Every team that puts more than one model behind an application eventually reinvents the same piece of infrastructure: a layer that sits between applications and model providers, so that no application has to know which provider is serving a request, what it costs, or what happens when it fails. Call it a proxy, a control plane, or — as the industry has settled on — an AI gateway. It looks like an API gateway from the outside, and early implementations largely were API gateways with an LLM provider bolted on. That no longer holds. LLM traffic breaks enough of the assumptions a normal API gateway is built on that a distinct category of infrastructure has formed around it.
Why LLM Traffic Doesn't Fit a Regular API Gateway
Four properties of LLM traffic don't show up in typical REST or gRPC workloads, and each one forces a different design decision in the gateway layer.
Streaming by default
Responses arrive token-by-token over minutes, not milliseconds — the gateway has to speak SSE and WebSockets natively, not just proxy a request/response cycle.
Token economics
Cost and load aren't request counts — a single call can burn anywhere from a few hundred to a few hundred thousand tokens, so metering has to happen inside the payload, not at the connection.
Non-deterministic output
The same input can produce a different — sometimes unsafe — output on every call, which regular schema validation was never built to catch.
Provider heterogeneity
Dozens of providers, each with its own API shape, rate limits, and failure modes, all need to look like one consistent endpoint to the calling application.
Everything that follows is a gateway's answer to one or more of those four problems.
The entry point: who gets in, what they're allowed to send, and how much of it.
Centralized Access
Every application talks to one endpoint instead of wiring direct SDK calls to OpenAI, Anthropic, Google, and whichever provider gets added next. That single choke point is what makes every other capability on this list possible — you can't enforce a budget, redact a payload, or fail over to a backup model on traffic the gateway never sees. A single production gateway routing to well over a hundred models across providers is now a fairly ordinary deployment, not an edge case.
Authentication & Authorization
Every request is verified before it's allowed anywhere near a model, and role-based policy decides not just who can call the gateway but which models, which data classifications, and which spend tier they're allowed to touch. In agentic systems this extends further: an agent acting on a user's behalf needs a scoped, delegated identity rather than a shared service credential, so that one compromised agent can't act with another user's full permissions.
Request Validation
Schema, payload size, and content get checked before anything reaches a model — malformed JSON, an oversized context dump, or a request missing required fields is rejected at the edge instead of burning inference cost on something that was always going to fail.
Rate Limiting
This is where AI gateways diverge sharply from API gateways: limiting by request count alone doesn't protect anything, because two requests from the same user can differ in cost by three orders of magnitude depending on context length. Production rate limiting has to combine request-count, token-count, and concurrency limits, scoped per user, per application, and per API key, so one runaway script can't consume an entire team's provider quota.
Where the spend actually goes, and how much of it can be avoided.
Cost Controls
Budgets, quotas, and usage limits are enforced before spend gets out of hand — not read off a bill afterward. That means real-time token-level tracking broken down by user, team, model, provider, and sometimes geography, so a finance owner can see exactly which application, and which prompt pattern, is driving the number up.
granular cost attribution: per user · team · model · providerIntelligent Routing
Requests get sent to the model actually suited to them — by cost, latency, capability, or plain availability — instead of hard-coding one model into application code. Strategies range from simple round-robin and least-busy routing to cost-weighted selection, where a provider charging several times more for the same capability gets proportionally less traffic. The payoff is also resilience: routing logic is what lets a gateway silently redirect traffic away from a provider that's degraded, before anyone downstream notices.
Response Caching
Frequent responses get reused rather than regenerated. Exact-match caching handles identical requests; semantic caching goes further, recognizing that "What's the capital of France?" and "Tell me France's capital city" are the same question and serving the cached answer for both. The latency difference is stark — a cache hit returns in single-digit milliseconds against several seconds for a live generation — and well-tuned semantic caches routinely cut both latency and inference spend by a meaningful double-digit percentage.
cache hit: <5ms · live inference: 2–5sWhat happens when something breaks, and how anyone finds out.
Observability
Every request, error, latency figure, usage number, and success rate gets tracked centrally — not scattered across each application's own logging. This is what turns "the model felt slow today" into an actual root cause: a specific provider, a specific model version, a specific spike in context length, visible in one trace.
Retries & Fallbacks
When a provider or model fails, the gateway recovers instead of the failure reaching the user. That typically means automatic retries with exponential backoff, a circuit breaker that stops sending traffic to a provider once its failure rate crosses a threshold, and layered fallback rules — a general fallback for timeouts, a separate one for content-policy rejections, and another for context-window overruns, since each failure mode calls for a different next step.
Protecting what flows through the gateway, and evolving it without breaking anyone downstream.
Privacy & Redaction
Sensitive data gets masked before a prompt ever leaves the organization's boundary — names, account numbers, credentials, health information — so a third-party model provider never sees what it doesn't need to. This capability has expanded well beyond redaction alone: gateways increasingly run inline prompt-injection detection and output filtering too, since content-aware attacks are the risk category a conventional API gateway's security model was never built to catch.
Encryption
Communication in transit and data at rest — logs, cached responses, audit trails — are protected the same way any other sensitive enterprise data would be, including meeting data-residency requirements that dictate which region a request, and its cached response, is allowed to be processed or stored in.
Versioning
Model and API changes roll out without breaking applications already depending on the previous behavior. A provider deprecating a model, or a team wanting to test a newer version against a small slice of traffic, becomes a routing-configuration change at the gateway rather than a coordinated redeploy across every consuming application.
Where the AI Gateway Fits Once Agents Enter the Picture
The twelve capabilities above describe a gateway sitting in front of model calls. Agentic AI systems complicate that picture, because an agent doesn't just call a model — it calls tools, queries data stores, and sometimes calls other agents, each of which needs its own governance. Recent enterprise infrastructure thinking has started describing this as a small family of specialized gateways rather than one gateway trying to do everything, tied together by capabilities that cut across all of them.
Unified model access — routing, caching, cost accounting, failover.
Controls which Model Context Protocol tools and servers an agent can discover and call — the piece that prevents ungoverned "shadow" tool access.
Governs agent runtime behavior itself — identity, execution limits, human-approval checkpoints.
Enforces access control and masking on the data an agent is allowed to query.
Makes sure a RAG pipeline respects document-level permissions, not just index-level access.
Inline inspection for prompt injection and data leakage across the whole request path.
The traditional layer, still handling the non-AI traffic these systems also depend on.
Three capabilities run horizontally across all of them rather than living in any single gateway:
The practical takeaway isn't that every organization needs seven gateways. It's that the AI gateway described in this article is the piece enterprises reach for first — because it's the one every LLM call passes through regardless of whether an agent is involved — and the natural anchor point the other, more specialized control points get built around as agentic adoption grows.
Choosing a Gateway
The landscape spans open-source proxies you run yourself to fully managed platforms, and the right choice depends more on team size and compliance posture than on any single feature checklist.
| Gateway | Model | Known for |
|---|---|---|
| LiteLLM | Open-source | Developer-first, OpenAI-compatible API in front of 100+ models; easy to self-host, needs hardening for regulated environments |
| Portkey | Commercial / SaaS | Prompt- and model-aware routing with strong token-level observability |
| Kong AI Gateway | Commercial / open-source | Traditional API gateway extended for AI; strong in Kubernetes and existing enterprise security stacks |
| Cloudflare AI Gateway | Managed, edge | Edge-cached responses; reports large latency reductions on cache hits |
| AWS Bedrock | Managed service | Serverless access to proprietary models; simplicity over long-run cost efficiency at scale |
| TrueFoundry | Commercial / SaaS | Enterprise control plane emphasizing lifecycle management and environment-based governance |
| Envoy AI Gateway | Open-source | Built on Envoy proxy; fits teams already standardized on Envoy/service-mesh infrastructure |
That crossover example is the shape of the decision, not a universal rule: below it, a managed gateway is usually cheaper once engineering time is counted; above it, or once data-residency requirements rule out sending traffic through a third party, self-hosting starts to pay for itself.
Teams rarely need all twelve capabilities on day one. Centralized access, authentication, and basic rate limiting are the floor — get those in place before a single application ships against a real model. Cost controls and retries/fallbacks are the next tier, worth adding the moment more than one provider is in play. Privacy, redaction, and fine-grained routing tend to arrive last, driven by compliance requirements and provider count rather than by raw traffic volume.
Closing Thought
An AI gateway earns its name by handling the things a regular API gateway was never asked to: metering by token instead of by request, absorbing a model provider's bad day without anyone downstream noticing, and inspecting content that changes on every call instead of validating a fixed schema. As agentic systems add tool calls, data access, and multi-agent coordination on top of plain model calls, the AI gateway doesn't go away — it becomes the first, most heavily used link in a small chain of purpose-built control points that governs how an enterprise's AI systems are actually allowed to act.