Manus AI Pricing and Credit Consumption Guide

According to a 2026 cost analysis by No Code MBA, executing unconstrained autonomous agents in a browser environment can consume up to 1,500 credits for a single complex task, representing a real-world cost of six times more than a standard API query, or up to $8 when executed on entry-tier subscription plans. This potential for rapid credit depletion makes understanding the underlying pricing architecture essential for developers managing production workflows. Manus AI structured its pricing to accommodate different usage levels by combining a monthly or annual subscription with a credit refresh system. Annual subscriptions offer a 17% discount compared to monthly plans, which provides a cost-saving path for long-term deployments.

Understanding the billing model requires separating your active subscription from the monthly credit allowance. Every paid account gets a flat monthly allocation of credits, which are used as currency to run tasks in the virtual machine environment. In addition to these monthly pools, Manus AI offers a daily refresh allowance for basic tasks, which resets every 24 hours and does not roll over.

The membership tiers are structured as follows:

• Free Plan ($0/month): Designed for testing and small tasks, this tier includes a limited daily refresh pool of 300 credits. It provides access to Chat Mode and Manus Lite in Agent Mode. Daily refresh credits do not accumulate and are reset every 24 hours.

• Pro Entry ($20/month): Provides 4,000 monthly credits. This plan is aimed at developers testing agents or running lightweight automated tasks.

• Pro Mid ($40/month): Provides 8,000 monthly credits. This tier is suitable for professionals requiring daily agent assistance for research and code generation.

• Pro Top ($200/month): Provides 40,000 monthly credits. Designed for heavy workloads and high-frequency background execution.

• Team Plan (Starts at $20/seat/month): Designed for collaborative environments. It offers a shared credit pool, administrative controls, and shared workspace analytics.

Credits do not roll over to the next billing cycle. If you exhaust your monthly allocation, you must purchase additional credit packs manually to continue running tasks. This lack of rollover means users must carefully align their plan selections with their anticipated monthly task volume to prevent paying for unused credits.

Unlike simple API calls that charge purely per token, Manus AI credit consumption is determined by the complexity, resource requirements, and active execution time of each task. Simple queries that use only Chat Mode are economical, whereas tasks that require the agent to browse the web, execute scripts, or build web applications incur higher costs because they trigger full browser automation loops and virtual machine runtime.

Developers can anticipate credit consumption based on these estimated ranges:

• Simple Queries & Chat: 10 to 50 credits per request.

• Code Generation & Execution: 200 to 500 credits.

• Web Research Tasks: 100 to 300 credits.

• Deep Research (Multiple Sources): 500 to 900 credits.

• Web App Building: 500 to 1,000+ credits.

• Wide/Complex Analysis: 800 to 1,500+ credits.

To clarify how these ranges translate to real usage, consider two specific execution scenarios. In the first scenario, an agent is instructed to perform a competitive intelligence research task. The agent starts by launching a browser, navigating a search engine, querying five competitors, reading the homepage text of each site, extracting product features, and compiling a summary. The browser startup consumes 100 credits, page navigation across five sites consumes 250 credits, extracting text consumes 150 credits, and the final planning and compilation consume 180 credits, resulting in a total of 680 credits for a single run.

In the second scenario, a developer asks the agent to build a simple React dashboard. The agent generates the file structure, writes the React code, creates the configuration files, launches a local web server to compile the files, encounters three compilation errors, corrects the syntax in the files, recompiles, verifies the page runs, and packages the code. The initial generation consumes 250 credits, launching the server and compiling consumes 200 credits, the three correction loops consume 450 credits, and final verification consumes 100 credits, totaling 1,000 credits.

The primary drivers of credit consumption include the following:

1. Virtual Machine Uptime: Every minute the virtual browser is active and navigating pages consumes credits.

2. LLM Input and Output Tokens: The agent needs to plan, reason, and react at every step, which requires continuous LLM calls that accumulate token charges.

3. Browser Navigation and Click Actions: Iterating through web page links, filling forms, and rendering JavaScript page elements consume significant processing power, which translates to high credit rates.

4. Self-Correction Loops: If the agent runs into a compile error or a page loading issue, it attempts to self-correct by rewriting code or reloading the page. Each self-correction cycle consumes more credits.

For developers running continuous bots, scheduled scrapers, or background scripts, Manus AI offers a dedicated virtual machine extension. The Basic Cloud Computer add-on is $10/month for persistent disk space of 35 GB. This VM runs Ubuntu LTS and remains active 24/7, allowing you to run background processes without keeping your personal browser session open.

The specifications for the Basic Cloud Computer add-on are:

• Storage: 35 GB of persistent disk capacity.

• Compute: 2 vCPUs.

• Memory: 1 GB RAM.

• Networking: Dedicated public IP address and unlimited inbound data transfer.

This extension is highly beneficial for developers who need to persist database files, keep environment configurations active, or host small Telegram or Discord bots. Unlike standard ephemeral agent sessions that wipe all files once the prompt execution completes, the cloud computer retains all files and system changes, making it a reliable persistent workspace.

Developers can access this environment directly via a virtual terminal in the browser or configure custom SSH access keys. Inside this environment, you can install custom pip packages, set up databases, and configure systemd services. For example, if you run a daily data aggregation script, you can write a standard cron job that triggers your script inside the cloud computer. The script can execute local files, fetch external API endpoints, and dump the structured output directly into the persistent storage directory, ensuring that your data remains intact across agent reboots.

Persist Manus AI outputs in shared team workspaces

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To manage credit consumption effectively and avoid unexpected credit exhaustion, developers should implement strict cost-control practices. Unconstrained agent runs can easily execute loops that consume thousands of credits without yielding a useful output. This is especially true when agents attempt to debug code or browse paginated web pages without clear boundaries.

Apply these optimization strategies to manage costs:

1. Decompose Complex Tasks: Break large projects into small, distinct steps. Instead of asking the agent to build a complete dashboard in one prompt, ask it to write the database schema first, review the code, and then generate the API routes. This prevents the agent from entering long, unconstrained debugging loops.

2. Define Prompt Constraints: Give the agent explicit limits. Instruct it to search only a specific number of sources or to stop execution if an error is not resolved within two attempts. For example, use instructions like 'Search only the top three Google results' or 'Limit execution to five browser steps.'

3. Choose the Correct Mode: Use Chat Mode for coding advice, documentation checks, or general questions. Chat Mode only costs 10 to 50 credits, whereas Agent Mode triggers the full VM and browser automation, costing 500+ credits.

4. Implement Exit loops in Scripts: When running custom Python scripts in the Cloud Computer, write strict halt conditions to ensure the script terminates if an API or page resource is unavailable.

To see the value of prompt optimization, compare these two prompt styles:

Raw Prompt: 'Scrape this retail website and extract all products, prices, and reviews.'

Optimized Prompt: 'Access this retail website. Scrape only the first page of the electronics category. Extract the product name, price, and rating for the first ten items. Do not follow pagination links. Stop execution and output the results if any HTTP error is returned.'

The raw prompt can run infinitely, following pagination links and trying to parse hundreds of user reviews, which can consume over 1,200 credits. The optimized prompt restricts the scope to a single page and ten items, completing the task in under 150 credits while producing the exact data needed for your prototype.

While the Manus Cloud Computer provides an excellent execution environment, it is not designed to serve as a durable, collaborative database for teams. Ephemerality, single-user directories, and lack of version tracking make it difficult for multiple team members to collaborate on agent outputs. Fast.io serves as the coordination layer where agent output becomes team output.

Instead of storing files on isolated VMs, developers can connect their agents to Fast.io workspaces to enable shared access and advanced data processing. Fast.io features include:

• Shared Workspaces: Create org-owned spaces where team members and agents read, write, and collaborate on the same files, with granular permissions spanning organization, workspace, folder, and file levels.

• URL Import: Pull files directly from Google Drive, OneDrive, Box, Dropbox, or any direct URL using OAuth. With Fast.io URL Import, teams remove the need for local storage or heavy disk I/O.

• Intelligence Mode: Enable Intelligence Mode on any workspace to automatically index files for semantic search. Agents can query this workspace through the Fast.io MCP server, retrieving answers with citations to specific files, pages, and snippets without requiring a separate vector database.

• Metadata Views: Turn documents into a live, queryable database. Describe what fields you want extracted in natural language, and the AI designs a typed schema (such as Text, Integer, Decimal, Boolean, URL, JSON, Date & Time), matches files, and populates a filterable grid. Unlike simple search, Fast.io Metadata Views provide structured extraction for contracts, invoices, insurance policies, or media assets.

• Ownership Transfer: An agent can sign up free, build the workspace, configure files, and hand off the organization ownership to a human via a claim link. The human can start the 14-day free trial (credit card required) on the Fast.io pricing page, while the agent retains admin access. Fast.io plans are Starter ($29/month), Business ($99/month), and Growth ($299/month).

• Collaborative Notes: Google-Docs-style real-time co-editing where humans and agents edit documentation or code side-by-side with visible cursors.

• MCP-Native Access: Fast.io exposes action-based MCP tooling via Fast.io MCP Server Documentation at /mcp or /sse to connect any LLM or orchestrator.

• Version History and Audit Trail: Track every file version and review agent modifications in an append-only audit log.

To connect your external agents to Fast.io, you can configure your agent to call the Fast.io MCP server. This allows your agent to perform file operations, query Metadata Views, and search workspaces directly. Here is an example of an MCP configuration JSON block for your agent environment:

{
  "mcpServers": {
    "fastio": {
      "command": "npx",
      "args": [
        "-y",
        "@fastio/mcp-server"
      ],
      "env": {
        "FASTIO_API_KEY": "your_long_lived_api_key"
      }
    }
  }
}

By pointing your agent to the Fast.io MCP server, the files generated during its execution loops are written directly to your shared workspaces. This ensures that team members can view the outputs in real-time, comment on specific file regions, and trigger workflows for review and approvals, moving agent output into a secure, collaborative workspace.