An AI gateway is the centralized control plane that sits between your applications and the large language models, agents, and tools they consume. It handles authentication, request routing, fallback, observability, cost tracking, and policy enforcement — the same role an API gateway plays for traditional REST traffic, but rebuilt for the very different shape of LLM workloads.
An AI gateway is enterprise infrastructure that unifies access to every LLM, MCP and Agent through a single API. It standardizes authentication, governance, cost controls, observability, and routing so engineering teams can ship AI features faster while security, finance, and compliance stay in control. |
If you have ever used a REST API gateway like Kong, Apigee, or AWS API Gateway, the analogy is straightforward. A REST gateway provides one address for hundreds of microservices, sets rate limits per consumer, and emits request logs for the platform team. An AI gateway does the same thing for OpenAI, Anthropic, Google Gemini, AWS Bedrock, VertexAI, Azure, Mistral, your fine-tuned models, your in-house models, and the agents and tools wired around them.
The reason “AI gateway” has overtaken “model gateway” as the standard term is that the job is broader than routing. Enterprise teams need to enforce data residency, mask PII before it leaves the building, give finance per-team budgets, give security audit trails, give product teams self-serve virtual keys, and give SREs latency-aware load balancing. None of that fits inside the word “router.” An AI gateway is the layer where governance, security, observability, and developer experience meet.
LiteLLM ships this layer as an open-source AI gateway. You can read the product overview on the LiteLLM AI Gateway page or jump to the docker quick start to spin one up locally.
AI Gateway vs. API Gateway: What's the Difference?
Generic API gateways were built for predictable REST traffic: small JSON payloads, deterministic responses, integer rate limits, and a fixed schema per endpoint. LLM workloads break almost every one of those assumptions. The table below shows where the two diverge.
Capability | Traditional API Gateway | AI Gateway |
|---|---|---|
Billing unit | Requests per second | Tokens, requests, and cost per model |
Model routing | Not aware of models | Provider, region, and version-aware routing with fallback |
Streaming | Limited or proxied as raw bytes | First-class SSE handling and partial-token logging |
Prompt handling | Treats prompts as opaque bodies | Prompt caching, redaction, templating |
Guardrails | Schema validation only | PII masking, jailbreak detection, content policies |
Cost controls | None | Per-team, Per-user, per-key, and per-tag budgets with alerts |
Observability | Status codes and latency | Token usage, eval scores, traces to Datadog, OpenTelemetry and more. |
Stretching a generic API gateway to cover LLM traffic usually fails in three places. Token-level billing has to be reconstructed from response bodies. Streaming responses break middleware that buffers requests. And policy enforcement on prompts requires LLM-aware logic, not regex on a JSON path. By the time those gaps are patched, teams have effectively re-implemented an AI gateway, just without the open-source community behind it.
For a deeper side-by-side, see our cluster article: AI Gateway vs API Gateway — What’s the Difference?.
How Does an AI Gateway Work?
Think of an AI gateway as one HTTPS endpoint that fronts every model, agent, and tool in your stack. Applications send a single OpenAI-style request to the gateway. Inside, the gateway authenticates the caller, applies guardrails, picks the right model and region, attaches the right credentials, streams the response back, and writes a complete usage record to your observability stack.
Where it fits in the AI stack
An AI gateway lives between your application layer and the providers. Above it sit your apps, agent frameworks, RAG pipelines, and copilots. Below it, sit OpenAI, Anthropic, Bedrock, Vertex AI, your fine-tuned endpoints, your vector stores, and any tools your agents call. The gateway abstracts all of that into one address with one auth scheme.
A concrete request flow
Imagine a customer-support copilot that needs to answer a billing question. The flow looks like this:
Client sends a chat-completion request to the gateway with a virtual key.
Gateway authenticates the key, checks RBAC, and validates the budget for the team it belongs to.
Guardrails redact PII, scan for jailbreak patterns, and enforce data-residency policies.
The router selects a primary model (say, GPT-4o in eu-west-1), with Anthropic Claude as the fallback if GPT-4o is rate-limited or down.
If the agent needs to call a tool, the gateway forwards the tool call through MCP, then merges the tool result back into the model context.
Streaming tokens are proxied to the client as they arrive, while the gateway records token counts, cost, latency, and trace IDs.
On completion, the gateway writes a structured log to Datadog, OpenTelemetry, S3, or any of the other supported sinks.
The important detail is that auth, policy, and PII checks run on every step — including any tool or agent called on the gateway routes downstream. Governance is enforced once, in one place, on the way in and on the way out. The gateway is an orchestration and governance layer, not just a router.
Key Features of AI Gateways
Most AI gateways converge on a similar core feature set, even if naming and depth vary. Here is what to look for, with notes on how LiteLLM implements each one.
1. Unified API across 100+ providers
A unified, OpenAI-compatible API is the table-stakes feature. LiteLLM normalizes 100+ providers — OpenAI, Anthropic, AWS Bedrock, Google Vertex, Azure, Cohere, Mistral, DeepSeek, Together, Fireworks, Groq, and many more — onto a single /chat/completions surface. Switching from one provider to another is a config change, not a code rewrite. See the full list in the provider docs.
2. Authentication and authorization
Enterprise auth means more than an API key. Modern gateways support SSO via OIDC and SAML, JWT validation against your identity provider, SCIM for provisioning, and fine-grained RBAC mapped to teams, projects, and environments. Virtual keys layer on top so a single end user, app, or CI job can be issued a scoped credential without touching upstream provider keys.
3. Credential management
The gateway holds upstream provider credentials in encrypted storage. Application teams never see the underlying OpenAI or Anthropic keys, which means a leaked virtual key can be revoked in seconds without coordinating a rotation across every provider account.
4. Model routing, load balancing, and fallback
The router is where AI gateways earn their keep. LiteLLM’s router supports weighted load balancing, latency-aware routing, usage-based routing, deployment health checks, and automatic fallback chains — so a 429 on GPT-4o quietly retries on a parallel deployment or a backup model without the client noticing. The full routing model is documented in the routing and load-balancing guide.
5. Observability and analytics
Every request emits a structured event with token counts, cost, latency, model, and trace context. LiteLLM ships native integrations with Datadog, OpenTelemetry, Langfuse, Arize, S3, Prometheus, and a dozen others. The admin UI shows spend per team, per key, per model, and per tag, so finance and engineering see the same numbers.
6. Caching and prompt management
Prompt caching cuts repeat-prompt latency from seconds to milliseconds and chops cost by an order of magnitude on heavy RAG workloads. LiteLLM supports Redis, in-memory, S3, and disk caches with per-route configuration; see the caching docs for setup.
What Is an AI Gateway Used For?
In production, an AI gateway is doing five jobs at once: it controls cost, enforces safety, gives platform teams a single integration target, governs how data leaves the company, and lets engineers wire up agents and tools without bespoke plumbing for every provider. Below are the day-to-day jobs that turn into checklist items in any enterprise AI rollout.
Benefits of an AI Gateway
Integrating an AI gateway bridges the gap between local experimentation and enterprise-grade production. The primary benefits include:
Vendor Lock-In Prevention: Gateways provide a unified, standardized API. This allows engineering teams to swap out underlying models (e.g., switching from OpenAI to Anthropic or an open-source model) seamlessly without rewriting application code.
High Availability and Reliability: With built-in features like load balancing, automatic retries, and fallback routing, a gateway ensures that if your primary AI provider experiences an outage or rate limit, traffic is instantly rerouted to a backup model so the end-user experiences zero downtime.
Enhanced Security and Compliance: Gateways act as a centralized firewall for AI traffic. They can detect and redact Personally Identifiable Information (PII) before it ever leaves your corporate network, ensuring compliance with data privacy regulations (like GDPR or HIPAA).
Centralized Observability: Instead of piecing together logs from multiple providers, an AI gateway offers a single dashboard to monitor token consumption, latency, error rates, and user interactions. This makes auditing and debugging significantly easier.
Cost Optimization (Caching): Through semantic caching, gateways can recognize when a user asks a question similar to one that was recently answered. It serves the cached response instantly, reducing latency for the user and saving the business the token cost of generating a brand-new response.
Rate limits and budget controls
Without a gateway, cost overruns are inevitable. Engineers move fast, models change pricing, a prompt template regression turns a one-token answer into a thousand-token answer, and the bill spikes. An AI gateway lets you set rate limits in requests per minute, tokens per minute, and dollars per day — per virtual key, per team, per model, per tag. Hard ceilings stop runaways. Soft ceilings page the right team.
Prompt caching to cut latency and cost
Caching is one of the highest-leverage features. For RAG, agents, and copilots that repeat similar prompts, the gateway returns cached completions in milliseconds. On Anthropic and Google, prompt caching also unlocks the providers’ own pricing discounts. Teams routinely cut blended LLM cost 30–60% by enabling caching at the gateway.
Guardrails: PII masking, content filters, safety policies
Guardrails plug in at the gateway and run on every request. PII detectors mask names, emails, phone numbers, SSNs, and custom regex patterns before prompts reach a third-party provider. Content filters from Lakera, Aporia, Pillar Security, Presidio, and Bedrock Guardrails plug in with a few lines of YAML. The same hooks fire on responses, so a model that hallucinates a customer record gets caught before it reaches the end user.
Virtual keys and RBAC
Virtual keys are the unit of access in a mature AI gateway. Each one is a short-lived, scoped credential bound to a team, a list of allowed models, a budget, and an expiry. RBAC layers on roles — admin, internal user, customer, agent — so you can grant a third-party SaaS partner read-only access to one model and a five-dollar daily ceiling without ever sharing your real provider key.
MCP gateway for agent tools and context
Agents need tools, and tools need governance. The Model Context Protocol (MCP) standardizes how agents discover and call tools — file systems, search APIs, internal databases, third-party SaaS. An MCP gateway routes tool calls through the same auth, audit, and policy layer that handles model calls. LiteLLM was the first enterprise AI gateway to ship native MCP support, with permissions, allow-lists, and full audit logs on every tool invocation. Read more in our companion piece: What is an MCP Gateway?.
Agent gateway for agent-to-agent traffic
As agentic systems mature, individual agents start calling other agents. The Agent2Agent (A2A) protocol describes that traffic, and an agent gateway routes it the same way an API gateway routes microservice traffic. LiteLLM exposes a native /a2a endpoint and ties agent calls into the same observability and budgeting plane as model calls. See the A2A docs and our deep-dive: What is an Agent Gateway?.
Common usage patterns
In the field, AI gateway adoption clusters around a handful of repeatable patterns: standing up an internal “ChatGPT for the company” backed by multiple providers; powering customer-facing copilots with strict cost ceilings per tenant; multiplexing eval and offline jobs across the cheapest available model; routing agent tool calls through MCP with audit logs that satisfy SOC 2 reviewers; and giving research and data-science teams a sandbox where every spend dollar is tagged to a project.
Stop Retrofitting Risk: The Case for Proactive AI Governance
The most expensive AI failures in 2024 and 2025 were not technical — they were governance failures. A chat copilot that leaked PII through a prompt. A budget that disappeared into a stuck retry loop. A model that was deprecated by the provider with two weeks’ notice and broke a customer-facing flow. Every one of those incidents is preventable, but only if governance is wired into the request path from day one.
Bolting governance later does not work. Once a hundred services have hard-coded a provider SDK and a key, getting them to route through a gateway becomes a multi-quarter migration. The cheap moment to standardize is the day the second team starts using LLMs.
Audit logs, residency, SSO, SCIM
A gateway-first architecture gives you four things compliance teams ask for in every review:
Audit logs of every prompt, every response, every tool call, every key used, with stable trace IDs.
Data residency controls that pin requests to providers and regions allowed for a given team.
SSO via OIDC and SAML so access is tied to your identity provider and de-provisioning is one offboarding ticket.
SCIM provisioning so users, groups, and roles flow automatically from your IdP into the gateway.
Compliance coverage
LiteLLM is SOC 2 Type II certified by Vanta, supports HIPAA-aligned deployments, and offers GDPR controls. The enterprise documentation lays out the controls available out of the box and the additional configurations available to enterprise customers.
Air-gapped and self-hosted deployments
For teams in regulated industries or sovereign environments, the gateway has to run inside the perimeter. LiteLLM is MIT-licensed, fully self-hostable, and supports air-gapped deployments with no outbound calls to a SaaS control plane. Run it on Kubernetes, Docker, Nomad, or a single VM — the binary is the binary.
What this means for CISO sign-off
A CISO’s job is to see, control, and revoke. An AI gateway gives all three by default: visibility through structured logs, control through RBAC and budgets, and revocation through one-click virtual key invalidation. That is the difference between a multi-month security review and a same-week green light.
Why Enterprise Teams Choose an AI Gateway
Two things drive enterprise adoption in 2026: cost discipline and risk discipline. The teams that win with LLMs are the teams that can move quickly without surprising finance, legal, or security.
Security and compliance benefits
Centralizing the request path collapses your audit surface. Instead of monitoring every microservice, your security team monitors one ingress. Instead of rotating keys across every provider account whenever a developer leaves, they revoke the virtual keys associated with that developer in the gateway. Instead of explaining to a customer why their PII briefly transited a foreign region, the policy that prevented it is documented and provable in logs.
Cost control and operational efficiency
With a gateway, finance gets per-team and per-project budgets, real-time spend telemetry, and alerting before the bill arrives. Engineering gets caching, batching, and routing decisions that quietly trim costs without code changes. The combined effect is reliable, double-digit cost reductions on the same volume of traffic.
Standardized access for engineering
New teams onboard in hours, not weeks. Pick a model, get a virtual key, ship. The gateway handles credentials, retries, fallbacks, and observability. Teams stop building their own retry libraries and their own provider abstraction layers, which means more time on actual product work.
Compliance without slowing development
Because policies live at the gateway, security and compliance teams can update controls without waiting for every downstream service to redeploy. Add a new PII rule, tighten a residency policy, deprecate an old model — the change takes effect on the next request.
Potential pitfalls and how to avoid them
Single point of failure: Run the gateway in HA mode across at least two zones, and make sure your routing policy includes provider-level fallback.
Vendor lock-in: Choose an MIT-licensed, OpenAI-compatible gateway you can self-host. Walking away should be a config change, not a rewrite.
Hidden latency: Look at P95 numbers, not averages. Production-grade gateways add a low-single-digit-millisecond overhead at high throughput — LiteLLM benchmarks at 8 ms P95 at 1k RPS.
Sprawling policy: Treat gateway config as code. Version it, review it, ship it through CI — just like the rest of your platform.
Proof points
LiteLLM is used in production at Netflix, Stripe, Lemonade, Rocket Money, OpenHands, Greptile, Google’s ADK, and federal agencies. The project has crossed 1B+ requests served, 240M+ Docker pulls, and 41,800+ GitHub stars at the time of writing — making it the most widely adopted open-source AI gateway. (Verify the latest counts at litellm.ai before publishing.)
Beyond LLM Routing: MCP, Agent Gateway, and Where They Fit
“AI gateway” is the umbrella term, but the ecosystem has produced a handful of narrower terms that describe specific slices of the same problem. Knowing how they relate keeps RFPs and architecture docs from talking past each other.
LLM gateway
An LLM gateway focuses on routing and standardizing model calls. It is the narrowest term — it usually does not include MCP, A2A, or full governance features. Most LLM gateways are a subset of an AI gateway. See the cluster article on LLM gateways for more.
LLM proxy
An LLM proxy is a transport-layer predecessor to a full AI gateway: it forwards requests, maybe rewrites a few headers, and that is it. Useful as a stop-gap, but it stops short of governance, virtual keys, and observability. Read more: LLM proxy explained.
MCP gateway
An MCP gateway routes tool calls and context for agents using the Model Context Protocol. It is the agentic counterpart to an API gateway: discoverable tools, scoped permissions, and audit trails. LiteLLM ships MCP support natively. See What is an MCP Gateway?.
Agent gateway
An agent gateway routes traffic between agents over the A2A protocol. As multi-agent systems become production-grade, this layer matters as much as the model layer. See What is an Agent Gateway?.
API proxy
A general-purpose API proxy forwards HTTP requests but is not LLM-aware: no token billing, no streaming-aware logging, no prompt-aware policy. Often where teams start, rarely where they end up. More: API proxy explained.
These layers overlap. A modern AI gateway like LiteLLM is the superset — it handles model calls, MCP tool calls, A2A agent calls, and governance, all from one control plane. That is why “AI gateway” is the term that survives in enterprise architecture diagrams.
How to Get Started with LiteLLM AI Gateway
Getting from zero to a running gateway takes minutes, not days. Here is the fastest path.
Step 1: Install via pip or Docker
Install the proxy with pip:
pip install ‘litellm[proxy]’
Or pull the official Docker image:
docker pull ghcr.io/berriai/litellm:main-latest
Step 2: Configure providers and API keys
Drop your provider keys into a config.yaml or environment variables. A minimal config wires up two providers as a fallback pair:
model_list: - model_name: gpt-4o litellm_params: model: openai/gpt-4o api_key: os.environ/OPENAI_API_KEY - model_name: claude-fallback litellm_params: model: anthropic/claude-3-5-sonnet-latest api_key: os.environ/ANTHROPIC_API_KEY
Step 3: Make your first call
Start the proxy on localhost:4000 with litellm --config config.yaml, then point any OpenAI client at it. No code changes, drop-in compatible.
Step 4: Set up Virtual Keys, RPM limits, and team management
Open the admin UI at /ui, create teams, issue scoped virtual keys, and set RPM, TPM, and dollar budgets per key. Wire SSO, configure guardrails, and route logs to your observability stack. The Docker quick start walks through the full sequence.
Step 5: Deploy to Kubernetes for production
LiteLLM ships Helm charts, Kustomize manifests, and a Postgres-backed control plane for production deployments. Run it air-gapped, behind a load balancer, with horizontal pod autoscaling. Most teams go from local laptop to a Kubernetes cluster serving real traffic in under a day.
Choosing the Right AI Gateway for Your Stack
There is no single right answer — the trade-offs depend on how regulated your environment is, how much LLM traffic you are pushing, and how big your platform team is.
Open Source vs. Managed
Open-source gateways win on cost, control, and the ability to fork. Managed gateways win on time-to-first-call. The middle path — open source with optional managed support — is where most enterprises land in 2026, because a forked or pinned binary is the only thing that survives a procurement review for sensitive workloads.
Self-Hosted vs. Cloud
Self-hosted is the default for regulated industries: financial services, healthcare, defense. Cloud-hosted is faster to start. The right answer is the one that lets your CISO say yes; everything else is a lower-order optimization.
Compare the field
For a side-by-side of LiteLLM, Portkey, Kong AI Gateway, Cloudflare AI Gateway, and OpenRouter — see our comparison: Best AI Gateways in 2026 — Compared. And on the deployment-model trade-off, see Self-Hosted AI Gateway vs. Cloud.
Trends in AI gateways
Three trends are shaping the gateway category in 2026. First, MCP is becoming the default protocol for agent tools, and gateways without MCP support are quickly being treated as legacy. Second, A2A is doing the same for agent-to-agent traffic. Third, governance and FinOps features — not raw routing performance — are becoming the basis for selection. Routing is solved; control planes are not.
LiteLLM: The Open-Source AI Gateway
LiteLLM is MIT-licensed, self-hostable, supports 100+ models, and is trusted in production by Netflix, Stripe, Lemonade, OpenHands, Google’s ADK, and federal agencies. It runs on Kubernetes, Docker, or a single VM, supports air-gapped deployment, and ships with virtual keys, guardrails, MCP, A2A, and a full observability stack out of the box.
Start for free · Read the docs · Book a demo
Frequently Asked Questions
What is LiteLLM AI Gateway?
LiteLLM is an open-source, MIT-licensed AI gateway that gives engineering teams a single OpenAI-compatible API across 100+ LLM providers, with built-in virtual keys, budgets, guardrails, MCP support, and observability. It can be used as a Python SDK or deployed as a self-hosted proxy server.
What are the main benefits of using an AI gateway?
Unified access to every model, centralized cost and rate-limit controls, enforced governance and PII masking, faster onboarding for new teams, and a single observability surface for token usage, latency, and spend. In short: faster shipping with fewer surprises.
What is the difference between an AI gateway and an API gateway?
An API gateway routes generic REST traffic and bills in requests. An AI gateway routes LLM traffic and bills in tokens, handles streaming and prompts as first-class concepts, enforces LLM-specific guardrails, and supports model-aware routing and fallback. The two are complements, not substitutes.
What is the difference between an AI gateway and an LLM proxy?
An LLM proxy forwards model traffic at the transport layer. An AI gateway adds governance, virtual keys, observability, MCP and A2A support, and policy enforcement. Most LLM proxies grow into AI gateways over time.
Why do organizations need an AI gateway now?
LLM workloads are now mission-critical, multi-provider, and tightly regulated. Without a gateway, teams duplicate auth, blow budgets, leak PII, and cannot prove compliance. With one, governance lives in a single place and engineers ship faster.
What is an MCP gateway?
An MCP gateway routes tool calls and context for AI agents using the Model Context Protocol. It applies the same auth, audit, and policy controls to tool calls that an AI gateway applies to model calls. LiteLLM was the first enterprise AI gateway with native MCP support.
What is an agent gateway?
An agent gateway routes traffic between agents using the Agent2Agent (A2A) protocol. It is the governance and observability layer for multi-agent systems, so an agent calling another agent goes through the same policies as a model call.
How does an AI gateway improve AI security and compliance?
By centralizing auth, audit logs, PII masking, residency enforcement, and key rotation in a single component. Security teams monitor one ingress, not dozens of services. Compliance teams get one place to point auditors.
What role does an AI gateway play in AI governance?
It is the enforcement point. Budgets, model allow-lists, residency rules, redaction policies, and access controls are configured once and applied to every request, every tool call, and every agent call.
Is an AI gateway open source?
Some are, including LiteLLM (MIT-licensed). Others are commercial-only. For regulated and sensitive workloads, an open-source, self-hostable gateway is usually the only option that survives a security review.
Does an AI gateway add latency to LLM calls?
A well-engineered AI gateway adds low-single-digit-millisecond overhead. LiteLLM benchmarks at 8 ms P95 at 1k RPS — a small fraction of the LLM’s own response time, and usually offset by caching gains.
What is the best open-source AI gateway in 2026?
LiteLLM is the most widely adopted open-source AI gateway, used in production by Netflix, Stripe, Lemonade, federal agencies, and others, with 41,800+ GitHub stars and 240M+ Docker pulls. For a side-by-side, see our “Best AI Gateways in 2026” comparison.
Can an AI gateway work with OpenAI-compatible APIs?
Yes — a strong AI gateway exposes an OpenAI-compatible API by default, so any OpenAI SDK or tool works without code changes. LiteLLM does this for 100+ providers.
What are the potential risks of deploying an AI gateway?
Single point of failure if you do not run HA, hidden latency if you pick the wrong implementation, and lock-in if you choose a closed-source vendor. All three are avoidable: run HA, benchmark before adopting, and pick MIT-licensed, self-hostable software.
How can I get started with an AI gateway from LiteLLM?
Install with pip install ‘litellm[proxy]’ or pull the Docker image, drop in a config.yaml, and start the proxy on port 4000. From there, create virtual keys in the admin UI and point any OpenAI client at the gateway. The Docker quick start is the fastest path.