LiteLLM vs OpenRouter: Gateway vs Aggregator Explained

Gateways and aggregators differ on who owns the data plane

The gateway vs aggregator distinction comes down to who owns the data plane, the component that carries live requests to model providers. A self-hosted gateway runs in your own environment. You deploy it next to your applications, you control its routing rules, and you decide what it logs. An aggregator runs in the vendor’s cloud. You point your code at its API, and it routes each request to one of many providers for a fee.

Ownership decides three things:

• Routing and policy: a gateway applies rules your team writes. An aggregator applies the options its hosted service exposes.

• Data path: a gateway sends requests straight from your network to the provider. An aggregator adds its own infrastructure to that path.

• Operations: a gateway is yours to deploy, scale, and patch. The aggregator’s vendor carries that work.

Both options expose an OpenAI-compatible API, so your application code looks the same either way.

LiteLLM gives one team a self-hosted proxy with budgets and fallbacks

LiteLLM is a strong starting point for a single team that wants to standardize on one API format and keep traffic inside its own infrastructure.

LiteLLM translates every provider into one OpenAI-compatible API

LiteLLM translates requests between provider APIs into one OpenAI-compatible format, so applications can switch models without writing to each provider’s interface. It runs as a Python library or as a self-hosted proxy server. Because the proxy runs in your infrastructure, requests go straight from your network to the model provider with no intermediary in between.

The open source version covers more than basic routing. LiteLLM tracks spend by key, user, and team, then enforces per-key budgets plus team caps. When a provider fails, LiteLLM supports fallback routing to the next one in an ordered list. Role-based access control (RBAC) and audit logs require LiteLLM’s Enterprise license.

Self-hosting LiteLLM makes your team the operator

The tradeoff is operational. Your team deploys LiteLLM, scales it, patches it, and answers the page when it fails. The team also owns the provider accounts, deployment pipeline, observability, and incident process around it.

Performance depends on that work. LiteLLM’s own benchmarks report 8 ms of 95th percentile (P95) overhead at about 1,000 requests per second (RPS) across four instances, measured against a mock model endpoint. GatewayScore, an independent gateway review site, also notes operator reports of the Python proxy slowing past about 300 RPS per instance, with memory growing toward out-of-memory crashes. Results vary with tuning, instance count, and deployment setup. LiteLLM is moving its gateway to Rust, so these Python-era figures may change.

OpenRouter trades ownership for fast access to hundreds of models

OpenRouter is the right choice for individual developers and small teams who value speed of access over ownership of the data plane. Stripe agreed to acquire OpenRouter in August 2026.

OpenRouter removes the operations work behind model access

OpenRouter offers more than 400 models behind one API key, through an OpenAI-compatible API, with no infrastructure to run. You sign up, add credits, and start calling models in minutes. OpenRouter runs the proxy, manages the provider relationships, and keeps pace with provider API changes.

OpenRouter covers more than raw access. It falls back to another provider when one returns an error. It enforces per-key credit limits before each request, but your policy options are limited to what its hosted service exposes.

OpenRouter charges a fee and keeps your traffic in its cloud

The cost of that convenience is a fee plus a data path you do not control. As of this writing, OpenRouter’s Standard plan charges a 5.5% credit-purchase fee, and its Business plan charges an 8% platform fee. It passes model prices through without markup. It is managed only, with no self-hosting option, so every request passes through OpenRouter’s cloud on every plan. Calling OpenRouter also adds one network hop to a hosted service before the request reaches the model provider.

Data residency and compliance depend on the plan. OpenRouter, a US company, offers EU data residency on its Business and Enterprise plans. Those requests still run on OpenRouter’s infrastructure, outside your own network. OpenRouter has a SOC 2 report, but it does not offer a Business Associate Agreement (BAA) under the Health Insurance Portability and Accountability Act (HIPAA). A healthcare organization needs a signed BAA before a vendor can handle protected health information, so health-data workloads need a different route.

The biggest differences are data path, cost, and operations

LiteLLM differs from OpenRouter in where requests travel, how spend is controlled, and who runs the infrastructure.

The Tetrate figure comes from an independent Broadcom/VMware test of the Envoy data plane behind Agent Router Enterprise, measured under real LLM load. The LiteLLM figure is LiteLLM’s own benchmark against a mock endpoint, so the two numbers are not a head-to-head comparison.

Teams outgrow LiteLLM when AI spreads across teams and providers

A single LiteLLM deployment works well for one team. The strain shows up once several teams, providers, and regions depend on it.

The warning signs show up in load, spend, and access

A team has outgrown a single LiteLLM deployment when the same problems keep returning. The common signals are:

• The proxy falls behind under heavy traffic, and adding replicas turns one component into a fleet to operate.

• Spend tracking covers only the traffic routed through each deployment, so direct API calls go uncounted.

• New developers file a ticket to get model access because no self-serve, governed path exists.

One fintech retired its home-grown gateways without rewrites

A home-built gateway can reach a working first version quickly. The ongoing cost is keeping pace with provider API changes across regions, which becomes a distributed-systems problem for the platform team.

A large fintech customer with thousands of developers shows what consolidation looks like. Before Agent Router Enterprise, the company reached AI through disconnected paths, including coding assistants such as Copilot, Codex, and Claude Code, plus internal agents. It leaned on home-grown gateways that cost effort to maintain, and those gateways saw only the traffic they routed. Model access was limited to OpenAI and Anthropic models.

After deployment, every tool entered through one governed path with full consumption telemetry. Model access widened to include Google Vertex AI, Mistral, and private open-weight models. The home-grown gateways were retired, which freed the engineering time spent maintaining them. None of the change required application rewrites. For a team running its own LiteLLM deployment, the comparable cost is the engineering time that deployment consumes.

Agent Router Enterprise keeps the interface, caps spend, and runs where your data stays

Agent Router Enterprise is an enterprise AI gateway built on Envoy, which carried over 2 million requests per second across more than 10,000 hosts at Lyft by 2017. The gateway is 100% OpenAI-compatible, so existing code connects by changing one base URL, with a typical time to first request under five minutes. Agent Router Enterprise 101 Overview walks through the developer and admin experience.

Agent Router Enterprise attributes every token to a person, team, agent, or project. Budgets are enforced inside the request path, so a looping agent gets capped before it spends past its limit. Tetrate’s budgeting feature shows this in action.

The data plane can run fully managed or in your own cloud, on-premises, or at the edge, while Tetrate runs the management plane. The data plane keeps carrying traffic even if the management plane is unreachable. Teams that need OpenRouter’s model breadth inside their own network can compare OpenRouter alternatives.

Conclusion: choose based on who should own the data plane

LiteLLM and OpenRouter solve the same interface problem from opposite sides of the network boundary. LiteLLM suits a single team that can run its own proxy, with routing, budgets, and fallbacks kept inside its infrastructure. OpenRouter suits developers who want hundreds of models today and accept a credit fee plus a hosted data path. When AI spreads across several teams, providers, and regions, an enterprise AI gateway keeps the same OpenAI-compatible interface while adding one policy layer your platform team controls.

If LiteLLM is hitting its limits or your data has to stay in your network, start a fast-track evaluation: first routed request under five minutes, nothing to install. Request a demo.

FAQs

Can you use LiteLLM and OpenRouter together?

Yes. LiteLLM can use OpenRouter as an upstream provider. Your team keeps keys, budgets, and logging in its own LiteLLM deployment while OpenRouter supplies model breadth. Requests still pass through OpenRouter’s cloud, and its platform fee still applies.

Do I need to rewrite agent code to switch to Tetrate Agent Router Enterprise?

No. Agent Router Enterprise is 100% OpenAI-compatible, so existing code connects by changing one base URL. The typical time to a first routed request is under five minutes.

Which one gives better cost visibility?

LiteLLM and OpenRouter both break spend down by API key. LiteLLM also tracks spend by user and team, with budgets that cover the traffic passing through each deployment. OpenRouter enforces per-key credit limits and charges a fee on credit purchases. Agent Router Enterprise attributes every token to a person, team, agent, or project across all providers, with budgets enforced in the request path.

What’s the performance overhead of each approach?

LiteLLM’s own benchmarks report 8 ms of P95 overhead at about 1,000 RPS against a mock endpoint. Results vary with deployment and tuning. OpenRouter adds one network hop to a hosted service. An independent Broadcom/VMware test measured the Envoy data plane behind Agent Router Enterprise adding about 2 ms per request under real LLM load.

How do I decide between a gateway and an aggregator?

Choose a gateway when you need your own routing policy, data residency inside your network, or no fee on spend. Choose an aggregator when you want fast access to many models with no infrastructure to run. When a self-hosted gateway starts to strain across several teams, evaluate an enterprise AI gateway.

Key terms glossary

Gateway: Infrastructure between your applications and AI providers that you control, whether self-hosted or managed on your behalf. It applies your routing rules and policies to every request.

Aggregator: A hosted service that offers many models behind one API key. It routes requests from its own infrastructure and charges a fee on spend.

OpenAI-compatible: An API that matches the OpenAI request and response format. Code written for OpenAI connects by changing only the base URL.

Fallback: Automatic rerouting of a request to another provider or model when the first one fails.

Enterprise AI gateway: A gateway built to govern AI traffic across many teams, providers, and regions from one policy layer.

Data plane: The component that carries live traffic between your applications and AI providers. In Agent Router Enterprise, the data plane keeps routing traffic even if the management plane is unreachable.

Data residency: A requirement that data is stored and processed inside a specific geographic region.