Chariot

Chariot is a cloud infrastructure platform built specifically for running AI agents at scale. It lets you deploy anywhere from one agent to millions, with each agent running in its own isolated instance that can hibernate when idle and wake on demand. The pay-only-while-working model means you are not burning money on idle compute, and persistent storage keeps state intact between runs so agents can pick up where they left off.

The core problem it solves is the operational overhead of managing agent infrastructure. Running a single AI agent on your laptop is easy. Running hundreds in the cloud with proper isolation, scaling, and persistence is a different challenge entirely. Chariot handles the orchestration so you can focus on what the agents actually do rather than how to keep them alive and talking to each other.

Deployment is straightforward. You can spin up a fleet with a single command or integrate Chariot into your existing workflow via Codex or Claude Code. The platform supports multiple runtimes including ZeroClaw, NanoClaw, Hermes, OpenClaw, and custom options, so you are not locked into one execution environment. On the model side, it works with OpenAI, Claude, GLM, MiniMax, and custom models, which gives flexibility if you want to mix providers or run your own.

The economics are worth understanding. Provisioned agents that have not run yet cost nothing. Hibernating agents cost a small amount for storage, around seven cents per day. Active agents cost more depending on size and runtime, with smaller configurations starting around twenty-seven cents per day. The pricing documentation claims up to 98% cost reduction compared to keeping traditional VMs running continuously, though real savings depend on your workload's burst patterns.

Each agent gets its own private VPS with isolated compute and persistent volumes. That means one agent cannot see another's files or processes, and state survives across wake cycles. For workloads where agents need to accumulate knowledge or maintain context over time, persistent storage is critical, and Chariot bakes it in rather than requiring you to bolt on external databases.

The target user is anyone building agentic systems that need to scale beyond a handful of instances. If you are prototyping on your local machine and wondering how to deploy to production, Chariot offers a path that does not require you to become a Kubernetes expert. If you are already running agents in the cloud and frustrated by the cost of always-on compute, the hibernation model could cut your bill significantly.

The platform is newer, so the community is still growing, and documentation may not cover every edge case. But for teams that have hit the limits of manual agent management and want infrastructure that understands how agents actually work, Chariot is a serious option. You can book a demo on their site or join their Discord to see whether it fits your use case.