Sacra estimates that Manus reached a $90 million annual revenue run rate in August 2025. The company introduced its first paid subscription plans in March 2025, transitioning from a free, invite-only beta model that it operated throughout 2024.

Manus has a three-tier pricing structure starting at $19 per month for basic plans and reaching $199 per month for premium tiers.

Manus raised $75 million in a Series B round led by Benchmark in April 2025, bringing its valuation to approximately $500 million. This followed a $10 million Series A in January 2023, bringing total funding to $85 million.

Investors include Benchmark as the Series B lead, along with Tencent, HongShan (formerly Sequoia China), and ZhenFund from earlier rounds. The company relocated its headquarters from China to Singapore in 2025 as part of its international expansion.

Manus is an autonomous AI agent platform designed to execute complex, multi-step tasks based on natural language instructions. Users provide task descriptions in plain English, and Manus initiates a dedicated cloud virtual machine equipped with tools such as web browsers, code interpreters, office applications, and design engines.

The platform offers two primary modes: a standard chat interface for Q&A interactions and an agent mode that activates the full autonomous execution environment. For example, when tasked with analyzing 100 sneakers and creating a sortable spreadsheet, Manus decomposes the objective into discrete steps, autonomously browses relevant websites, extracts pricing data, normalizes product information, generates a CSV file, and delivers the output as both chat results and downloadable files.

The system utilizes Anthropic's Claude language models, augmented with proprietary context engineering techniques to optimize prompt scaffolding and memory management, ensuring agent loops remain efficient and cost-effective. Users can schedule recurring tasks, run multiple parallel workflows based on their subscription tier, and access the platform via web and mobile applications. The Wide Research feature, introduced in July 2025, enables up to 100 parallel sub-agents to operate simultaneously, significantly reducing execution time for research-intensive tasks.

Manus operates a B2C subscription model with usage-based pricing tiers that scale with customer requirements. Monthly subscriptions range from $19 for basic access to $199 for premium plans, with each tier offering varying levels of concurrent task execution, scheduling capabilities, and computational resources.

The company employs a freemium go-to-market strategy, allowing users to access basic functionality with limited usage before transitioning to paid plans. During the beta phase, this approach generated a waitlist exceeding 2 million users prior to monetization. The business model benefits from low marginal costs after the core infrastructure is established, as additional users primarily generate API calls to third-party language models rather than requiring substantial incremental hardware or labor.

Manus is expanding its offerings to include team workspaces that pool credits and enable collaboration. Additionally, the company is developing an enterprise SDK to allow other businesses to embed Manus workflows directly into their applications. This platform strategy could transition the company from a direct subscription model to a developer-focused API business, with the potential to significantly increase average revenue per user.

OpenAI, Microsoft, Google, and Anthropic integrate agent capabilities directly into their foundation model offerings, leveraging integration advantages and subsidized pricing through their cloud infrastructure. Microsoft's Copilot Studio incorporates native Office 365 and Windows functionality, enabling system-level task execution that third-party agents access via APIs. Google's Gemini agents integrate with Workspace and Android, while OpenAI's agent features overlap with Manus's focus on autonomous task execution.

These incumbents can offer lower pricing by treating agent development costs as a customer acquisition and retention strategy for their broader cloud platforms. However, enterprise buyers increasingly seek vendor-neutral solutions to mitigate lock-in risks, creating opportunities for independent platforms such as Manus.

Adept's ACT-2 employs computer vision to directly control user interfaces, bypassing the API integrations that Manus uses for task execution. Cognition's Devin targets software engineering tasks, achieving quantifiable performance benchmarks on coding challenges that appeal to development teams.

These specialized competitors focus on specific verticals rather than general-purpose capabilities. Rework.ai, Sweep.ai, and similar coding-oriented agents integrate into development workflows and CI/CD pipelines, capturing high-value engineering use cases that might otherwise align with general platforms like Manus.

LangChain, AutoGen, and other open-source agent frameworks are commoditizing the orchestration layer underlying autonomous task execution. Organizations can use these tools to build custom agent workflows instead of subscribing to hosted platforms.

While open-source solutions require greater technical expertise for implementation and maintenance, they provide unlimited customization and eliminate ongoing subscription costs. This dynamic exerts pricing pressure on commercial platforms, which must differentiate through ease of use, reliability, and advanced features rather than basic agent functionality.

Manus is developing an agent-as-platform SDK that allows other companies to embed Manus workflows directly into their applications, shifting its focus from a consumer assistant to a developer platform. This approach targets the broader autonomous agent tooling market, which includes enterprise software integration and workflow automation in addition to individual subscriptions.

The platform model enables Manus to generate revenue from every downstream user interacting with partner applications, rather than relying solely on direct subscribers. Companies can build specialized agent experiences on Manus infrastructure and pay revenue-sharing fees, following a model similar to app stores.

The company is piloting domain-specific task packs for finance, merchandising, and legal research. These bundles include custom prompts, specialized tool integrations, and compliance features. Vertical solutions can command higher pricing than general-purpose subscriptions by addressing specific industry workflows that require deep domain expertise.

This vertical strategy allows Manus to compete with specialized software solutions rather than general-purpose AI agents, potentially accessing larger budget allocations for industry-specific tools. It also creates switching costs as customers integrate domain-specific workflows into their operations.

Manus's partnership with Alibaba's Qwen team provides access to advanced multimodal AI models and preferential cloud computing resources. This collaboration supports expansion into physical-world automation, including factory automation, retail applications, and smart home use cases.

With offices in Tokyo and San Francisco, alongside its Singapore headquarters, Manus is positioned to serve multinational enterprises that prioritize consolidated vendors over regional point solutions. This global presence is particularly relevant for large accounts requiring consistent agent capabilities across multiple countries and regulatory environments.

Model dependency: Manus depends extensively on third-party language models from Anthropic and other providers, which introduces both cost exposure and strategic risk. Increases in API pricing, access restrictions, or the introduction of competing agent platforms by these providers could result in margin compression or the loss of access to critical components of Manus's technology stack.

Regulatory scrutiny: As a Chinese-founded company utilizing AI agents to process sensitive business data, Manus is exposed to potential export control restrictions and data sovereignty issues, particularly in enterprise markets. Relocating its headquarters to Singapore may not fully mitigate these risks if governments impose limitations on AI services originating from Chinese entities.

Execution complexity: Deploying autonomous agents for real-world tasks introduces liability and reliability risks that are less pronounced in simpler AI applications. As Manus agents integrate with more external systems and manage higher-stakes workflows, the likelihood of costly errors or security breaches increases, which could constrain enterprise adoption.