Factory AI vs Devin: two serious autonomy platforms

Verdict and criteria matrix

Factory and Devin should be evaluated as operating systems for delegated engineering work. The central question is how each product turns a request into an environment, a plan, execution, evidence, a pull request, and a governed record. Model quality matters, yet it changes faster than deployment, workflow, and review economics.

What autonomy means in each product

Factory describes Droids as the unit inside a software factory. A Droid can work interactively, execute headlessly, run on a developer machine, attach to a persistent computer, and participate in larger Missions. Specification Mode creates a plan and approval boundary. Enterprise policy determines which models, tools, networks, and environments are available. Autonomy is therefore a configurable runtime property inside a broader system.

Devin describes the product as an autonomous software engineer. A cloud session gets a development environment, explores the repository, edits code, runs commands, uses a browser when needed, and prepares a pull request. Knowledge and Playbooks shape repeat behavior. The product also reaches Slack, ticket systems, APIs, a local CLI, Devin Review, and related Cognition surfaces. Autonomy is packaged as an assignable teammate with a managed work history.

The difference becomes visible when a task fails. Factory invites the platform team to ask whether the spec, model route, tool policy, environment, or Droid workflow needs adjustment. Devin invites the product owner to ask whether the task, Knowledge, environment snapshot, or session instruction needs adjustment. Both approaches can mature into an internal engineering system. They create different ownership structures.

Local CLI and cloud handoff

Droid CLI runs in the current project and supports interactive work plus droid exec for non-interactive automation. Factory can register a machine as a Droid Computer through BYOM, including Linux, macOS, and Windows hosts. Managed Droid Computers persist filesystem state, packages, services, and configuration between sessions. That persistence suits complex environments and raises the importance of machine lifecycle, credentials, patching, and isolation.

Devin CLI runs locally against local files and the local environment. The distinctive command is /handoff: it packages conversation context and the current git branch, then starts a cloud Devin session that continues the job. Cloud Devin normally starts from a repository-specific VM snapshot and works within its managed environment. This makes the transition from a ten-minute local investigation to a long remote run unusually explicit.

Specifications, knowledge, and repeatability

Factory Specification Mode turns a short feature brief into acceptance criteria, technical design, file-level work, testing strategy, and security considerations. Planning is read-only, and implementation waits for human approval. Saving the spec into the repository makes it reviewable beside the code and gives later runs a durable contract.

Devin relies on task prompts, planning inside sessions, repository environment configuration, Knowledge, and Playbooks. Knowledge captures facts Devin should remember about the codebase or organization. Playbooks encode repeatable procedures. DeepWiki builds a navigable explanation of repositories. Together they reduce the cost of rediscovery across independent sessions.

A team should keep source-of-truth facts in the repository or an owned system even when a vendor memory feature is useful. Build commands, architectural invariants, data classifications, and acceptance rules outlive any agent account. Vendor knowledge should cache and route those facts, not become the only copy.

Parallel work and long-running projects

Factory Missions pursue larger goals by decomposing them into features, starting worker sessions, coordinating through git, validating milestones, and recovering from some failures. Factory's own announcement also states the open problems plainly: excessive parallelism can add coordination cost, long plans accumulate errors, and worker scope remains a tradeoff. That is useful honesty for a product sold on autonomy.

Devin encourages multiple sessions for verifiable subtasks. Teams can trigger sessions from tickets, Slack, APIs, schedules, or the web product, then review returned pull requests. Child sessions and structured outputs support more complex orchestration. The practical unit remains a session with a scoped task, which keeps failure attribution clearer than one giant request.

The winning product is the one your team can govern at the desired concurrency. A platform capable of twenty workers has no economic advantage when two reviewers can safely accept four changes a day. Pilot concurrency should start below review capacity, then rise only after acceptance quality stays stable.

Enterprise controls and deployment

Factory explicitly targets regulated and high-security organizations. Its documentation describes Droids on laptops, CI runners, VMs, Kubernetes, devcontainers, and fully airgapped networks. Identity uses SSO and SCIM. Organizations can govern models, tools, access, network policy, telemetry, and data flow. OTEL export allows customer-owned metrics and traces, including session duration, model usage, files touched, commits, pull requests, and tool activity.

Devin Enterprise offers organization administration, SSO, controlled repository access, usage analytics, integrations, environment management, support, and contract terms suited to large deployments. Devin also provides a cohesive hosted product in which session history, pull requests, Knowledge, DeepWiki, and Review live near each other. Buyers seeking one managed control surface may value this more than deployment flexibility.

• Choose Factory when customer-controlled infrastructure, model gateways, airgapped operation, and OTEL ownership are written requirements.
• Choose Devin when a managed service with first-party knowledge, ticket, review, and autonomous-session workflows reduces internal platform work.
• Qualify plan boundaries. Individual-plan behavior cannot stand in for an enterprise deployment, and enterprise slides cannot justify a small-team purchase.
• Test audit evidence. Ask each vendor to trace one task from trigger through prompts, tools, environment, model usage, code changes, review, and final identity attribution.

Procurement should score operational burden on both sides. Factory can fit more infrastructure patterns, which gives the customer more choices to configure and maintain. Devin can provide a more opinionated managed system, which places more trust and dependency on Cognition. The correct burden is the one the organization has staff and controls to own.

Pricing and cost per accepted task

Factory has no public free plan. Pro costs $20 a month, Plus $100, and Max $200. Standard usage is governed by rolling five-hour, weekly, and monthly windows. When standard capacity is exhausted, Droid Core provides a separate pool of selected open-weight models, and prepaid Extra Usage can keep frontier models running. Teams and Enterprise are quoted.

Devin has Free, Pro at $20 a month, Max at $200, and Teams with an $80 monthly minimum. Devin self-serve plans use quota and on-demand credits, with separate billing behavior for Devin Review. The free plan makes a task-level proof easier, although its light allowance is unsuitable for estimating organization capacity.

Convert both meters into cost per accepted task class. Include subscription allocation, overage, environment setup, human scoping, review, re-review, and rollback. Separate small maintenance, feature implementation, migration, incident work, and code review because they produce different consumption and human burden. A blended company-wide average hides which product is good at which job.

When to pick Factory or Devin

A short evaluation should avoid the polished demo path. Give each product one undocumented bug, one well-specified feature, one repeated maintenance task, and one environment with a real private dependency. Require the same acceptance criteria and the same reviewer. The environment task tests deployment; the repeated task tests memory and workflow encoding; the bug tests exploration; the feature tests planning and execution.

Migration notes

Moving from Devin to Factory starts with assets rather than transcripts. Export stable Knowledge into repository documentation. Rewrite Playbooks as skills, commands, hooks, or headless workflows. Capture the VM snapshot setup as versioned environment scripts. Map ticket and chat triggers to Factory integrations or an owned queue. Select a local, BYOM, managed-computer, hybrid, or airgapped target for each task class. Rebuild usage alerts and audit exports before increasing autonomy.

Moving from Factory to Devin starts with approved specifications, agent instructions, skills, model rules, and environment definitions. Convert each repeated Droid workflow into Knowledge, a Playbook, an automation, or a task template. Recreate repository snapshots and secrets through Devin's supported setup. Decide whether local work begins in Devin CLI and moves through /handoff, or starts directly in cloud sessions. Verify that enterprise data, identity, audit, and retention controls match the signed plan.

Questions people ask

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