OpenRouter Alternatives: 10 AI Gateways and Routers Compared

Why teams look for OpenRouter alternatives

OpenRouter's pitch is one key, one bill, hundreds of models. That works until something stops fitting, and the reasons tend to be specific.

Billing friction is the most common trigger. OpenRouter takes no markup on inference and passes provider list prices through, but topping up credits carries a card-processing percentage with a small flat minimum, and crypto purchases a lower one. Bring-your-own-key is free up to a monthly allowance measured in provider list-price usage, and above it a percentage of the equivalent model cost comes out of your credits. OpenRouter's FAQ carries the current figures. The shape of your bill depends on how you fund the account, which surprises teams who read passthrough as free.

Provider quality variance is the second. A model slug can be served by several upstream providers with different quantization, context limits, and tool-calling reliability. The :nitro, :floor, and :exacto suffixes bias routing toward throughput, price, or dependable tool calling. Useful controls, and an admission that "the same model" is not always the same model.

Governance is the third. Compliance reviews want SSO, role-based access, per-team budgets, audit trails, and residency as first-class features rather than account settings. Self-hosting is the fourth: air-gapped environments cannot route production traffic through a third-party proxy at all.

Platform risk is the fifth. In late July 2026, WSJ, The Information, and Axios reported Stripe in talks to acquire OpenRouter for roughly $10B. The deal is reported, not confirmed, and nobody should re-architect on a rumor.

OpenRouter alternatives at a glance

Cost models and tiers last verified 21 August 2026. Each entry below links the vendor's own pricing page, because figures move and theirs is the only copy worth trusting.

| Tool | Type | Model access | Self-hosting | Governance (SSO, RBAC, audit) | Cost model | Best for | |---|---|---|---|---|---|---| | Major | App layer | Claude, Kimi, Gemini, Muse Spark, ChatGPT, Grok | Enterprise | SSO, RBAC, scoped credentials, audit at platform layer | Usage-metered, front-loaded then flat | Repeated reasoning that should become code | | LiteLLM | Self-hosted proxy | 100+ providers | Yes, air-gapped | Virtual keys, budgets, RBAC, SSO/SCIM on Enterprise | Open-source core, custom Enterprise | Running the gateway yourself | | Portkey | Hosted gateway, OSS core | Broad multi-provider | Yes, OSS gateway and VPC | SSO, RBAC, budgets, guardrails on Enterprise | Metered on log volume | Managed breadth with a self-host exit | | Requesty | Hosted gateway | 600+ models, 20+ providers | No | Spend limits, approved-model policy, PII, EU residency | Model cost plus a percentage markup | Widest catalog, least setup | | Together AI | Inference host | Open models it hosts | Dedicated and clusters | Account-level controls | Per-token, provisioned, per-GPU-hour | Open-weight models at volume | | Helicone | Observability with gateway | Multi-provider proxy | Yes, OSS; on-prem at Enterprise | Org and seat controls | Metered on request volume | Teams who cannot explain their bill | | Cloudflare AI Gateway | Hosted gateway | OpenAI, Anthropic, Google, Workers AI | No | Cloudflare account controls | Bundled in Cloudflare plans | Apps on Cloudflare's edge | | Vercel AI Gateway | Hosted gateway | Hundreds of models | No | Vercel team and project controls | Token passthrough, no markup | AI SDK and Next.js teams | | Amazon Bedrock | Cloud platform | Anthropic, Meta, Mistral, Nova, OpenAI | In-account, in-VPC | IAM, guardrails, AWS audit | Per token, batch discount, provisioned | Workloads inside AWS | | Google Vertex AI | Cloud platform | Gemini plus Model Garden | In-project | IAM, VPC Service Controls, Audit Logs | Per token or character, provisioned, batch | Workloads inside GCP | | TrueFoundry | Self-hosted gateway | 250+ models | Yes, VPC or on-prem | RBAC, SSO, quotas, MCP tool governance | Custom | Governance-first platform teams |

The 10 best OpenRouter alternatives

The list opens with the app layer because it addresses the cost driver every gateway below shares, then covers the gateways, hosts, and cloud platforms you came here to compare.

Major (the app layer)

Major is the enterprise platform where AI agents build the software they run on, a different layer from a gateway and the right one when the same reasoning repeats.

What it does. An agent on Major takes a task, works out how to handle it, and identifies the parts that run the same way every time. It builds an app for those parts and runs the app instead of reasoning through them again. The app is deterministic code with its own managed database, storage, permissions, and logs, with SSO and audit at the platform layer. The model stays in for judgment and steps out of everything else.

Take invoice-exception triage. Today an agent reads each invoice, compares line items against policy, and decides whether to flag it, so four thousand invoices means four thousand rounds of reasoning over the same policy. Under the app-layer approach the agent reasons through the policy once and builds an app running the deterministic checks in code: totals arithmetic, tax rules, purchase-order matching, vendor allowlist. Those four steps leave the model path, and what reaches the model is the residue, the invoices where something genuinely does not reconcile. Deciding which steps to move is its own skill, covered in agentic workflow patterns.

One more effect matters if you are shopping for a router. Because state lives in the app rather than one vendor's context window, switching models later does not lose the work. A router makes the model swappable per request. An app layer makes the model choice reversible.

Key advantages

• Repeatable work runs as code, so it gives the same result every time and spends no tokens.
• State persists in a managed database with permissions and audit trails, so work resumes and can be inspected.
• Every app an agent builds is reusable across the organization.

Ideal users. Platform leaders whose agent bill grows in a straight line with volume, and operations leads running high-repetition processes like invoice triage or ticket classification.

Cost model. Usage-metered, front-loaded then flat: the reasoning happens once when the app gets built, and running the app afterward spends nothing on that step. Major pricing.

LiteLLM

The default answer when the gateway has to run inside your own infrastructure.

What it does. LiteLLM is an open-source proxy presenting an OpenAI-shaped API in front of a hundred-plus providers, translating formats so your code stays still while the model behind it changes. You run it, and the operational burden is yours. Enterprise adds virtual keys, budgets, rate limits, spend tracking, audit logs, RBAC, SSO and SCIM.

Key advantages

• Air-gapped and multi-region deployment, which hosted gateways cannot offer at any tier.
• Virtual keys with per-key budgets, which makes internal chargeback tractable.
• No third party in the request path.

Ideal users. Platform engineers in regulated environments whose security review rejected a hosted proxy.

Cost model. Open-source core, with LiteLLM Enterprise quoted on request.

Portkey

Managed breadth with an open-source gateway underneath, a rare combination.

What it does. Portkey proxies requests across providers with routing, retries, timeouts, fallbacks, and load balancing, recording every call. The gateway is open source and can be hosted independently, with observability, guardrails, and governance on the managed product. Its logging tells you what a request cost and how it failed, narrower than agent observability, which asks what the agent did across many calls.

Key advantages

• Self-host the gateway or use the managed one, and change your mind later.
• Guardrail hooks and custom policies applied per request.
• SSO, RBAC, team budgets, and private VPC deployment on Enterprise.

Ideal users. Teams that want a managed gateway now and the option to pull it in-house later.

Cost model. Metered on recorded log volume, with request overage on the paid production tier. Portkey pricing.

Requesty

The closest like-for-like swap if you want a bigger catalog and no operational work.

What it does. Requesty routes across 600+ models from 20+ providers behind one API, with routing policies, automatic fallbacks, load balancing, and prompt caching. Governance covers spend limits, approved-model policies, guardrails, PII detection, and EU residency.

Key advantages

• Larger model catalog than most managed gateways.
• No seats and no minimum spend, so a small team can adopt it without a contract.
• Approved-model policies, the control most requested and least often available.

Ideal users. Developers replacing OpenRouter one-for-one who want breadth plus governance controls.

Cost model. Base model cost plus a percentage markup. Requesty pricing.

Together AI

An inference host rather than a router, and the difference matters before you compare them.

What it does. Together AI runs open-weight models on its own GPUs through serverless endpoints, dedicated deployments, provisioned throughput, and raw clusters. It does not proxy you to Anthropic or OpenAI. It serves the models it hosts.

Key advantages

• Control over the deployment, from serverless through reserved capacity to bare GPUs.
• Fine-tuning and dedicated endpoints for open models.
• No intermediary between you and the machine running inference.

Ideal users. Teams standardizing on open-weight models at volume, especially with a fine-tuned checkpoint.

Cost model. Per-token serverless, provisioned throughput, dedicated GPU hours, or per-GPU-hour clusters. Together AI pricing.

Helicone

Observability first, gateway second, and it should be evaluated on the first.

What it does. Helicone logs requests, responses, latency, and cost, adding caching, rate limits, and fallbacks on top. It is open source and self-hostable, with on-premises deployment at the enterprise tier.

Key advantages

• Request-level tracing that makes cost attribution possible.
• Open source with a real self-host path.
• Drops in alongside an existing gateway rather than replacing it.

Ideal users. Teams who can already reach every model they need and cannot explain their bill.

Cost model. Metered on request volume and stored data, with a free allowance and paid tiers above it. Helicone pricing.

Cloudflare AI Gateway

The obvious pick if your application already runs at Cloudflare's edge.

What it does. It sits between your application and model providers, adding analytics on requests, tokens, and cost, plus logging, caching, rate limiting, retries, and fallback across OpenAI, Anthropic, Google, and Workers AI.

Key advantages

• Response caching that cuts repeat provider calls.
• Rate limiting and retries at the edge, close to your users.
• Available across Cloudflare plans, so adoption is a configuration change.

Ideal users. Teams already on Workers, Pages, or Cloudflare's network.

Cost model. Bundled into Cloudflare plans. Cloudflare AI Gateway docs.

Vercel AI Gateway

Best fit for AI SDK teams who want one key and no token markup.

What it does. One endpoint and one key reach hundreds of models for text, image, video, and embeddings, retrying against another provider when one fails. It works with AI SDK v5 and v6, OpenAI Chat Completions, and Anthropic Messages, so migration is usually a base-URL change.

Key advantages

• No markup on tokens, including with bring-your-own-key.
• Automatic cross-provider failover.
• Spend monitoring across providers in one view.

Ideal users. Next.js and AI SDK teams already on Vercel.

Cost model. Token passthrough with no markup, billed through your Vercel account. Vercel AI Gateway docs.

Amazon Bedrock

The right call when the security review is harder than the model comparison.

What it does. Bedrock exposes models from Anthropic, Meta, Mistral, Amazon, OpenAI, Cohere and others through AWS APIs, inside your account and your VPC. Guardrails add content filters, topic restrictions, sensitive-data handling, grounding checks, and prompt-attack detection.

Key advantages

• Traffic and data stay inside AWS boundaries you already govern.
• Guardrails and IAM apply to model calls the way they apply to everything else.
• Batch inference runs at a discount for work that can wait.

Ideal users. Enterprises on AWS where procurement and compliance are the binding constraint.

Cost model. On-demand per token, batch at a discount, provisioned throughput by model unit and hour. Bedrock pricing.

Google Vertex AI

The same logic as Bedrock, applied to Google Cloud.

What it does. Vertex AI serves Gemini plus Model Garden third-party models including Claude, Llama, and Mistral, with availability varying by model. IAM, VPC Service Controls, and Cloud Audit Logs govern access the way they govern the rest of your GCP estate.

Key advantages

• VPC Service Controls perimeters around model access, which few gateways match.
• Gemini and several third-party families under one billing account.
• Provisioned throughput for predictable capacity.

Ideal users. GCP-native teams, particularly where the data already sits in BigQuery.

Cost model. Per token or per character by model, plus provisioned throughput and separately priced batch. Vertex AI pricing.

TrueFoundry

Pick it when governance depth is the blocker and the gateway must live in your VPC.

What it does. TrueFoundry runs a gateway in your own cloud or on-premises, covering routing across 250+ models, fallbacks, quotas, and policy controls, with tracing, token analytics, and governance for MCP servers and agent tools.

Key advantages

• Deploys in a customer-controlled environment with RBAC and SSO.
• Governance covers MCP and agent tool calls, not only model calls.
• Prompt testing and versioning alongside the gateway.

Ideal users. Platform teams standing up an internal gateway for many product teams.

Cost model. Custom, quoted per deployment. TrueFoundry pricing.

How to choose an OpenRouter alternative

Choose on the constraint that actually binds you, and be honest about which layer of the stack you are actually shopping for.

If your cost problem is call volume rather than call price, and the same reasoning runs over thousands of near-identical items, pick Major and move the repeatable steps into an app. Shaving a percentage off four thousand identical reasoning calls is worth less than not making three thousand of them.

If you want the widest catalog with the least setup, pick Requesty. Six hundred models, no seats, and you can be on it this afternoon.

If you must self-host or run air-gapped, pick LiteLLM, with Portkey's open-source gateway as the fallback when you want a managed option later.

If governance is the blocker, pick TrueFoundry or Portkey Enterprise. Both put SSO, RBAC, quotas, and tracing in a deployment you control, and TrueFoundry extends policy to agent tool calls.

If you are already on AWS or GCP, pick Bedrock or Vertex AI. The catalog is narrower and the compliance conversation is already over, which is usually the trade worth making.

Routing and the app layer solve different problems

A gateway decides which model answers a request and on what terms. An app layer decides whether a model needs to answer at all. Both are real jobs, and most teams running agents in production want both. Neither is AI orchestration, where the concern is how steps get sequenced rather than where they execute.

The distinction matters most for one workload shape: high-volume repeated reasoning, where an agent applies the same logic to thousands of items that differ only in their particulars. Invoice triage. Ticket classification against a fixed taxonomy. Contract clause extraction. For that shape, routing strategies improve the price and reliability of each call while leaving the call count untouched, and the call count is the term doing the damage. That is also the limit of most LLM cost optimization work. Move the deterministic middle into an app and gateway volume drops to the exceptions, where a router earns its place by giving those harder calls the best model available.

The routing layer answers a question about the call, never about the need for it

Here is the constraint, unsoftened. Every tool on this list, Major included, answers a question about the model call. Gateways make each call cheaper, faster, more governable, easier to fail over. None of them make the call unnecessary. If your agent reasons through the same policy on all four thousand invoices, the routing layer is working on the wrong number.

The resolution is to stop repeating the reasoning. Let the agent reason through the repeatable chunk once, build an app for it, then run the app. Where the work is genuinely different every time, a gateway is doing the job it should and the two layers sit together. That is what Major is: the enterprise platform where AI agents build the software they run on. The repeatable middle becomes deterministic code with its own database, permissions, and audit trail, the model handles the real exceptions, and cost front-loads then flattens instead of tracking usage. Because state lives in the app rather than a context window, the model choice stays reversible across Claude, Kimi, Gemini, Muse Spark, ChatGPT, and Grok. New accounts get $100 in free credits, enough to build an agent that runs in production rather than a demo.

If your model bill grows in a straight line with volume, build the invoice-exception triage agent first: reason through the policy once, let the agent ship an app for the totals, tax, PO-match, and vendor checks, and send only the genuine exceptions back to a model. Get started on Major and build your invoice-exception triage agent.

Related articles

• Lovable vs Bolt: AI App Builder Comparison for 2026
• 10 Best Base44 Alternatives for Production Apps (2026)
• Zapier vs Make: Which Automation Platform Wins in 2026?