Best LLM Gateways in 2026: 8 Tested and Ranked

OpenRouter

OpenRouter is the simplest answer to "I need one API key for every model." You sign up, add credits, and call any model through a single OpenAI-compatible endpoint. The homepage now lists 400+ models from 60+ providers, including Claude, GPT, Gemini, Llama, DeepSeek and Mistral. No infrastructure to run, no proxy to deploy.

It's best for solo builders and small teams who want to test models against each other without writing a separate integration for each one. The unified billing alone saves you from juggling five provider accounts and five invoices.

Pricing is pass-through: you pay each model's standard per-token rate with no markup. OpenRouter makes its money on a 5.5% platform fee on non-crypto credit purchases (5% flat for crypto), plus a 5% fee on bring-your-own-key requests above 1M per month. The free tier gives you 25+ free models at 50 requests a day, rising to 1,000 a day once you've bought $10 of credits.

The catch: it's a hosted black box. You can't self-host it, every request routes through OpenRouter's servers, and that 5.5% credit fee adds up at scale. For a regulated team that needs data to stay in its own VPC, this is a non-starter.

LiteLLM

LiteLLM is the most widely adopted open-source LLM proxy, and it's the one I reach for when I want control. It gives you one OpenAI-format API for 100+ LLMs, handles the translation between provider formats, and ships with a polished admin UI for keys, budgets and rate limits. You self-host it, so your traffic never leaves infrastructure you own.

It's best for platform teams who want to standardize model access across an organization without locking into a vendor. The spend-tracking and virtual-key system is genuinely good: you hand each team a key, set a budget, and get per-key usage without building any of it yourself.

The open-source version is free. Infrastructure typically runs $200 to $500 a month depending on traffic. The Enterprise tier (SSO, SCIM, audit logs, support) isn't publicly priced, with references putting a basic plan around $250/month and larger contracts negotiated directly.

Where it falls short: LiteLLM is a Python proxy, and under heavy concurrency it adds real latency. Self-hosting also means you own the on-call. When the proxy falls over at 2am, that's your pager, not a vendor's. Budget for the DevOps time, not just the $0 license.

If you're standardizing model access across an org, you're probably also building agents on top. My breakdown of the best AI agent frameworks pairs well with a gateway like this one.

Portkey

Portkey bets you need production safety more than another API proxy. Where LiteLLM focuses on routing, Portkey's differentiator is governance: guardrails, PII redaction, jailbreak detection and audit trails built into the gateway layer. Every request is logged, traced and attributed, so you see cost broken down by feature, user and model in a real-time dashboard. The homepage now claims access to 1,600+ LLMs through a unified API.

It's best for enterprise teams shipping AI into regulated environments, where "who saw what data and when" is a compliance question, not a nice-to-have. Worth noting: Portkey was acquired by Palo Alto Networks in 2026, which signals where it's headed (security and enterprise governance).

The free Developer tier covers 10,000 logs a month with 3-day retention. Production runs $49/month for around 100,000 logs, and most of what you pay for across tiers is log volume and retention, not request throughput. Enterprise is custom.

The catch: you're paying for observability and guardrails, so if all you need is failover and a unified endpoint, Portkey is more than the job requires. The log-volume pricing also means a chatty app can blow past the free tier fast. For a small team, that's overkill.

Cloudflare AI Gateway

Cloudflare AI Gateway is the near-zero-ops option if your app already lives in Cloudflare's ecosystem. It sits between your application and the model providers and adds analytics, caching, rate limiting and spend limits with almost no setup. The core features are free with just a Cloudflare account, and inference is passed through at the provider's own per-token rate with no markup.

It's best for teams already running Workers, Pages or Cloudflare's CDN who want gateway features without standing up new infrastructure. DLP scanning is free on all plans, which is a real perk for anyone worried about leaking data into prompts.

The newer Unified Billing feature lets you pay for third-party model usage (OpenAI and others) on your Cloudflare invoice, with a 5% fee on credits purchased that way. You don't have to use it; bring your own provider keys and the gateway itself stays free.

Where it falls short: it's lighter on advanced routing logic and guardrails than Portkey or Bifrost, and the experience is best inside Cloudflare. If you're on AWS or GCP with no Cloudflare footprint, the pull is weaker.

Picking the right infrastructure layer is the kind of decision that pays off for years. If you're assembling a full AI stack, our roundup of the best AI tools is a useful map of what plugs in where.

Bifrost

Bifrost, built by the Maxim AI team, is the gateway to reach for when raw throughput is the constraint. It's open source, written in Go, and the headline number is real: at 5,000 requests per second on a single instance it adds around 11 microseconds of overhead per request, versus the 8ms-plus you get from Python-based proxies. The team claims roughly 50x faster than LiteLLM on identical hardware, backed by reproducible load tests.

It's best for high-traffic production systems where gateway latency actually shows up in your p99. It exposes a unified OpenAI-compatible API across 12+ providers (OpenAI, Anthropic, Bedrock, Vertex and more), plus automatic fallbacks, load balancing, semantic caching, governance and Vault-backed secret management. Setup takes under 30 seconds via npx or Docker.

It's free to self-host, with a managed cloud option for teams that don't want to run it themselves.

The catch: it's newer than LiteLLM, so the community, plugin ecosystem and battle-tested edge cases are thinner. Go is also a different operational profile than Python if your team has never shipped it. The speed is genuine, but you're trading maturity for it.

Helicone

Helicone is the observability-first gateway. It's open source (YC W23), routes traffic across 100+ models, and the pitch is that logging, tracing and analytics come baked into every request with no extra config. One line of code and every call is monitored. It also does caching, rate limiting and provider fallback like the others.

It's best for teams that care more about debugging and analyzing LLM behavior than about heavy routing logic. You get traces, sessions, user analytics, alerts and queryable logs (with its own HQL query language), and it integrates cleanly with OpenAI, Anthropic, Azure, Gemini, plus LiteLLM and OpenRouter if you're already on those.

The Hobby tier is free with a 10,000 requests per month allowance. Pro is $79/month, Team is $799/month, and Enterprise (SOC 2, GDPR) is custom. There's no markup on the inference itself.

Where it falls short: Helicone leans observability over gateway. If your main need is multi-provider failover and intelligent routing at scale, a dedicated gateway like Bifrost or LiteLLM does that job harder. Many teams run Helicone alongside another gateway rather than as the only one. If observability is your real question, see our guide to the best LLM observability tools.

Kong AI Gateway

Kong AI Gateway is the right call if you already run Kong for your regular APIs. It extends the same proxy you know with an AI Proxy plugin that gives a unified interface across providers, so developers can switch models without touching application code. In 2026 Kong pushed hard into the agent era: version 3.14 shipped an Agent Gateway supporting agent-to-agent traffic and first-class Model Context Protocol support, including an MCP Registry.

It's best for platform and infrastructure teams that already standardized on Kong and want AI traffic governed by the same policies, rate limits and auth as everything else. The Semantic Policy Engine, which enforces rules based on the meaning of a request using on-gateway embeddings, is a sharp feature for safety-conscious teams.

The catch: the open-source version lacks the GUI, advanced analytics and the enterprise plugins (OIDC, SAML, AI Rate Limiting Advanced) you actually need for production AI. Those sit behind a paid Enterprise license that isn't cheap, and the total cost of ownership climbs once you add them. This is the heaviest option here. Don't adopt Kong just for the AI gateway; adopt it if you already live in Kong.

If you're heading toward agent-to-agent and MCP traffic, our roundup of the best MCP servers covers what connects on the other end.

TrueFoundry

TrueFoundry rounds out the list as the managed-but-self-hosted middle ground. It runs an AI gateway inside your own cloud (VPC), pairing the data-residency benefit of self-hosting with the support and SLAs of a managed vendor. You get unified model access, spend tracking, budgets and rate limits, but you're not the only one on call when something breaks.

It's best for teams that want LiteLLM-style control without owning the entire operational burden, and that have the budget for a commercial contract to get there.

Pricing is custom and quote-based, so you'll need to talk to sales. There's no free self-serve tier the way LiteLLM or Helicone offer.

Where it falls short: the lack of transparent pricing makes it hard to evaluate quickly, and for a small team the commercial commitment is heavier than just running open-source LiteLLM and eating the DevOps cost. This is an enterprise buy, not a weekend experiment.