Comparisons
Data Governance
Enterprise AI
Factory vs Devin: where your engineering data goes
October 1, 2026 - 5 minute read
Droid is Factory's coding agent. Engineers can work with it in a terminal or editor, or delegate a task and review the resulting changes. Devin is Cognition's coding agent, with managed sessions for delegated work and a CLI for working directly with it.
Giving either tool access to a private repository raises questions that a hosting label cannot settle. Code read locally may be sent to a model elsewhere, and test output may be retained in a saved session. A Factory vs Devin privacy review needs to follow those copies. Both vendors document private deployment options as of October 2, 2026, with different responsibilities and limitations for each configuration.
Factory vs Devin on data handling
Factory's data-flow documentation distinguishes local filesystem activity, model requests, and telemetry. Droid's agent loop runs on the machine where Droid is installed. File contents included in prompts or tool context can still leave that machine through the configured inference path.
Suppose an agent investigates a failing integration test in a private repository. It reads a source file, includes part of it in a model request, and saves the test output in a session. If that session is synchronized or exported, the task has created several copies of engineering data. Each destination needs its own access and retention policy.
Cognition's enterprise documentation states that customer data is stored within the customer's tenant for dedicated deployments. It also describes retaining Devin data for the customer relationship unless specified otherwise. Establish which parts of that commitment cover the proposed deployment, including model processing and support access.
For either vendor, follow the test investigation from the checked-out file to the model response and retained session. Record where each copy goes, who can access it, and when it is deleted. Factory's separation of the runtime from the configured model or gateway makes the inference destination an explicit part of that review. An internal gateway can still forward requests to an external provider, whose agreement must cover the data it receives.
Factory vs Devin on deployment boundaries
Factory documents cloud-managed, hybrid, and fully airgapped deployment patterns. These patterns differ in their runtime dependencies and data boundaries. A hybrid deployment can combine customer infrastructure with selected cloud features, while the fully airgapped pattern has no Factory cloud dependency at runtime.
The same documentation describes an Enterprise EU deployment with separate regional backend, session storage, and inference endpoints. It also identifies an important exception: organization pointers, user profiles, and billing data remain in the global US deployment. A requirement that every category of data remain in Europe needs to account for that exception.
Devin's enterprise deployment documentation distinguishes Enterprise Cloud from Customer Dedicated Deployment. Compare the proposed implementation, including customer and vendor responsibilities, rather than translating a deployment label into an assumed guarantee.
Cognition's CLI reference includes devin airgap doctor for checking airgapped configuration and model endpoint connectivity. Include that CLI path alongside the hosted and dedicated options. Require the applicable deployment guide, license terms, model requirements, and feature limitations for the exact Devin configuration under consideration.
Factory's airgap runbook is explicit about Droid's behavior. A dedicated Enterprise build skips Factory sign-in, cloud session sync, crash reporting, update checks, and Factory-bound telemetry. It uses the custom model endpoints and policy sources configured inside the environment.
For that dedicated build, Factory-managed models and Factory Router are unavailable in Airgap Mode. Cloud-dependent capabilities such as session sharing, Slack integration, and hosted analytics are unavailable too. Internal models, artifacts, Git access, and collectors become part of the customer's operating responsibility.
Factory's September 23, 2026 Router report describes a different scope from the dedicated Airgap Mode runbook. Factory Private, in private preview, supports routing with internally hosted model endpoints, including air-gapped deployments. The report says model selection and task execution stay within the organization's environment. Confirm preview access and the supplied build rather than assuming this capability applies to every airgap installation.
Factory also distinguishes offline behavior from enforcement. Airgap Mode skips Factory-bound work, but network isolation comes from the customer's infrastructure and sandbox policy. Verify which build is proposed and which destinations it needs before approving the network rules.
Separate training, retention, and model access
Cognition's security documentation describes a training opt-out that also enables zero data retention with its model providers. Confirm the plan, applicable defaults, and contract. A model-provider retention commitment does not automatically delete application sessions, source-control artifacts, or customer-managed logs.
Factory's managed settings expose separate controls for cloud session synchronization, cloud session retention, and model access. The documented session-retention setting accepts 14 to 365 days. That setting governs cloud sessions, not every copy of data throughout a customer's environment.
Model governance is another separate decision. A centrally selected endpoint can make it easier to establish which provider agreement applies. Factory documents allowed custom-model base URLs and controls over whether users can add their own custom models. A personal API key should not silently introduce a destination that has not passed review.
Factory's BYOK documentation states that custom model credentials stay local and that custom models are available in the Droid CLI and desktop app. They do not appear in the hosted web or mobile platforms. That surface distinction matters when a proposed workflow depends on an internal gateway.
Ask both vendors for a written treatment of deletion, backups, support access, and any contract-specific exceptions. Keep those answers attached to the selected plan and architecture. No-training, model-provider zero retention, application retention, and network isolation should remain separate entries in the approval record.
Keep telemetry inside the privacy review
Factory's telemetry privacy controls distinguish metrics from optional message-content spans. Content export is off by default and requires a customer-configured collector. Factory's collector does not receive spans.
That distinction is narrower than saying no content can ever reach any Factory service. Cloud session synchronization has its own control and purpose. A telemetry setting should not be used as evidence about a separate application data flow.
Factory documents aggregate telemetry mode, which removes specified user identifiers and suppresses message content. Some attributes, including session and repository context, remain. Review the exported fields against the organization's privacy policy rather than assuming that the word “aggregate” makes every datapoint anonymous.
There is a second important qualification. If a customer enables message-content export, the documentation says that the content is raw, without PII scrubbing or secret detection. That collector needs appropriate access control and retention. A customer-owned destination can still expose sensitive data if it is configured carelessly.
For a private deployment, test telemetry with synthetic prompts and harmless tool output. Inspect the actual records at the collector. Record the fields and destinations that appear, and confirm what happens when content export is disabled.
Choose the boundary the team can prove
A useful pilot begins with a representative repository containing no production secrets. Restrict egress to the approved destinations, run a bounded engineering task, and inspect the network and storage evidence. Exercise a denied endpoint as well as a successful request.
For an airgapped evaluation, repeat the test without public connectivity and through a restart. Confirm that model access, policy distribution, artifact installation, and the permitted engineering workflow still operate. Review which cloud features have deliberately been removed from the design.
The review should end with a specific deployment choice and an account of the source files, model requests, sessions, and telemetry it produces. Factory's configurable destinations and offline controls are useful when they match those requirements. Evaluate Cognition's dedicated deployments and airgapped CLI against the same requirements. Choose the configuration the team can verify and operate, including the copies of data it must retain or delete itself.
Review a Factory deployment against your required data boundary.
