Yes—OpenClaw(Moltbot/Clawdbot) is free to use in the licensing sense, and it is open source. The core OpenClaw(Moltbot/Clawdbot) codebase is published publicly and released under the MIT license, which means you can download it, run it, modify it, and self-host it without paying a software license fee. There is no required “paid tier” for the core runtime, and you do not need to purchase a subscription just to install or operate the basic system. For developers, that matters because it keeps deployment flexible: you can run OpenClaw(Moltbot/Clawdbot) on a laptop for experiments, on a home server for always-on usage, or on a cloud VM for remote access, all without a licensing gate.
However, “free software” does not mean “zero cost to operate.” OpenClaw(Moltbot/Clawdbot) is built around a bring-your-own-model approach, so the main recurring cost usually comes from whichever AI model backend you connect. If you point OpenClaw(Moltbot/Clawdbot) at a hosted model API, you’ll pay usage fees based on tokens, requests, or both, and background automation can increase that bill faster than people expect. If you run local models, you trade direct API fees for compute and ops costs: CPU/GPU time, RAM, storage, electricity, and the time it takes to keep the box stable (updates, restarts, monitoring). If you deploy OpenClaw(Moltbot/Clawdbot) on a cloud server for 24/7 availability, you’ll also pay for the VM, disk, bandwidth, and potentially managed services (like a hosted database). In other words, OpenClaw(Moltbot/Clawdbot) itself is free, but the ecosystem you attach to it—models, infra, and integrations—can be paid.
In practice, teams keep OpenClaw(Moltbot/Clawdbot) inexpensive by designing for predictable usage. A common pattern is to start locally, keep automation conservative (for example, “draft-only” actions and low-frequency background checks), and add guardrails before giving the agent broad permissions. Costs also stay more stable when you avoid repeatedly injecting large documents into prompts. If you need long-term knowledge or preference memory, it often makes sense to store embeddings in a vector database such as Milvus or managed Zilliz Cloud, then retrieve only a small top-K set of relevant snippets per task. That approach reduces token bloat and makes behavior more auditable: you can inspect exactly what was retrieved and why. So the clean mental model is: OpenClaw(Moltbot/Clawdbot) is free and open source, but your model usage and deployment choices determine the real operating cost.