Factory vs Devin: Droid workflows, privacy, and deployment

Factory vs Devin, or Droid vs Devin, is a comparison most enterprise engineering teams make sooner or later. Cognition launched Devin as an autonomous "AI software engineer" and built it around managed coding sessions. Factory builds Droid, which works in the terminal, in the Factory App, in editors, and in CI. Teams evaluating agents for real work usually end up with both on the shortlist.

On the surface they look alike. Both can investigate a bug, change code, and run tests. Both support interactive development, where an engineer steers, and delegated engineering work that comes back for review.

The differences show up later, when a reviewer pushes back, a migration spans several services, the work needs to run inside CI under company model policy, or a security review asks where code and session records go. The comparison below uses product documentation as of October 2026.

Factory vs Devin at a glance

Droid is one part of a software factory

The biggest difference is scope. Devin is a coding agent you assign work to. Factory treats the agent as one stage in a larger loop it calls a software factory.

In that loop, bug reports, customer feedback, and requirements become planned changes. Droids build, test, review, and secure those changes before they ship. Monitoring the deployed software then feeds the next round of work.

This matters for a pilot. If you only test "write this function," the two products can look the same. If you test the whole path from ticket to merged and monitored change, you are testing the system around the agent, and that is where Factory has put most of its work: Missions for planning, Droid Computers for environments, Droid Exec for CI, review automation, and model policy for governance.

Benchmark results

Factory has published results on Terminal-Bench, a benchmark of 80 human-verified terminal tasks covering build repair, dependency management, service configuration, and code changes. A task only counts when its hidden tests pass within the time limit.

In Factory's September 2025 report on Core v0.1.1, Droid ranked first at 58.75% with Opus 4.1. Droid with GPT-5 scored 52.5%. Claude Code with Opus scored 43.2% and Codex CLI 42.8%. Devin was not part of that comparison.

Factory's October 2026 Droid 1.0 post added a Terminal-Bench 4 head-to-head that included Devin. Droid scored higher with both models tested. With Claude Opus 5.5, Droid passed 65.2% of tasks to Devin's 59.6%, at 24% lower cost and 16% less time per passed task. With GPT-6.1 Sol, Droid passed 57.6% to Devin's 45.0%, at 37% lower cost and 11% less time.

Devin has no way to turn off its tool for asking the user a question. On GPT-6.1 Sol tasks where it stopped to ask, the post counted the task as passed if Codex passed it, so the adjustment favors Devin.

A newer result matters more for cost. In Factory's June 2026 Router evaluation, Factory Router reached 99% of Claude Opus 4.7's pass rate on Terminal-Bench 2 at 20% lower cost per session.

These are vendor-published numbers. Treat them as a reason to include Droid in a pilot, then measure both agents on your own repositories.

Interactive work: correcting the agent

Take a dependency migration. The new library breaks an older API caller, and a reviewer asks you to keep the existing error response.

The agent now has to change both the code and the tests, then leave a result that someone else can review or pick up. A fix that only changes the explanation leaves the bug in place.

The Factory App gives Droid sessions a visual workspace with project switching, machine connections, diffs, and review feedback. Engineers who prefer the terminal can do the same from the CLI or their editor. Devin offers Devin CLI and Devin Desktop alongside its delegated sessions.

Test both interfaces with a real correction. The tool you use to fix a change matters as much as the tool you use to start it. Check that the handoff shows which tests passed and which ones the environment could not run.

Droid vs Devin on delegated work

Here the two products take different approaches.

Missions give Factory a structured path for bounded, multi-feature work. The engineer and Droid agree on a plan, split it into features and milestones, and the engineer approves it before work starts. Progress stays visible, so you can step in when an assumption changes. For the migration, the plan should name the API behavior that must stay compatible and the caller tests that must pass before a milestone is done.

Missions need a repository that can be tested automatically. An agent cannot prove a migration works if the test environment is missing.

Cognition documents parallel managed sessions, playbooks, and reusable instructions. Separate sessions suit independent maintenance tasks. A coordinated migration also needs a shared compatibility decision and a safe order for changing producers and consumers, so check how each setup handles that order. In either product, a failed compatibility check should stop the dependent change.

Saved instructions and persistent machines

Cognition's Knowledge documentation marks that feature deprecated and moves shared guidance to Skills in Plugins, available across web, CLI, and Desktop. Droid reads AGENTS.md files and Skills from the repository, so instructions are versioned and reviewed with the code.

Droid Computers solve a different problem. They keep the whole environment between sessions, including installed packages, files, and configuration. Teams can register their own machines or use Factory-managed compute.

Persistent context and persistent machines both need review. Written instructions go stale. A long-lived machine can collect uncommitted changes, credentials, or dependencies that the repository does not describe. Keep the build definition and test command in the repository so the next engineer can reproduce the result without inheriting the machine's history.

CI and model choice

Droid Exec runs Droid non-interactively in scripts and CI. It writes output for the surrounding job and only changes files when you allow it. That lets a team try Droid inside an existing pipeline without moving the whole process to a new interface. Set it up so a required test that cannot run fails the job.

Model choice is the other big lever. Devin CLI supports model configuration, Adaptive routing, and Fusion pairings. Droid works with the models Factory supports, custom models through your own provider endpoints in the CLI and desktop app, and Factory Router, which picks a cheaper model when it can finish the task reliably. Custom models are not available in the hosted web or mobile apps.

For enterprises, the policy layer matters as much as the model list. Factory's managed model policy lets admins approve models and custom endpoints centrally. Separate local preferences from enforced organization controls when you reproduce a run. Record the model and routing mode with every pilot result, because changing either one changes what you are measuring.

Factory vs Devin on data and deployment

Giving either agent a private repository raises questions a hosting label cannot settle. Code read locally may be sent to a model elsewhere, and test output may be saved in a session. Factory's data-flow documentation separates local filesystem activity, model requests, and telemetry. Droid's agent loop runs on the machine where it is installed, but file contents included in prompts or tool context still leave that machine through the configured inference path.

Cognition's enterprise documentation distinguishes Enterprise Cloud from Customer Dedicated Deployment and states that customer data is stored within the customer's tenant for dedicated deployments. Its CLI reference includes devin airgap doctor for checking an airgapped configuration and model endpoint connectivity. Its security documentation describes a training opt-out that also enables zero data retention with its model providers. Ask which parts of each commitment cover the deployment you would actually run, including model processing and support access.

Factory documents cloud-managed, hybrid, and fully airgapped deployment patterns. Its Enterprise EU deployment has a separate regional backend, session storage, and inference endpoints, while organization pointers, user profiles, and billing data remain in the global US deployment. The dedicated airgap build skips Factory sign-in, cloud session sync, crash reporting, update checks, and Factory-bound telemetry. For that dedicated build, Factory-managed models and Factory Router are unavailable, along with session sharing, Slack integration, and hosted analytics. Network isolation comes from the customer's infrastructure and sandbox policy.

Retention and telemetry need their own entries in the review. Factory's managed settings control cloud session sync and cloud session retention separately, and the session-retention setting accepts 14 to 365 days. Its telemetry privacy controls keep message-content export off by default and send it only to a customer-configured collector. If a customer enables that export, the content is raw, without PII scrubbing or secret detection, so the collector needs its own access control and retention.

How to run a fair pilot

Pick three tasks on the same repository state: an investigation-led bug fix, a repeated maintenance task, and a coordinated change across several files. Use separate working copies without production credentials or customer data. For a private deployment, restrict egress to the approved destinations and check where source files, model requests, sessions, and telemetry actually land.

Add one realistic reviewer correction to each task and check that it reaches both code and tests. For the longer task, stop after a failure and ask another engineer to resume from the diff, test output, and next action.

Then measure what matters:

• Time from assignment to approved merge, including setup, failed attempts, and review
• Model and compute cost across all attempts
• Missing tests and regressions found in review
• How much reviewer effort each change needed

Session counts and activity logs do not tell you whether the work was good.

The answer may differ by task. Choose based on the patches, test results, and reviewer effort each product produced under the setup your team will actually use.

Frequently asked questions

What is the main difference between Droid and Devin?

Both are coding agents that can investigate a bug, change code, and run tests. Factory builds Droid into a wider software factory: interactive sessions, planned multi-feature Missions, persistent Droid Computers, Droid Exec for CI, and centrally governed model choice. Devin centers on managed coding sessions, with CLI and Desktop interfaces for interactive work.

Has Devin been compared with Droid on Terminal-Bench?

Yes. In Factory's October 2026 Droid 1.0 post, Droid scored higher than Devin on Terminal-Bench 4 with both models tested: 65.2% vs 59.6% with Claude Opus 5.5, and 57.6% vs 45.0% with GPT-6.1 Sol. With GPT-6.1 Sol, a task where Devin stopped to ask a question counted as passed if Codex passed it. Factory's September 2025 Terminal-Bench report placed Droid first on Core v0.1.1 at 58.75% with Opus 4.1, and Devin was not part of it. Run both on your own tasks before deciding.

How should an enterprise team compare Droid and Devin?

Give both the same three tasks on the same repository state: an investigation-led bug fix, a repeated maintenance task, and a coordinated change across several files. Add one reviewer correction to each, then measure time from assignment to approved merge, review effort, and total cost.

Which private deployment options do Factory and Devin document?

Factory documents cloud-managed, hybrid, and fully airgapped deployment patterns, plus an Enterprise EU deployment. Cognition documents Enterprise Cloud and Customer Dedicated Deployment, and its CLI includes a command for checking an airgapped configuration.

Further reading

• Evaluating coding agents on your codebase
• Air-gapped AI development that works offline
• Reviewing agent-generated pull requests