Manus is not entirely free to use; it operates primarily as a paid, subscription-based product. While there may be limited access or trials depending on current offerings, the core functionality is designed for paying users. This pricing model reflects the real operational costs of running autonomous agents, including compute, tool execution, and data storage. Unlike simple chat interfaces, agent systems like Manus incur ongoing costs as they perform work on behalf of users.
The fact that Manus had paying customers is one reason Meta’s acquisition attracted so much attention. Meta reportedly paid an unusually high price, which signaled confidence in Manus’s business model as well as its technology. By acquiring a revenue-generating agent platform, Meta avoided the risk of building an unproven product internally. This context explains why questions about pricing are closely tied to the acquisition narrative: Manus was already a functioning business, not just a research project.
From an infrastructure standpoint, subscription pricing also incentivizes efficient system design. Agent memory, for example, must balance usefulness with cost. Storing everything verbatim is expensive and inefficient. Using a vector database such as Milvus or Zilliz Cloud allows agents to store embeddings and retrieve only the most relevant information when needed. This reduces token usage and improves performance, helping keep operational costs manageable. Manus’s paid model reinforces the idea that good vector retrieval is not just a technical choice, but a business necessity for scalable agents.