Best OpenRouter Alternatives

OpenRouter — A broad model catalog and provider choice

OpenRouter makes sense if your shortlist changes frequently or you need to compare providers serving the same model. Its catalog includes proprietary and open-weight models, and free variants can help you test an integration before you fund it. Provider preferences also give experienced teams a way to balance availability, latency, data handling, and cost without wiring up several provider SDKs.

The main trade-off is that the token quote alone may understate what you pay. OpenRouter lists a platform fee on credit purchases, with different terms by plan. Free variants also have their own request limits and availability. If your application depends on one particular model, compare that route at a realistic monthly volume instead of using the cheapest model in the catalog as a stand-in.

Keep OpenRouter if a long list of models or the ability to choose among multiple hosts is worth more to you than a discount on one route. Before moving, write down which provider restrictions and fallback settings your application relies on. Those choices may need to be re-created in another gateway.

• Model quote
• Rate limits
• Pricing

LAYSO — Lower prices on selected routes with one managed API key

LAYSO fits developers who regularly call a handful of models and want to pay less on those routes without running a proxy. The public catalog listed 219 model entries at the time of comparison. One key can access eligible GPT, Claude, and Gemini models, and the catalog exposes input, output, and cache prices by route. The ordinary API bills by usage against prepaid platform credits.

The 50% figure in this guide applies to the quoted GPT-5.4 mini route. Other models, routes, and accounts can have different prices. Verify that the route and exact model ID are available to your key. A public catalog listing shows what the platform offers, while your key’s model list shows what you can call.

OpenRouter offers more catalog entries in this snapshot. If you depend on a niche model or a specific upstream provider, confirm availability before switching. LAYSO is a closer fit if your workloads are predictable enough to price individually. Its free model activities are useful for evaluation when the featured model, eligibility rules, and usage window fit your project.

• Live catalog
• API documentation

LiteLLM — A self-hosted, open-source gateway

LiteLLM is the strongest alternative here when you want to operate the gateway in your own environment. The proxy offers an OpenAI-compatible endpoint and integrates with more than 100 providers. That number describes integrations rather than a catalog of models you can call immediately. You decide which models to expose, which upstream credentials to use, and how requests travel through your infrastructure.

The open-source proxy is free to run. Budget separately for provider inference, compute, storage, and whoever will maintain the service. A production deployment needs key rotation, monitoring, upgrades, and a plan for when a provider starts returning errors or throttles your traffic. You also need to decide how to meter internal projects and reconcile usage against provider invoices.

Choose LiteLLM when deployment control is a requirement and your team already operates internal services. It can be a better fit than a hosted catalog for private routing or internal provider keys. A managed gateway is usually easier to evaluate if your immediate goal is a working API key and a predictable quote for one model.

• Proxy quickstart

Portkey — Observability, routing policies, and team controls

Portkey is worth a look when your immediate problem is operating AI across several teams. Its gateway combines routing with observability, key management, and controls that can help explain why a request failed or cost more than expected. That is a different buying decision from choosing a reseller purely for its token rates. Start by mapping the project budgets, logs, and provider restrictions you need.

Its model directory spans thousands of entries, including listings from multiple providers. Treat that as a directory count. Actual access depends on the providers you connect and the credentials and settings available to your account. The free gateway tier lowers the cost of trying the control plane, while provider inference may still be billed separately.

Portkey can be the better choice if your organization already has provider contracts and needs consistent controls over them. For a solo project that primarily wants one prepaid balance, price the full setup first. Compare the gateway plan and underlying inference bill together, and verify what your team can inspect on the tier you intend to use.

• Gateway plans
• Model directory
• Universal API

Requesty — Managed routing with a bounded free trial for real requests

Requesty is a practical option for developers who want a managed router and a free allowance they can try with their own application. Its public pricing lists up to 200 daily requests on the free tier, subject to the available free models. Its catalog reports 244 unique models and lists a larger number of provider endpoints. Compare unique models when breadth matters, and compare endpoints when you care about alternate routes.

Its published pay-as-you-go pricing adds 5% to provider rates. Requesty also documents a bring-your-own-key option without that markup, in which the upstream provider charges for inference. Check which arrangement applies before comparing the figures on the model directory to the amount you expect to pay.

Requesty is useful when you want to test routing and monitoring together with minimal setup. It may be less attractive for a workload that sends the same expensive model request at high volume every day. Take one known workload, estimate the cost including markup, and check whether the free allowance covers the tests you actually want to run.

• Pricing
• Model catalog
• OpenAI SDK setup

Vercel AI Gateway — A managed gateway for teams already using Vercel or the AI SDK

Vercel AI Gateway is a strong default for teams already shipping applications with the Vercel AI SDK. It offers a large managed catalog, supports the OpenAI-compatible endpoints, and prices tokens at provider list rates with zero gateway markup. The free tier includes a monthly five-dollar credit. Vercel says the monthly free credit no longer applies once the account transitions to paid usage.

Zero markup is useful as a price baseline. It does not make every model or route cheaper than a gateway offering a discount. Check the list price for the exact model, whether payment processing charges apply, and what happens when a request falls back to another credential or provider. Decide which routes you are willing to pay for before allowing fallback on production traffic.

Vercel can be the better fit when your deployment, telemetry, and SDK are already integrated with it. If you use the OpenAI SDK outside Vercel, still test the endpoints and features you depend on. Run one streamed request and one tool call with your actual configuration to check the output and usage fields you rely on.

• Gateway pricing
• Gateway and model coverage

Together AI — Hosted open-weight inference with room to move beyond serverless

Together AI makes sense for teams choosing open-weight models and considering how to serve them as usage grows. You can start with a model on its serverless catalog and evaluate more predictable capacity if traffic becomes sustained. This is especially relevant when the team has already tested a model and wants more control over how it is served.

Together offers an OpenAI-compatible interface, but you still need to replace the model ID and review which OpenAI request fields the selected model supports. Model availability and rates differ between serverless, dedicated, and fine-tuned deployments. Compare prices from the same deployment mode and include any cost of reserved capacity when you estimate a monthly bill.

Choose Together if your shortlist is dominated by open-weight models and you expect to tune the serving setup. Its curated catalog has a different focus from a marketplace for proprietary GPT and Claude variants. For a small project, run a serverless test first and record the observed token use, errors, and latency before committing to a capacity plan.

• Pricing
• OpenAI compatibility

GroqCloud — Fast inference on a shortlist of supported models

GroqCloud is useful when responsiveness matters and one of its supported models fits the task. Its model pages publish per-model prices and throughput information, and its free tier gives you a place to test an application. The table uses GPT-OSS 120B as the price example. That model is distinct from GPT-5.4 mini, so its rate is useful for estimating a different workload.

The catalog focuses on production and preview models supported by Groq. Some entries require enterprise access, and preview models can change. Free usage is also subject to multiple limits, including requests per minute and tokens per minute. A batch of long prompts can hit a token limit before it reaches the request cap. Review the account limits for the model before running a concurrency test.

Choose Groq when you can use a model it serves and your tests show the response time you need. Measure time to first token and complete response time separately on the same prompts. If your application needs models Groq does not serve, a wider gateway can make it easier to keep a single integration.

• Model rates
• Free and paid limits
• Billing

What 60 million tokens cost each month

Suppose a support assistant sends 50 million uncached input tokens and generates 10 million output tokens each month. Both quotes below use the same GPT‑5.4 mini model. Each subtotal is the number of millions of tokens multiplied by the published rate for that direction.

The difference is $41.25 per month, or 50% at these rates. LAYSO shows a USD reference equivalent and deducts platform credits. The example excludes cached tokens, funding fees, taxes and optional extras. Check the GPT‑5.4 mini model page for current routes and prices. If most of your prompts hit the cache, repeat the calculation using the cached input rate.

Move an OpenAI SDK integration in four steps

1. Create a LAYSO API key and store it in the LAYSO_API_KEY environment variable.
2. Set the SDK base URL to https://api.layso.ai/v1 and use the new key.
3. Use the exact model ID from the pricing page. OpenRouter’s openai/gpt-5.4-mini is gpt-5.4-mini on LAYSO.
4. Install the Python SDK with python -m pip install openai, then run the example. Compare output quality, latency and billed usage before redirecting production traffic.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.layso.ai/v1",
    api_key=os.environ["LAYSO_API_KEY"],
)

response = client.chat.completions.create(
    model="gpt-5.4-mini",
    messages=[{"role": "user", "content": "Write one sentence about API gateways."}],
)
print(response.choices[0].message.content)

Keep provider-specific routing headers separate from your SDK calls. If your application uses tool calling, streaming or JSON schemas, test those paths with the chosen model before switching the rest of your traffic.

Frequently asked questions

Compare the models you actually use

Check live channel prices, cached input rates and model availability before choosing a gateway.