The software factory platforms worth comparing

June 10, 2026 - 4 minute read

Building a software factory means connecting coding tools to a repeatable process for testing, reviewing, and releasing changes. An editor agent can help an engineer work through a change, while a delegated agent can prepare a pull request from an assigned task. Neither supplies the whole delivery process on its own.

Factory is the company behind Droid, one of the coding agents a team can use in that process. Comparing software factory platforms means checking how their agents fit the team's source control, CI, and review practices. The product descriptions reflect documentation available on October 1, 2026.

Choose the workflow before the feature list

For an interactive fix, the engineer needs to inspect a diff and correct the approach without losing the relevant context. For an issue-to-PR assignment, the execution environment must reproduce the build and return a patch with test results. A cross-repository migration also needs dependency ordering and checks against the exact producer and consumer revisions.

These differences are more useful than counting interfaces. A terminal can run on a laptop or inside a remote job. An editor can connect to a cloud agent. Inspect where commands execute, where model context goes, and where session state persists rather than treating an interface name as a deployment boundary.

Define completion in terms of the task. A generated patch may still need correction. A pull request may have passed local tests while missing an integration check. Keep merge and production release behind their existing controls regardless of which product prepares the change.

Compare the products at the point of use

Factory supplies Droid through the App, CLI, editor integrations, and automation workflows. Its Missions organize larger assignments into a reviewed plan, features, and milestones. This structure is relevant when several changes must meet a shared acceptance condition, while a normal session can handle a smaller fix.

Factory's deployment patterns describe managed, hybrid, regional, and airgapped operation. Select the actual offering before assessing privacy or operating cost. Installing Droid on customer hardware does not, on its own, move its control plane or inference into that network.

GitHub Copilot combines developer-facing coding tools with a coding agent that works through GitHub's pull-request workflow. For a team already using GitHub issues, reviews, and Actions, the important integration point is the resulting PR and its checks. Review the agent's execution environment and permissions alongside repository settings. A PR is an artifact for review, not proof that the requested behavior works.

Cursor combines its editor experience with a CLI and cloud agents. Its cloud-agent environment provides isolated remote execution, so an IDE-only description misses a substantial workflow. Compare a local interactive task separately from a remote assignment, including repository setup, network access, and how an engineer corrects the result.

Claude Code is available in the terminal, IDE, desktop app, and browser, according to its overview. The relevant choice includes the interface and model-service arrangement. A local terminal process and a hosted model endpoint are separate parts of the data path, so evaluate the documented configuration rather than reducing the product to its original CLI surface.

Aider is a terminal-based tool for working on code with a model. Its model documentation describes supported model connections. Running the tool on infrastructure you own gives you control of that process, while the selected inference service determines where its context is processed. The team still needs a reproducible environment and review process around it.

Continue offers IDE extensions and a terminal coding agent that can edit files, run commands, and work through multi-step tasks. Its terminal and editor entry points deserve evaluation against the intended workflow. Model configuration, supporting services, and team administration should be assessed separately from the location of the extension.

These products can occupy overlapping roles. A team may keep its editor while changing the agent used in its terminal or CI jobs. A narrower tool can be sufficient when the surrounding delivery system already supplies reliable orchestration and validation.

Inspect model and governance boundaries separately

Model availability, custom endpoints, and administrative policy are different capabilities. A product may offer several models without allowing an arbitrary gateway. A developer may be able to configure an endpoint while an organization policy prohibits its use. Record the effective setting for the specific interface under review.

Factory's custom-model documentation limits BYOK to the CLI and desktop surfaces. It also distinguishes documented endpoint examples from fully tested official APIs. That is the level of specificity needed for any product when an internal inference gateway is a requirement.

Follow the complete data path. Local execution, customer-owned orchestration, regional hosting, no-training terms, and disconnected operation answer different requirements. An endpoint's private hostname does not establish the location of the provider behind it. Likewise, a service's security attestation does not automatically cover a customer's runner or gateway.

For administrative controls, distinguish defaults from enforcement. Factory's Enterprise Controls govern matters such as allowed models and autonomy limits. Verify the applicable controls for each candidate by attempting a harmless prohibited action under the intended execution identity.

Audit records also need precise interpretation. An administrative log can record a policy change without recording every command an agent ran. Activity metrics can show tool use without showing the correctness of the patch. Retain the repository revision, test output, and review decision independently of a vendor dashboard.

Test the handoffs the platform must support

Choose a representative change with a known reproduction and acceptance test. Start each candidate from the same revision, provide equivalent access, and record differences in model or execution configuration. Include setup and failed attempts in the result rather than comparing only successful generations.

Ask a reviewer to introduce a real constraint, such as preserving an older client's error response. Check whether the correction reaches both implementation and tests. For delegated work, have another engineer resume after a failure using only the retained task state and repository artifacts.

Compare the resulting diff, relevant tests, reviewer corrections, and total time to approval. Add billed model usage and compute costs where the measurement sources support them. A public benchmark can suggest difficult test cases, but it cannot determine the cost or success rate of a different repository and deployment.

Select the product whose workflow covers the missing part of delivery without creating an unsupported data path or unmanageable operating burden. That may be a coordinated Factory Mission, a GitHub-centered PR workflow, an editor-led interaction, or a terminal tool inside infrastructure the team already operates. The decision should follow the tested handoffs rather than a universal feature winner.

Talk with Factory

Discuss your team’s software development, privacy, or deployment requirements.