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Where each of these stops.
These are good products. Each one solves a slice of the same problem deliberately well, and for plenty of teams a slice is all that is needed — every page below says plainly when that team is you.
What INFRO is betting on is that the slices belong together: the request that gets routed is the request that gets traced, priced, and checked against your organization's policy, and splitting that across three vendors is what creates the gaps.
Model marketplace and router
INFRO vs OpenRouter
OpenRouter is the best-known unified endpoint for language models: one API key reaching a very large catalog of text models across providers, with routing and fallbacks between them. It is a mature, widely used product and the closest thing this category has to a default..
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LLM observability
INFRO vs Helicone
Helicone is an observability platform for LLM applications — logging, tracing, cost tracking, evaluation, and a gateway — and it is open source, which matters to a lot of teams. If the problem you have is that you cannot see what your AI application is doing, it is a strong and well-regarded answer..
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LLM router and gateway
INFRO vs Requesty
Requesty is an LLM routing gateway: one endpoint across many language models with fallbacks, caching, spend controls, and analytics. It overlaps INFRO more closely than anything else in this list — the disagreement is about scope, not about whether routing is worth doing..
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Media inference platform
INFRO vs Runware
Runware is a media-generation platform built on its own inference infrastructure, with a strong focus on image and video performance and cost. Where most of this category treats media as an afterthought, Runware treats it as the product — and it shows..
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Our reading of each product's public documentation as of August 2026. We do not quote competitors' prices — theirs change and a wrong number about someone else's business is worse than none. Think a row is wrong? Tell us and we will fix it.
The whole layer, in six stages.
A router gives you the second. An observability proxy gives you the third. A media platform gives you one modality of the first.
01Connect
One API and one key across text, image, video, and audio — instead of an integration per vendor.
02Route
Health, price, and latency decide where a request lands, and a failing route is retried elsewhere.
03Observe
Every request keeps its model, latency, tokens, and exact cost, attributable to a project or a user.
04Optimize
Spend broken down while it happens, against reference pricing, with ceilings that stop rather than warn.
05Govern
Roles, budgets, approved models, and an audit trail over every team using AI on one account.
06Scale
Directory-driven access and enterprise agreements when AI stops being one team's project.
A router gives you the second stage. An observability proxy gives you the third. A media API gives you one modality of the first. The reason those are separate products is history, not architecture — the request that gets routed is the request that gets traced, priced, and checked against policy, and splitting it across three vendors is what creates the gaps.