Construct Computer vs fx: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Construct Computer and fx — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Construct Computer
Construct
An AI employee with its own cloud Linux computer that runs workflows, builds internal tools, and finishes scheduled work for small teams.
Key features
- Dedicated Cloud Computer: Each user's agent gets a real Linux cloud desktop, so it can run software and produce files rather than only generating text.
- Reusable Workflows: Encode a process once as agent steps, connected apps, and notifications, then version, schedule, and let any teammate re-run it.
- Internal Tool Builder: Describe the tool your team needs and Construct writes, validates, and publishes a working internal app straight into your cloud desktop.
- Scheduled Jobs with History: Schedule an agent prompt, a connected-app action, or a whole workflow to run once or repeatedly, with a full record of results.
- Inspectable Memory: Preferences, decisions, and project context are stored with supporting evidence and history, and can be reviewed, corrected, or forgotten.
- Shared Team Workspace: People, agents, files, apps, and conversations live in one workspace with invitations, roles, and precise access controls.
- Multi-Channel Access: Message Construct from the web, Slack, Telegram, Discord slash commands, or its own native email inbox, with per-channel routing and access policies.
- Cited Research Reports: Gathers sources, compares details, and turns open-ended questions into cited research you can review or share.
- Resumable Long Runs: Jobs that fail partway through resume from where they stopped rather than restarting, targeting reliability on multi-step work.
- Data Ownership and BYOK: Workspaces are isolated and never used as training data, you own the output, and Pro allows bringing your own model keys.
Best for
- Process Automation: Turning a recurring manual business process into a versioned workflow anyone on the team can trigger.
- Internal Tooling: Shipping a small internal app for a team need without pulling in engineering time.
- Inbox and CRM Follow-Through: Letting an agent read, reply, and close the loop across connected tools instead of leaving half-finished automations.
- Market and Topic Research: Producing cited research reports on a subject for review or client delivery.
- Scheduled Reporting: Running a recurring report or data pull on a schedule and keeping the result history in one place.
- Solo Founder Leverage: Handing off operational work as a one-person company without hiring a first operations employee.
- Cross-Channel Team Requests: Letting teammates hand work to the agent from Slack, Discord, Telegram, or email without changing tools.
fx
Vercel Labs
Vercel Labs' tiny open-source coding agent — a ~6 MB native CLI written in Zig, built for speed, embeddability and Unix-style ergonomics.
Key features
- Tiny Native Binary: The whole agent ships as a roughly 6 MB executable designed for instant installation and for embedding in resource-constrained environments and agent sandboxes.
- Instant Time to Prompt: fx cold starts in about 10 microseconds and performs no unnecessary work or I/O before accepting user input, which matters for programmatic invocation.
- Minimal Memory Footprint: A single-digit-megabyte memory baseline lets you pack many concurrent instances onto one machine.
- Shell-Like Ergonomics: Scroll history is preserved by default and output is deliberately sparse, so the CLI composes like a Unix tool instead of taking over the terminal.
- Context Efficiency: A minimal system prompt and tool surface reduce token spend and improve time-to-first-token performance.
- WebAssembly Builds: Optimal fx.wasm builds from the Zig toolchain shrink the binary further and make the network stack pluggable, enabling the in-browser demo.
- Model and Provider Agnostic: Works with local models, LLM gateways, direct provider API access or existing subscriptions rather than locking you to one vendor.
- Extensible Small Core: Capabilities are added through skills, plugins and MCPs, following a Unix-like philosophy of a small core with composable extensions.
Best for
- Sandboxed Agent Execution: Ship a full coding agent inside a container or sandbox where a large runtime would not fit.
- Embedding in Larger Systems: Use fx as the agent harness inside your own product or internal platform rather than building a loop from scratch.
- CI and Scripted Automation: Invoke a coding agent from pipelines and scripts where fast cold starts and quiet output matter more than an interactive UI.
- Agent Harness Research: Experiment with system prompt and tool design on a deliberately minimal, readable Apache-2.0 codebase.
- Local-Model Coding: Run agentic coding against a locally hosted model without any dependency on a specific cloud provider.
- Browser-Based Demos and Playgrounds: Compile to WebAssembly and run the agent client-side with networking delegated to browser fetch.