend caps, or both? Map your requirements to each platform's capabilities. 4. **Do you already have an API gateway?** If so, evaluate whether you want a unified platform or are willing to operate two separate systems. 5. **What are your observability requirements?** Basic request logging, or full tracing with session support and cost attribution? The answer narrows the field significantly. 6. **Do you need custom logic?** If your use case requires anything beyond off-the-shelf policies, evaluate the programmability of each platform. Can you write real code, or are you limited to configuration? 7. **Where are your users?** If latency matters (and for AI applications, it usually does), edge deployment should be a strong factor. 8. **What is your deployment model?** Cloud-managed, self-hosted, or hybrid? This immediately filters out some options. 9. **What is your budget?** Model the total cost at your expected scale, including per-request fees, logging costs, and compute charges. Free tiers are great for prototyping but check what happens at 1M requests/month. 10. **What does your security posture require?** API key vaulting, SOC2 compliance, data residency constraints, and PII handling policies all affect which platforms qualify. ## Get Started with Zuplo's AI Gateway If you are looking for a platform that combines AI gateway capabilities with full API management, edge deployment, TypeScript programmability, and native MCP support, [Zuplo](https://zuplo.com) is built for exactly that. The free tier gets you started in minutes. No credit card required, no infrastructure to manage. Route your first AI request, set up cost controls, and see real-time analytics -- all from a single dashboard. [Start building with Zuplo's AI Gateway for free.](https://portal.zuplo.com/signup)