Manus’s pricing plan is built around subscription tiers that allocate usage via a credit system and set limits such as how many tasks you can run concurrently. In March 2025, Manus publicly launched paid subscriptions starting at $39/month and also offered a higher tier at $199/month, with each tier including a monthly credit allotment and different concurrency limits (for example, the lower tier allowing fewer simultaneous tasks than the higher tier). The key idea is that Manus prices not just “access,” but the operational resources required to execute agent tasks—compute, tool calls, and the overhead of running multi-step workflows.
This pricing model is also part of why Meta’s acquisition drew so much attention and why the reported price was described as unusually high. A subscription plan demonstrates that users will pay for outcomes, which is a stronger signal than raw user growth alone. For Meta, acquiring Manus meant acquiring a working go-to-market motion for agent software: packaging, tiering, and a way to manage resource costs while still delivering value. Meta can integrate that commercial and operational logic into its broader business strategy, whether that’s serving SMB workflows, creator tooling, or internal automation. In other words, pricing is not an afterthought here—it’s evidence that the agent can be operated as a real product.
For developers, Manus’s credit-based tiers point to a practical engineering constraint: agents are expensive unless you control retrieval and context efficiently. If your agent needs to reference lots of documents, you don’t want to paste entire corpora into every prompt. Instead, you embed content and retrieve only what’s relevant per step. A vector database such as Milvus or Zilliz Cloud supports exactly this pattern, helping you keep prompts lean and predictable while improving task accuracy. That’s how you make credit usage feel fair to users: better retrieval means fewer wasted steps, fewer retries, and less token overhead. As Meta scales Manus, the same economics apply—pricing plans are ultimately downstream of infrastructure efficiency, and vector retrieval is one of the most effective levers.