Author: Aron Schuhmann
Reviewer: Shane Thomas
AI gateway: one integration point for every provider
Your AI application probably talks to more than one model provider. You might put simple classification workloads on an older, cheaper model like GPT-4o, while using Claude Opus 4.8 for long-context reasoning. Maybe you want to use an open-source frontier model when the task involves sensitive data. Each provider has its own SDK, authentication scheme, rate limits, and response format. Managing that sprawl with direct integrations creates a maintenance burden that compounds with every new model you add.
An AI gateway solves this by giving you a single integration point in front of every provider. Most teams building with LLMs today use at least two model providers, and many rely on three or more.
This guide covers how gateways work under the hood, the features that matter, and how to implement one without overengineering it.
What is an AI gateway?
An AI gateway is a middleware layer that sits between your application and one or more large language model providers. It gives you a single integration point and a single API key for every provider. It intercepts outbound requests, applies routing logic, enforces policies, and normalizes responses before they reach your application code.
Core concept and how it fits into an AI stack
You can think of the gateway as the equivalent of an API gateway, but purpose-built for LLM traffic. Where a traditional API gateway manages REST or GraphQL endpoints, an AI gateway manages model calls. It handles provider-specific request formats, token-based rate limiting, prompt-level caching, and cost attribution that standard API gateways were never designed for.
In a typical AI stack, your gateway sits between your application layer and the providers. Your application might be a Next.js app, a Node service, or an agent pipeline. The providers might be OpenAI, Anthropic, Google, Mistral, or self-hosted models behind vLLM or Ollama.
Your code sends a standardized request to the gateway. The gateway translates it into the provider-specific format, forwards it, and translates the response back into a consistent schema.
LLM gateway vs. AI gateway: are they the same thing?
The terms overlap significantly, and in practice many vendors use them interchangeably. The distinction, when it exists, is scope. An AI gateway may handle traffic for vision models, speech-to-text, embedding endpoints, and traditional ML inference services in addition to LLMs. An LLM gateway focuses specifically on text-generation models and the primitives they require: token counting, prompt caching, streaming response handling, and tool-call routing.
For most TypeScript teams building agent-based applications, the practical difference is minimal. What matters is whether the gateway supports the providers and features your stack needs, not which label it uses.
The challenges of direct LLM API integration
Before exploring how LLM gateways work, it helps to understand what you run into without one.
Vendor lock-in and model fragmentation
When you integrate directly with a single provider, your prompt templates, retry logic, and response parsing are all coupled to that provider's API shape. Switching from OpenAI's chat completions format to Anthropic's messages API means rewriting integration code, updating error handling, and retesting every call site.
Multiply that by three or four providers, and you have a fragmentation problem that slows down model experimentation. Teams that want to compare outputs from GPT-4o, Claude, Gemini, and Llama-hosted models end up maintaining four different integration paths. Vendor lock-in is not just a migration risk. It slows down your day-to-day iteration on prompt engineering and model selection.
Cost unpredictability and lack of visibility
Each provider bills differently. Some charge per input and output token separately, others bundle them, and pricing changes frequently. Without a centralized layer tracking token usage across providers, you often discover cost overruns in monthly invoices rather than in real time. A single runaway prompt loop can burn through thousands of dollars before anyone notices.
Security and compliance gaps
Direct integrations scatter API keys across services and repositories. Each integration needs its own audit trail for data flowing through the model. If you are operating under SOC 2, HIPAA, or GDPR requirements, auditing five separate provider integrations is five times the compliance work.
Reliability and rate-limit handling
Every LLM provider has rate limits, and they enforce them differently. OpenAI uses per-minute token and request caps. Anthropic has its own tier system. When one provider throttles your requests or goes down entirely, direct integrations fail unless you have built custom fallback logic for each provider individually.
How an AI gateway works
When your application makes a model call through a gateway, the request passes through a series of layers before it reaches a provider.
Request routing and provider abstraction
Your gateway receives a standardized request and decides where to send it. Routing decisions can be based on model name, cost thresholds, latency targets, task type, or custom policies you define. The gateway translates your request into the target provider's format, so your application code never handles provider-specific schemas.
Middleware layers: auth, rate limiting, and caching
Before your request reaches a provider, the gateway applies middleware:
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Authentication and authorization: validate API keys or tokens, check role-based access control (RBAC) policies, and reject unauthorized requests before they consume any provider tokens.
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Rate limiting: enforce per-user, per-team, or per-project rate limits independent of the provider's own limits. This protects your budget and prevents noisy neighbors in multi-tenant environments.
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Caching: match incoming prompts against cached responses. Semantic caching goes further by matching prompts that are similar but not identical, returning cached results when the semantic distance is below a threshold.
Response handling and fallback logic
The provider's response is normalized into your gateway's standard format. If the primary provider returns an error, times out, or hits a rate limit, the gateway automatically retries against a fallback provider. This failover happens transparently, so your application receives a response without needing to know which provider ultimately served it.
The entire round trip typically adds 3 to 10 milliseconds of overhead, based on benchmarks published by LiteLLM and Portkey for self-hosted proxy configurations.
Key features to look for in an AI gateway
Your requirements will evolve as your usage scales, but these are the capabilities worth evaluating from the start. The table below covers the six that matter most, what each one does, and what you lose if you skip it.
| Feature | What it does | Why it matters |
|---|---|---|
| Multi-provider routing and load balancing | Routes requests across providers with configurable strategies: round-robin, cost-optimized, latency-optimized, or weighted | Prevents single-endpoint bottlenecks; lets self-hosted Llama and open-weight models sit alongside OpenAI and Gemini without special-casing |
| Unified API and SDK interface | Exposes an OpenAI-compatible endpoint that translates to each provider's native format automatically | Your application code stays unchanged when you add a provider or swap models; ChatGPT-based tooling and the OpenAI SDK work out of the box |
| Cost tracking and budget controls | Per-request token counting and cost attribution by team, project, or user, with real-time budget caps and alerts | Catches runaway costs before the end-of-month invoice; gives finance and eng teams the same view of token usage |
| Access control and audit logs | RBAC policies controlling model access, with full request/response logs including timestamps, user identity, token counts, and latency | Required for SOC 2, HIPAA, and GDPR compliance; essential for debugging production failures |
| Semantic and prompt caching | Exact-match caching for identical prompts; semantic caching for near-duplicates using embedding distance | Reduces redundant provider calls and cuts costs for workloads with high prompt similarity, without any change to application logic |
| Guardrails and content filtering | Pre-request scans for PII, banned content, and prompt injection; post-response filtering for policy violations | Enforces content policies consistently across every provider from a single middleware layer |
When to use a gateway vs. direct integration
The comparison table below breaks down the tradeoff across seven dimensions. The right column wins as soon as you move past a single provider, need fallback logic, or need to track costs across teams. If you are using one provider for one use case with no plans to change, direct integration keeps things simpler.
AspectDirect API integrationLLM gatewaySetup complexityLow for one provider, grows linearly per providerHigher initial setup, constant marginal cost per providerProvider switchingRequires code changes per call siteConfiguration change at the gatewayFallback and retryCustom logic per providerBuilt-in, policy-drivenCost visibilityPer-provider dashboards, manual reconciliationCentralized, real-time, attributed by team or projectAccess controlManaged per integration, inconsistentUnified RBAC, single policy layerLatency overheadNone (direct call)3 to 10 ms additionalMaintenance burdenGrows with each provider addedStays constant as providers scale
Common AI gateway use cases
You will likely reach for an LLM gateway at one of these inflection points, depending on how your team and product are scaling.
Production AI applications and agent pipelines
If you are running agents that call tools, chain model calls, and iterate across multiple steps, you are generating high volumes of LLM requests. A gateway gives you centralized tracing across those calls, automatic fallback when a provider degrades, and cost attribution per agent run rather than per provider invoice. For generative AI applications with real users, that observability is not optional. It is how you debug failures at 2am without reading raw API logs across three providers.
Enterprise governance and policy enforcement
If your team operates in a regulated industry, you need consistent policies across all model interactions: PII filtering, data residency controls, model access restrictions by department. A gateway enforces these policies at a single chokepoint rather than requiring each team to implement them independently. Retrieval-augmented generation pipelines present the same challenge at the data layer. A gateway that logs every retrieval call alongside every generation call gives your compliance team a complete picture.
Multi-tenant SaaS platforms
If you are building a product where your customers trigger LLM calls, chatbots, document summarizers, or AI-assisted workflows, you need per-tenant rate limiting, cost tracking, and isolation. A gateway gives you these controls without building them into your application layer. Chatbot-heavy products in particular generate the kind of high-frequency, repetitive traffic where semantic caching pays for itself quickly.
Cost optimization across teams and projects
When your teams share a pool of LLM providers, you can use a gateway to route lower-priority requests to cheaper models and reserve expensive models for tasks that need them. Budget caps per team prevent any single group from exhausting shared resources.
Monitoring, observability, and debugging your AI gateway
You need visibility into what is flowing through your gateway, how it is performing, and where things break. Without observability, a gateway is a black box.
Traces, metrics, and request-level visibility
Every request through your gateway should produce a trace: the full lifecycle from incoming request to provider call to response. Key metrics include time-to-first-token, total latency, token counts (input and output), cache hit rate, and error rate per provider. These metrics feed dashboards that let you spot degradation before it affects users.
Evals and output quality guardrails
Monitoring latency and errors tells you whether the gateway is healthy, but not whether the model outputs are good. You need evals: automated checks that score output quality against criteria you define. Eval hooks and tracing let you measure regressions when you change a prompt, swap a model, or adjust routing weights.
Alerting on latency, cost spikes, and error rates
You should set alerts for the signals that matter most: p95 latency exceeding your SLA, hourly cost exceeding a budget threshold, error rate crossing a percentage floor, or a specific provider failing repeatedly. Alerting closes the loop between observability data and human action.
Popular AI gateway solutions compared
Your options range from open-source proxies you self-host to fully managed platforms with enterprise features. Here is how the most widely adopted solutions break down.
Open-source options: LiteLLM, Portkey, and others
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LiteLLM: a widely used open-source proxy that provides an OpenAI-compatible interface for 100+ providers. It handles routing, fallback, and basic cost tracking. LiteLLM is a strong choice if you want a lightweight gateway you can self-host and extend. You can run it as a Docker container alongside your application, which makes it straightforward to add to an existing deployment without rearchitecting anything.
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Portkey: offers an open-source gateway core with features like semantic caching, guardrails, and detailed analytics. Portkey's managed offering adds enterprise features including team-based access controls and budget management. Its routing logic supports task-type classification using NLP, directing requests to different providers based on inferred intent, useful when your application handles a wide range of natural language processing workloads where different models perform differently by task category.
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MLflow AI Gateway (formerly MLflow Deployments): integrates with the MLflow platform and provides a unified endpoint for model serving and LLM routing. Best suited if you are already using MLflow for experiment tracking.
Managed and enterprise-grade gateways
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OpenRouter: a managed gateway that aggregates hundreds of models across dozens of providers through a single interface. It handles routing, pricing normalization, and fallback across providers. OpenRouter is a strong choice if you want managed model access without self-hosting infrastructure.
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Vercel AI Gateway: integrates directly with Vercel's deployment platform and provides model routing, provider failover, and cost tracking for AI applications deployed on Vercel. A natural fit if your frontend and API layers already run on Vercel.
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Cloudflare AI Gateway: runs at the edge across Cloudflare's global network, adding caching, rate limiting, and observability to your AI model calls. It sits in front of any provider and works well if you already use Cloudflare for CDN or Workers.
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AWS Bedrock and Azure AI Services: cloud-native options that provide model routing within their respective cloud platforms. They work well if your infrastructure is already consolidated on one cloud, but they create lock-in to that cloud provider's model catalog.
How to evaluate them for your stack
When you are comparing AI gateways, focus on these criteria:
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Provider coverage: does it support the models you use today and the ones you are evaluating?
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Latency overhead: measure added latency under your actual traffic patterns, not just vendor benchmarks.
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Extensibility: can you add custom middleware, routing rules, or guardrails without forking the codebase?
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Deployment model: does it run where your infrastructure lives (self-hosted, cloud, edge)?
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Cost: factor in both the gateway's own pricing and the cost savings it enables through caching, routing, and budget controls.
Best practices for implementing an AI gateway
You can get a gateway running quickly, but making it production-grade takes deliberate choices early on.
Start with a unified API layer before adding features
Your first goal is a single API that abstracts away provider differences. Get that working, tested, and deployed before layering on caching, guardrails, or advanced routing.
Define routing and fallback policies early
Do not wait for a provider outage to discover you have no fallback logic. Define your primary and secondary providers per model class, set timeout thresholds, and test failover behavior before you need it in production. Foundation models from different providers have meaningfully different latency profiles. Routing time-sensitive tasks to faster models is a policy decision worth encoding from day one, not a manual override you add when latency starts spiking.
Enforce cost budgets and access controls from day one
Retroactively adding budget caps after a cost overrun is painful. Set per-team or per-project budgets during initial rollout, even if the limits are generous. The same applies to RBAC: defining who can access which models upfront prevents ad-hoc key sharing and audit gaps. Vector database calls and embedding generation run through the same gateway infrastructure and benefit from the same budget and access controls. Account for those when you size your initial budgets.
Test gateway behavior with synthetic and production traffic
Run synthetic load tests that simulate your production traffic patterns: concurrent requests, mixed model calls, and provider failures. Shadow production traffic through your gateway before cutting over fully. Compare output quality between your current direct integrations and the gateway path before you switch.
AI gateway capabilities on Mastra
Mastra workflow with agent and tool steps
If your stack is TypeScript, Mastra gives you AI gateway capabilities built directly into its agent framework. Instead of deploying and managing a separate gateway service, you get model routing, provider abstraction, and fallback logic as part of the same platform where you build agents and workflows.Mastra's model routing connects to hundreds of models across dozens of providers through a single interface.
You configure your providers once, and Mastra handles the translation between your application's standardized calls and each provider's API format. Switching from one model to another is a configuration change, not a code rewrite. This is the core value of an AI gateway, and Mastra delivers it without requiring you to run a proxy or middleware layer alongside your application.
Beyond routing, Mastra's platform includes the observability and control features you would expect from a standalone gateway. You get cost tracking across providers, structured logging for every model call, and the ability to set policies that govern how your agents interact with different models. Because these capabilities live inside your application framework, they share context with your agent logic, your workflow state, and your tool integrations.
You can chain agent steps into durable workflows with storage-backed state that persists across suspensions. Mastra also exposes MCP server support, so your agents can connect to any MCP-compatible tool alongside your model routing setup. You deploy to Vercel, Netlify, Cloudflare Workers, or a standalone Hono server without rewriting your gateway or agent logic.
Get started with Mastra to see how AI gateway capabilities and workflow orchestration fit into your stack.
The future of AI gateways
If you are planning your gateway architecture today, these three trends will shape what you need from it over the next 12 to 18 months.
Agentic AI and multi-agent orchestration demands
As your agents become more autonomous, calling tools, spawning sub-agents, and making multi-step decisions, your gateway needs to handle more complex traffic patterns. A single agent run might generate dozens of model calls across different providers. You will need your gateway to trace, rate-limit, and attribute costs at the agent-run level, not just the individual-request level.
Data residency and sovereignty
Many teams have a strong preference for data sovereignty and want to control where model calls are processed and where logs are stored. Future AI gateway implementations will need built-in data residency controls, letting you route requests to providers or regions that meet your requirements without changing application code. Teams operating under HIPAA will face this directly as more healthcare workflows incorporate generative AI and large language models at scale.
KV cache routing and inference-layer optimizations
If you are running conversational workloads, KV cache routing is an optimization worth watching. Your gateway routes follow-up requests to the same inference endpoint that holds the key-value cache from the previous turn, avoiding recomputation and reducing time-to-first-token. Standard load balancers break this pattern because they distribute requests without considering cache affinity. Self-attention mechanisms in transformer-based models make the KV cache especially valuable for long conversations.
Recomputing it on every turn wastes both compute and latency budget. Neural network inference frameworks like vLLM expose cache-aware routing APIs that gateways are beginning to support. This is where LLM infrastructure is converging with deep neural network serving concerns, and the gateway layer is where that optimization will eventually live.
Wrapping up
An AI gateway gives you a single control plane for model routing, cost tracking, access control, and observability across every provider your team uses. Start with the unified API layer, add fallback policies and budget controls early, and layer in caching and guardrails as your traffic patterns become clearer.
