The goal of an LLM gateway is not simply to cut AI spending. It is to get more value from every token by matching each request with the right balance of cost, latency, quality, and reliability.
An LLM gateway, also called an AI gateway, gives developers one endpoint for accessing and routing requests across multiple AI providers. The best option helps teams control total AI costs while deciding where premium speed and model capability are worth paying for.
This comparison evaluates gateway economics: provider markups, platform fees, self-hosting burden, routing controls, and request-level spend visibility. The goal is not to identify one universally cheapest tool. It is to find the gateway that helps your team make better decisions about AI spend.
What to look for in an LLM gateway
Cost and performance tradeoffs: The right gateway should help you decide when a request needs premium speed or intelligence—and when a less expensive route delivers the same business result.
Markup and fees: Gateway charges can compound as usage grows. Compare token markups, credit-purchase fees, BYOK terms, and any platform charges.
Deployment model: Self-hosting offers control over data and infrastructure, but your team owns security, uptime, and maintenance. Managed services reduce that operational work.
Reliability and latency: Production AI workloads need fallback behavior and choices between fast, premium routes and lower-cost routes that can tolerate more delay.
Spend visibility and controls: Request-level records, budget controls, and team or application-level allocation make it possible to understand where AI spend goes—and whether it is producing results.
Model access: Breadth matters when you need a specific provider or model. It should not outweigh economics, operational burden, and the quality of routing decisions.
Compare top LLM gateways
These gateways differ in how they help teams manage AI usage: some prioritize self-hosted control, some prioritize broad model access, and others combine routing with spend visibility, governance, or caching. Compare the tradeoffs in deployment, pricing, and cost-control approach.
| Gateway | Deployment | Pricing | Best for |
|---|---|---|---|
| Ramp Router | Managed | Free routing during beta, list price on tokens | Optimize cost, speed, and quality across AI workloads |
| LiteLLM | Self-hosted OSS; Enterprise support/deployment options available | Free (open source), Enterprise custom pricing | Full self-hosted control, DevOps capacity required |
| OpenRouter | Managed | Provider prices; 5.5% card fee or 5% BYOK fee above $25K/month | Broad model access, fast setup |
| Portkey | Self-hosted or managed | Free self-hosted, managed from $49/mo, priced on logs | Observability and governance alongside routing |
| Requesty | Managed | 5% pay-as-you-go markup; 0% with your own provider keys | Repeatable, cache-friendly workloads |
| Bifrost | Self-hosted (Enterprise: VPC/on-prem/air-gapped) | Free (open source), Enterprise custom pricing | Low-overhead routing, hierarchical budgets |
1. Ramp Router
Ramp Router helps teams allocate AI spend across the requests where it creates the most value. It routes requests based on the tradeoff among cost, latency, quality, and availability, so teams can reserve premium capacity for work that needs it and use better-value routes for work that does not.
Router is a managed, OpenAI-compatible endpoint, so adoption can be as simple as changing a base URL. It also records usage and cost per request, giving teams visibility into how each application, model, provider, and routing decision affects AI spend.
Ramp built Router on routing technology it has operated internally for three years, routing more than 2.75 trillion tokens monthly across production workloads. Ramp CTO Rahul Sengottuvelu says Router reduced the company’s internal LLM costs by 30% without sacrificing performance.
Ramp tests every new model on real work using Ramp SWE-Bench, its own benchmark built from production engineering work, before Router starts routing to it. If a provider has an outage or hits a rate limit, Router can send the request to another available model instead of letting it fail.
During beta, Router’s routing layer is free: teams pay the published token price for the model that serves each request, with no Router markup. New users receive $26 in credits.
- Deployment: Managed.
- Fee structure: Free routing during beta; published model-token prices apply.
- Key features: OpenAI-compatible endpoint, Thompson-sampling learned routing, ~30ms added latency, a 99.999% success rate, per-request usage and cost records, a broad selection of model providers including Anthropic, OpenAI, Fireworks, and xAI.
- Integrations: Works with any framework built for the OpenAI Chat Completions API.
Pros: No markup during beta, plus $26 in credits for new sign-ups. Three years of internal production use, including a 30% cost reduction from Router. Spend visibility built in, not bolted on.
Cons: Smaller model catalog than OpenRouter or LiteLLM while it scales up.
Try Ramp Router
2. LiteLLM
LiteLLM is an open-source proxy you self-host. Because you bring your own provider API keys, LiteLLM adds no markup to token spend. The software itself is free, including commercial use.
What you give up is the managed layer: you own the infrastructure, uptime, and engineering time to run and maintain it. That tradeoff makes sense if you already have the DevOps capacity and want full control.
- Deployment: Self-hosted open source; Enterprise deployment and support options are available.
- Fee structure: Zero markup, infrastructure cost is yours.
- Key features: Unified interface to a wide selection of providers, automatic retries and fallbacks, per-project budget limits, OpenTelemetry observability on guardrail violations.
- Integrations: Azure OpenAI, Vertex AI, Anthropic, Bedrock, Groq, Mistral, Together AI, plus observability tools like Lunary, MLflow, Langfuse, and Helicone.
Pros: MIT-licensed and free to self-host commercially, with more than 56,000 GitHub stars and a broad integration ecosystem spanning cloud providers and observability tools.
Cons: You own the infrastructure, uptime, and engineering time. Single sign-on (SSO), role-based access control (RBAC), and a managed guardrails UI require the paid Enterprise tier, which is custom-priced rather than published.
3. OpenRouter
OpenRouter charges provider list prices for AI usage, then adds a fee based on how you pay. Buying credits by card adds 5.5% (minimum $0.80); crypto payments add 5%. If you bring your own provider keys, the first $25,000 in monthly usage is fee-free, then OpenRouter charges 5% on additional usage.
That pricing structure may be a reasonable tradeoff for teams that value broad model access and minimal setup.
- Deployment: Managed.
- Fee structure: Provider prices, plus payment or BYOK fees depending on your setup.
- Key features: Broad multi-provider model access, edge routing with automatic provider fallback, drop-in OpenAI SDK compatibility.
- Integrations: LangChain, LlamaIndex, Vercel AI SDK, PydanticAI, Mastra, plus native Python, TypeScript, and Rust software development kits (SDKs) and a command-line interface (CLI).
Pros: Broad model selection with close to zero setup time. Inference pricing itself passes through at cost, no markup. Wide framework support (LangChain, Vercel AI SDK, and more) makes drop-in adoption easy.
Cons: Costs can rise at scale—card-funded usage carries a 5.5% purchase fee, while BYOK is free only for the first $25,000/month before a 5% fee applies. There is no self-hosted option.
4. Portkey
Portkey's gateway is open source and free to self-host. Its managed cloud tier starts free and scales into paid plans (from $49/month) priced on request-volume and observability features ("recorded logs"), not a per-token markup.
You can start self-hosted and move to managed later without changing gateways. The managed tier's pricing model takes some getting used to, though, since it isn't priced on tokens at all.
- Deployment: Self-hosted or managed.
- Fee structure: Free self-hosted, managed tiers priced on log volume, not tokens.
- Key features: Unified API across a broad multi-provider catalog, built-in observability, tracing, cost tracking, guardrails.
- Integrations: LangChain, CrewAI, AutoGen, and other agent frameworks. Drop-in compatible with OpenAI and Azure.
Pros: Combines managed compliance, guardrails, and observability in one package. Offers both self-hosted and managed deployment options.
Cons: Onboarding and key-lifecycle management can be infrastructure-heavy without strong internal processes. Palo Alto Networks acquired Portkey in May 2026, so teams should monitor roadmap changes under its new ownership.
5. Requesty
Requesty charges a 5% markup on provider prices when you use its pay-as-you-go service. A model that costs $10 per million tokens from the provider directly costs $10.50 through Requesty. If you bring your own provider keys, Requesty says it adds no markup.
It offers prompt and semantic caching, which can reduce costs for repeated or similar requests. The size of those savings depends on your workload and cache-hit rate.
- Deployment: Managed.
- Fee structure: 5% pay-as-you-go markup on provider prices; 0% with your own provider keys.
- Key features: Multi-provider model access, prompt and semantic caching, spend limits, PII protection, and EU routing.
- Integrations: OpenAI-compatible with a single base-URL change, plus built-in spend limits and PII detection.
Pros: Clear pay-as-you-go pricing, a no-markup BYOK option, and caching for repeat-heavy workloads.
Cons: Managed-only deployment and a smaller public open-source footprint than LiteLLM.
6. Bifrost
Bifrost is Maxim AI's open-source LLM gateway, built in Go and optimized for latency-sensitive production traffic. The open-source software (OSS) tier is free to self-host indefinitely, with no feature gating on core routing, caching, or budget controls.
The step up is Enterprise: custom-priced, not published, and gated behind a demo request. It adds guardrails, cluster mode, enterprise SSO, RBAC, and audit logs, plus a 14-day free trial.
- Deployment: Self-hosted (OSS). Enterprise adds virtual private cloud (VPC), on-prem, or air-gapped options.
- Fee structure: Free OSS, Enterprise custom-priced.
- Key features: Multi-provider access, semantic caching, hierarchical budgets, MCP support, automatic failover, and load balancing.
- Integrations: Drop-in replacement for OpenAI, Anthropic, Google GenAI, and AWS Bedrock SDKs. Also works with LangChain, PydanticAI, Prometheus, and OpenTelemetry for observability.
Pros: A free self-hosted OSS tier with routing, caching, budget controls, failover, and load balancing. Native Model Context Protocol (MCP) support for agentic workloads.
Cons: Enterprise pricing isn't public, the same opacity as LiteLLM's paid tier. Newer to the market than LiteLLM or OpenRouter, with a smaller community track record.
There is no single lowest-cost gateway for every team. Self-hosted tools can eliminate gateway markups but add infrastructure work; managed gateways reduce that burden but may charge platform fees. The right choice depends on whether your team needs full operational control, the broadest model catalog, or cost-aware routing with visibility into every request.
Put AI spend where it creates value
AI costs should not be managed by restricting usage. They should be managed by giving premium models, low latency, and higher token budgets to the work where they produce the greatest return—and finding better-value routes everywhere else.
Ramp Router gives teams a managed way to route requests based on cost, speed, quality, and availability while tracking request-level spend. See where token budgets are going, route eligible work to lower-cost paths, and preserve premium capacity for the AI experiences that matter most.
Router is free through 2026, with no routing markup and $26 in credits for new users. Make one base-URL change to start optimizing the economics of every AI request.
Try Ramp Router
Ramp Router is currently in beta—routing is free to use, with no markup on the tokens you consume. Get Started today.
FAQs
One endpoint for accessing multiple AI models. Instead of wiring your app to one provider at a time, you send requests through our router which can choose the right model for the job based on quality, cost, and availability. No lock-in. One line to switch.
Your request goes to Ramp Router first. Router authenticates the request and helps you track the usage, model, provider, and cost. Router routes eligible requests to a more cost-efficient tier when it won't affect quality. See Router Strategies to save even more.
Router supports the latest models from OpenAI, Anthropic, and other providers, including select open-source models such as Kimi. Ramp regularly adds support for new models as they become available. See the full list of supported models here.
Several gateways in this comparison—including Ramp Router, LiteLLM, OpenRouter, and Bifrost—support fallback or failover configurations that can reroute eligible requests when a provider is unavailable. Confirm each vendor’s behavior, model compatibility, and setup requirements before relying on it in production.
“Invoices, cards, tokens. The categories change but the principle doesn't: know where the money is going, remove the work around it, and make sure the spend is worth it.”
Maciej Mylik. Finance
ElevenLabs
“There's just no surprises anymore. No more waiting two months to find out how a job did. We know how it's doing as it's happening.”
Erich Kuss
Financial Systems Manager, Infinity Home Services
“More token spend isn’t proof that AI is working. Less isn’t proof that it isn’t. What matters is whether we’re buying the right level of intelligence for the work. Ramp lets us make that judgment in the same place we manage every other type of spend.”
Cody Nutt
Senior Director of Business Systems, Daxko
“Most banks treat the back office as a cost to keep down. We treat ours as a return to compound, which is why we run it on Ramp. Now we put our clients on Ramp, too.”
Patrick Gaughen
President & COO, Hingham Institution for Savings
“Browserbase builds infrastructure so AI agents can do real work. Ramp is doing the same for finance. It’s not another tool. It’s a system purpose-built for AI-driven finance, and that’s why we chose Ramp as our financial operating system from day one.”
Paul Klein IV
Founder & CEO, Browserbase
“We used to pay up to $20k a year for our AP platform. With Ramp, we’re earning back well over that amount. That's money that belongs to the mission now, not to the back-office software.”
Heidi Coffer
Chief Financial Officer, Boys & Girls Clubs of San Francisco
“The tricky thing about corporate travel policy is timing. We didn't need a stricter policy. We needed the policy to show up earlier. With Ramp Travel, it finally does.”
Keith Frantz
Director of Enterprise Risk Management, Prosper
“We're accountable to our funders, our partners, and the families we serve. That accountability starts with how we manage every dollar. Ramp makes it easy for our team to spend wisely, track in real time, and keep overhead low so more resources reach the families navigating infertility.”
Rachel Fruchtman
CFO, Jewish Fertility Foundation
