Run AI Agents on your cloud infrastructure
Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents, built on top of Okteto’s development platform. Each agent runs in its own containerized environment on your infrastructure, with the services, tools, and policies it needs. You can spin up agents with a single click or API call. No local setup. No git worktrees. The beta…
Run AI Agents on your cloud infrastructure launched on June 18, 2025 with 12 votes, #1,876 of 7,999 launches that month and more than 75% of that year's launches. The site was still online in September 2026, 1 year on.
In plain words
Agent Fleets provides managed, containerized environments for running AI agents on your cloud infrastructure. Each agent operates in its own isolated environment with pre-configured services, tools, and policies, eliminating the need for local setup or complex terminal management. Teams can deploy agents with a single click or API call, supporting models like Claude Sonnet 4 and frameworks like OpenHands. Currently in beta, it's designed for development teams seeking streamlined AI agent workflows without local configuration overhead.
written from the facts on this page · September 2026
From the sources
Give every AI Coding Agent an isolated, production-like environment on your infrastructure to build, test, and verify against real services and data.
Every agent task gets an isolated, production-like environment on your infrastructure to build, test, and verify against real services and data. Keep Claude Code, Cursor, Codex, Copilot, or your own internal agents. Let Okteto become the powerful environment layer underneath them. Install the Okteto skill, point the session to your cluster, and watch your agents immediately start using Okteto to validate their tasks. Run complete, isolated, multi-service environments or only the services that the task touches . Your agent decides, not the vendor. Builds, deploys, and runs your end to end tests and checks the endpoints itself. Once the work has been verified, open a pull request, with a live…from okteto.com
In the maker’s words, at launch
Hi, founder of Okteto here! We’ve been experimenting with AI agents in our workflows at Okteto. Running them locally worked at first, but quickly became painful. git worktrees, multiple terminals, and messy context switches slowed us down. So we built Agent Fleets: ephemeral, fully managed environments for AI agents…
Pricing, as stated on its site
Free tier, paid plans — Okteto offers a freemium model with a free Starter tier (up to 5 seats). Scale and Enterprise tiers require contacting sales for pricing details.
checked September 2026 · prices change
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BudaMay 2026 · buda.im · ▲197Run a full AI agent team in the cloud — no hardware, no setup. Persistent memory, browser automation, parallel agents. The OpenClaw alternative built for teams. Start free.
Cloudways Managed AI AgentsAug 2026 · cloudways.com · ▲84Skip the setup and run OpenClaw & Hermes, fully managed
MaritimeMar 2026 · maritime.sh · ▲98Hi HN, my name is Maria, and I’m a co-founder of Maritime. We started Maritime at MIT to build infrastructure for companies that need to run thousands of isolated AI agents for their customers. Imagine you set up an agent like OpenClaw, or a personal assistant agent with a custom framework, and want to give a separate version of it to every customer/friend. Each customer needs their own agent running in an isolated microVM, with persistent state, secrets, triggers, and sleep/wake behavior. Building such scalable and secure infra will take you months and will cost hundreds of…
Running AI agents across environments needs a proper solutionMar 2026 · github.com · ▲8Hi HN folks, I have been building AI agents for quite some time now. The shift has gone from LLM + Tools → LLM Workflows → Agent + Tools + Memory, and now we are finally seeing true agency emerge: agents as systems composed of tools, command-line access, fine-grained system capabilities, and memory. This way of building agents is powerful, and I believe it is here to stay. But the real question is: are the systems powering these agents ready for that future? I do not think so. Using Docker for a single agent is not going to scale well, because agents need to be lightweight and fast. LLMs…
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