Kopai is the cloud for AI agents. Build the agent; we run it. Publish any agent as an API (native or OpenAI-compatible, with streaming), list it in the marketplace, or call it from your own product. Analytics count what an agent costs to run separately from what it earns. A one-command benchmark runs it against reference agents before you ship. Certification expires, and answers get re-checked. Chat and the API use the same engine, so an exported agent behaves like the one you tested.
Domain experts have knowledge worth paying for, but their income is capped by their calendar. One consultation, one hour, one client at a time. AI agents change that math: package what you know once, and let it solve the same problem for hundreds of people without you in the room.
Building the agent turned out to be the easy part. The real questions were: how do you package knowledge so an agent actually knows it, how do you prove the agent works, how do you charge for it, and how does a stranger trust an agent from someone they've never met? That's the layer nobody had built, so we built it: an agent harness, RAG and orchestration, an evaluation pipeline, and a pay-per-use ledger that handles the entire money lifecycle.
We're deliberately not chasing publish counts. We're running workshops with domain experts and tracking one number: paid usage per agent. Alongside that, we're deploying the same infrastructure into enterprises that need production-grade agents running behind their own firewall.
Two markets, one stack. We'd rather prove both work than assume either does.
Try building an agent, or just poke at what's live, and tell us where it breaks.
Thank you for upvoting our product on launch day and making our launch memorable. It was exhausting not gonna lie but your feedbacks and wishes are always worth it.
These are some tools found on the Kopai marketplace. These have been created by domain experts in their field. We're running workshops with domain experts to bring some of that knowledge to you.
Congrats on the second launch. Curious about the analytics that separate cost to run from earnings per agent, does that break down per model/provider too? Would be useful for picking which model to route to for cost vs quality tradeoffs.
whats the trigger for a certification expiring, a set time window or a new model version? and does the one-command benchmark rerun on its own after that?
Hi Product Hunt Team,
It has been a crazy 2 months after out first launch. It may seem a little too quick to do our second one but we have a good reason, I promise.
Kopai is now the cloud for AI Agents. You can create, host, distribute and monetise your AI Agents. That is not all, you have full lifecycle support, that is the evaluation and analytics supports for your agents. Finally if you want to use it in your own platform, we have API's that let you do so, without ever having to bother about the underlining mechanics of Agent creation. Underline tools:
Multi Agent Orchestration
RAG
LLMOps
Over 900+ connectors
API Integrations
Eval System
HITL
Analytics
You can now manage your team as well, we have a dedicated workspace for you to invite and collaborate with your team.
Thank you for all your reviews and your constant support. We are open to discussion and feedback. Do leave a review
Thanks, Team Kopai.
About Kopai on Product Hunt
“The Cloud for AI Agents”
Kopai launched on Product Hunt on September 8th, 2026 and earned 107 upvotes and 10 comments, placing #12 on the daily leaderboard. Kopai is the cloud for AI agents. Build the agent; we run it. Publish any agent as an API (native or OpenAI-compatible, with streaming), list it in the marketplace, or call it from your own product. Analytics count what an agent costs to run separately from what it earns. A one-command benchmark runs it against reference agents before you ship. Certification expires, and answers get re-checked. Chat and the API use the same engine, so an exported agent behaves like the one you tested.
Kopai was featured in API (98.7k followers), Developer Tools (519.7k followers) and Artificial Intelligence (478.9k followers) on Product Hunt. Together, these topics include over 218.2k products, making this a competitive space to launch in.
Who hunted Kopai?
Kopai was hunted by Surya Sekhar Datta. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
Want to see how Kopai stacked up against nearby launches in real time? Check out the live launch dashboard for upvote speed charts, proximity comparisons, and more analytics.
Surya Sekhar Datta
Hey Product Hunt! 👋
I'm Surya, co-founder of Kopai.
Domain experts have knowledge worth paying for, but their income is capped by their calendar. One consultation, one hour, one client at a time. AI agents change that math: package what you know once, and let it solve the same problem for hundreds of people without you in the room.
Building the agent turned out to be the easy part. The real questions were: how do you package knowledge so an agent actually knows it, how do you prove the agent works, how do you charge for it, and how does a stranger trust an agent from someone they've never met? That's the layer nobody had built, so we built it: an agent harness, RAG and orchestration, an evaluation pipeline, and a pay-per-use ledger that handles the entire money lifecycle.
We're deliberately not chasing publish counts. We're running workshops with domain experts and tracking one number: paid usage per agent. Alongside that, we're deploying the same infrastructure into enterprises that need production-grade agents running behind their own firewall.
Two markets, one stack. We'd rather prove both work than assume either does.
Try building an agent, or just poke at what's live, and tell us where it breaks.
Kopai: https://usekopai.com