Best API Gateways for AI Agents

Calling OpenAI or Anthropic directly works for prototypes. It breaks down at scale for three reasons.

First, cost scales with tokens, not requests. A single agent call that sends a 4,000-token prompt and receives a 2,000-token response costs 30x more than a call with a 200-token prompt. Request-based rate limiting can't prevent a runaway agent from burning through your monthly budget in an hour.

Second, agents need failover between providers. When OpenAI returns a 429 or 503, your agent shouldn't crash. It should retry with Anthropic or a local model. Traditional gateways can route traffic, but they don't understand model equivalence or prompt compatibility across providers.

Third, caching works differently for LLM traffic. Semantic caching, where similar prompts return cached results even if the wording differs slightly, can cut API costs by 40-60% for workloads with repeated query patterns. A 2024 study on GPT Semantic Cache showed cache hit rates between 61% and 69% across common query categories. Cached responses returned in under 50ms, compared to multi-second API calls.

AI gateways solve all three problems. They sit between your agents and LLM providers, adding token-aware rate limiting, intelligent caching, automatic failover, and cost observability without changing your application code.

We compared each gateway across six dimensions that matter for agent workloads:

• Streaming support: Does the gateway handle Server-Sent Events (SSE) for real-time token delivery? Can it track token counts during a streaming response, not just after?
• Token usage tracking: Can you see input tokens, output tokens, cost per request, and aggregate spend broken down by model, user, or API key?
• Caching: Does it offer exact-match caching, semantic caching, or both? What's the cache hit rate in practice?
• Failover and routing: Can you define fallback chains across providers? Does it support load balancing by latency, cost, or custom rules?
• Rate limiting: Token-based or request-based? Can you set budgets per team, project, or individual key?
• Self-hosting: Can you run it on your own infrastructure, or is it SaaS-only?

Pricing is noted for each tool, but we weighted capabilities and developer experience over raw cost since most gateways offer free tiers for experimentation.

1. Portkey

Portkey is purpose-built for LLM traffic. It routes requests to over 250 models through a single API, with built-in observability, caching, and reliability features. Portkey processes over 400 billion tokens monthly across 200+ enterprise customers, which gives it battle testing that newer gateways lack.

Key strengths:

• Automatic fallbacks, load balancing, and conditional routing across providers
• Semantic caching and exact-match caching to reduce latency and cost
• Detailed token tracking with cost attribution per request, per key, per team
• Guardrails for content filtering and output validation
• Full streaming support with real-time token counting

Limitations:

• Governance features (audit logging, RBAC) are locked behind the Enterprise tier, which starts around $2K-5K/month
• Pro tier limits log retention to 30 days
• Self-hosting requires an Enterprise agreement

Best for: Teams running production agents that need managed observability and multi-provider routing.

Pricing: Free tier for development. Pro tier for production. Enterprise for governance and compliance (SOC 2 Type 2, ISO 27001, HIPAA).

2. Kong AI Gateway

Kong added AI-specific capabilities to its established API gateway starting with version 3.6. By version 3.11, Kong offers token-aware rate limiting, semantic caching via Redis, prompt compression, and multi-model routing. If you already run Kong for your API infrastructure, the AI plugins integrate without adding another service to your stack.

Key strengths:

• Token-based rate limiting that enforces quotas on prompt tokens, response tokens, or total tokens per user or application
• Prompt compression plugin that reduces token count by up to 5x while preserving 80% of semantic meaning
• Semantic routing that directs requests based on prompt content, latency targets, or cost constraints
• Full SSE streaming support in AI Proxy and AI Proxy Advanced plugins
• Self-hosted, Kubernetes-native, or Kong Cloud deployment

Limitations:

• AI features require Kong Gateway Enterprise for production use
• Configuration is more complex than AI-native gateways since you're layering plugins onto a general-purpose gateway
• Pricing is opaque and requires a sales conversation

Best for: Teams already using Kong for API management who want to add AI gateway capabilities without introducing a new service.

Pricing: Kong Gateway OSS is free (Apache 2.0). AI-specific plugins require Kong Gateway Enterprise (quote-based).

3. Helicone

Helicone started as an LLM observability platform and expanded into a full gateway. Its differentiator is zero-markup pricing: you pay exactly what providers charge, plus Stripe processing fees. The gateway handles failovers, rate limiting, caching, and logging with minimal setup, often requiring just one line of code to integrate with your existing OpenAI SDK calls.

Key strengths:

• Zero-markup model pricing with intelligent routing to the cheapest provider per request
• Open source and free to self-host
• Automatic request logging with no extra configuration
• Prompt management with versioning and a built-in playground
• OpenTelemetry support for distributed tracing
• Supports 100+ LLM providers through the OpenAI SDK format

Limitations:

• Smaller provider ecosystem than Portkey (100+ vs 250+ models)
• Semantic caching is less mature than Portkey or Kong's Redis-backed implementation
• Enterprise features and pricing are less transparent than competitors

Best for: Cost-conscious teams that want transparent pricing and strong observability without vendor lock-in.

Pricing: Free tier with 10,000 requests/month (no credit card). Open source for self-hosting. Paid plans for higher volumes.

4. LiteLLM

LiteLLM is an open-source Python library and proxy server that gives you an OpenAI-compatible API for 100+ providers. It's the most popular self-hosted option, with the MIT-licensed core handling routing, cost tracking, and budget management without licensing fees.

Key strengths:

• MIT-licensed core with no vendor lock-in
• Virtual keys with per-key budget limits and rate limiting
• Built-in retry and fallback logic across model deployments
• Application-level load balancing with cost tracking
• Admin UI for managing keys, budgets, and users

Limitations:

• Running it yourself means managing compute, databases, monitoring, and upgrades
• SSO, RBAC, and premium support require an enterprise agreement
• Total cost of ownership for self-hosted deployments runs around $3,500/month in infrastructure and maintenance at moderate scale

Best for: Teams with DevOps capacity who want full control over their AI gateway without licensing costs.

Pricing: Free (MIT license). Enterprise support available for SSO, RBAC, and SLAs.

5. Cloudflare AI Gateway

Cloudflare's AI Gateway runs on its global edge network, meaning your agent's LLM requests route through the nearest Cloudflare PoP before reaching the provider. The core gateway features (analytics, caching, rate limiting) are free. You need a Cloudflare account and one line of code to get started.

Key strengths:

• Free core features with no per-request fee
• Global edge network reduces latency for geographically distributed agents
• Unified billing across providers on the Workers Paid plan
• Dynamic routing and automatic failover between models
• Secure key storage so provider API keys never reach your application code
• Data Loss Prevention (DLP) for prompt content

Limitations:

• Persistent logging and secrets management will become paid features
• High-volume logging adds hidden costs through Workers execution
• Fewer AI-specific features than purpose-built gateways like Portkey
• No self-hosting option

Best for: Teams already on Cloudflare who want basic AI gateway features at zero cost, or anyone who needs edge-based latency optimization.

Pricing: Free core features. Workers Paid plan ($5/month) unlocks unified billing and higher limits.

6. OpenRouter

OpenRouter is a model marketplace with gateway capabilities. It gives you access to 290+ models from every major provider through a single API key, with no markup on provider pricing. The standout feature is free models: dozens of models available at zero cost with rate limits of 20 requests/minute and 200/day.

Key strengths:

• 290+ models with no price markup over provider rates
• Zero Data Retention (ZDR) option routes requests only to providers that don't store prompts
• Automatic fallback routing between models
• Free models for prototyping and experimentation
• OpenAI-compatible API format

Limitations:

• Limited observability compared to dedicated gateways, with no built-in dashboards for token-level cost breakdowns
• No semantic caching
• No self-hosting option
• Rate limits on free models restrict production use

Best for: Developers prototyping agents who want quick access to many models without managing provider accounts, or teams that need ZDR for data-sensitive workloads.

Pricing: Pay-as-you-go at provider rates. Free models available with rate limits. No minimums or lock-in.

7. Google Apigee

Apigee is Google Cloud's enterprise API management platform with AI-specific capabilities for managing agent traffic. It's the heaviest option on this list, designed for organizations that need centralized governance across hundreds of APIs and AI endpoints.

Key strengths:

• Token-based quota policies at the API product level
• Model Armor integration for AI safety checks on prompts and responses
• API specification boosting that auto-enhances API docs for agent discoverability
• MCP server bundling into managed AI products
• Multi-cloud model routing with circuit breaking
• Integration with Looker Studio for custom AI usage reports

Limitations:

• Significant complexity and setup time compared to developer-focused tools
• Pricing requires a Google Cloud commitment
• Overkill for small teams or single-agent deployments

Best for: Enterprise teams on Google Cloud that need unified API and AI gateway governance.

Pricing: Quote-based through Google Cloud.

8. TrueFoundry

TrueFoundry combines an AI gateway with an MCP gateway in a single platform, making it relevant for teams building agent systems that need both LLM access and tool orchestration. Gartner recognized TrueFoundry in both its 2025 Market Guide for AI Gateways and its Innovation Insight report on MCP Gateways.

Key strengths:

• Unified AI gateway and MCP gateway with OAuth 2.0 secured tool access
• Supports 250+ LLMs through a single endpoint
• MCP server registry with centralized discovery and RBAC
• Request-level tracing and audit logs across agent workflows
• Identity injection so agents act on behalf of specific users

Limitations:

• Smaller community and ecosystem than Kong, Portkey, or LiteLLM
• Enterprise pricing is not publicly listed
• Newer platform with less production track record at scale

Best for: Teams building agentic systems that need both LLM routing and MCP tool governance in one place.

Pricing: Contact TrueFoundry for pricing.

These two features matter most for agent workloads, and gateways handle them very differently.

Streaming support is table stakes. All eight gateways support SSE streaming, which delivers tokens to the client as they're generated instead of waiting for the full response. The differences are in what happens during the stream. Kong, Portkey, and Helicone provide real-time token counting while the response is still flowing. OpenRouter and Cloudflare support streaming but with less granular mid-stream analytics.

Token usage tracking separates the contenders. Portkey leads with hierarchical cost attribution: you can break down spend per request, per API key, per team, and per project. Kong tracks tokens through its rate limiting plugins with enforcement at the user or application level. LiteLLM tracks usage per virtual key with configurable budget caps. Helicone logs every request automatically with cost-per-user and cost-per-session breakdowns.

On the simpler end, Cloudflare provides aggregate analytics across your gateway. OpenRouter shows per-request costs but lacks team-level breakdowns. Apigee integrates with Looker Studio for custom reports. TrueFoundry provides request-level tracing across agent workflows.

Self-hosting options vary significantly. LiteLLM (MIT license), Helicone (open source), and Kong (OSS tier) can all run on your infrastructure. Portkey requires an Enterprise agreement for self-hosting. Cloudflare, OpenRouter, Apigee, and TrueFoundry are SaaS-only or require managed deployments.

For caching, Portkey and Kong offer both semantic and exact-match caching. Semantic caching uses embedding similarity to match rephrased versions of the same question, returning cached results even when the wording differs. Cloudflare provides edge caching through its global network. LiteLLM and Helicone support exact-match caching. OpenRouter, Apigee, and TrueFoundry either lack caching or handle it through their broader platform capabilities.

API gateways handle the request layer. But agents produce output that needs to go somewhere: reports, generated code, processed documents, analysis results. These artifacts need persistent storage that other agents and humans can access.

This is where the gateway layer meets the workspace layer. Your API gateway routes LLM calls and tracks costs, while a workspace like Fast.io handles what happens with the results. Agents can write outputs to shared workspaces through the Fast.io MCP server, where files are automatically indexed for semantic search via Intelligence Mode. Other agents or team members can then find and build on that work without manual file transfers.

The combination matters because agents rarely work alone. A research agent that calls GPT-4 through your API gateway might generate a report that a summarization agent needs next. If the research agent writes to a Fast.io workspace, the summarization agent can query the same workspace through the MCP server and retrieve exactly the files it needs. File locks prevent conflicts when agents work concurrently.

Fast.io's free agent tier includes 50GB storage, 5,000 credits/month, and 5 workspaces with no credit card required. The MCP server exposes 19 consolidated tools for workspace, storage, AI, and workflow operations. Agents manage files, run semantic queries, and transfer ownership to humans when the work is done.