# Devin vs Windsurf: which coding workflow should you use? The names have changed. Compare the desktop review loop with a cloud handoff, then score the work that comes back. agentlist.iocomparisonUpdated Oct 12, 20268 min read If you are choosing between Devin and Windsurf, first separate the cloud workflow from the desktop workflow. The names have moved: the official Windsurf Cascade documentation now redirects to [Devin Desktop’s Cascade documentation](https://docs.devin.ai/desktop/cascade/cascade). The catalog calls this entry Windsurf / Devin Desktop. Its documentation describes Cascade and Devin Local as two local agents. An old comparison of two independent products will leave you with the wrong shortlist. Our starting recommendation: use a desktop trial for work that needs frequent steering, and a cloud trial for a bounded task you can hand off with a reproducible environment. A local editor does not, by itself, mean local model inference or that source code stays on your machine. ## Choose the handoff you need | Your task | First trial | Evidence to request | | --- | --- | --- | | Adjust a UI while inspecting it | Desktop, with short edit-and-review cycles | A visible result at the target size, plus the relevant diff | | Fix a well-described test failure | Cloud if the environment can be recreated | The reproduction before the fix, passing checks after it, and a reviewable patch | | Explore unfamiliar code | Desktop if your questions will change as you learn | File references and an explanation you can verify | | Repeat a migration across repositories | Pilot one bounded cloud task before parallelizing | Per-repository checks, scope limits, and a rollback path | [Devin’s cloud guidance](https://docs.devin.ai/essential-guidelines/when-to-use-devin) describes a remote working environment with shell, editor, and browser tools. That makes environment setup part of the purchase decision. A repository that builds only on a founder’s laptop is not ready for an unattended comparison. ## Check which agent owns the permissions Desktop permission settings differ by agent. The [terminal documentation](https://docs.devin.ai/desktop/terminal) gives Cascade Disabled, Allowlist Only, Auto, and Turbo execution levels. Its denylist requires approval; it does not make the command impossible to run. Devin Local has a separate permissions model. Record the selected agent and mode in your trial notes instead of writing only “Windsurf.” Begin with a test repository and no production credentials. Include a command that should require approval. Observe whether execution stops at that boundary. A prompt that says “be careful” is not evidence that permissions are configured correctly. ## Compare the bill after the result is accepted [Current Devin billing documentation](https://docs.devin.ai/admin/billing) separates self-serve plans, with included quota and on-demand credits, from Enterprise billing in Agent Compute Units. Do not apply an old ACU calculation to every current plan. Save the plan, allowance, selected model, and usage shown in your account before each trial. Count setup separately from repeatable work. A cloud environment may take an hour to prepare and then serve many tasks. Desktop work may start immediately but need more active supervision. Neither pattern wins until you count what your team actually spends. ``` Cost per accepted change = (subscription allocation + metered usage + human review cost) / accepted changes Also record: setup time, elapsed time, corrections, rejected runs. ``` For example, if five runs cost $12 in usage and two produce accepted changes, the usage component is $6 per accepted change. It is not $2.40. Add review time and the relevant subscription allocation before using that number in a purchasing decision. These are illustrative figures, not measured Devin costs. ## What changes when you move the task to the cloud? Imagine a three-person team repairing an intermittent checkout failure. In the desktop workflow, a developer can point at the failing request, change the brief, and inspect the next edit immediately. In a cloud handoff, those observations need to become durable inputs: the failing fixture, the expected response, the setup command, and the limits on what the agent can change. If you omit them, the cloud run is also a test of how well the agent guesses your environment. Prepare the environment once, then give a fresh run only the written brief. If it cannot install dependencies or reach the test service, fix that setup before scoring its coding ability. Separate “could not run the project” from “ran it and produced an incorrect fix.” Both cost time, but they require different remedies. Repeating a broken setup with a more expensive model does not answer the environment question. The handoff back matters just as much. Ask for the starting commit, final diff, exact checks, any checks that could not run, and a short reproduction of the corrected behavior. Review what happens if the base branch changes during the task. A correct patch against yesterday’s code still needs integration work before it can ship. ## Where each workflow can disappoint | Workflow | Likely friction to investigate | A useful boundary | | --- | --- | --- | | Desktop with frequent steering | Small corrections accumulate into a long supervised session | Count active prompting, review, and repair minutes, not just model time | | Cloud with an incomplete brief | The run completes a plausible interpretation of the wrong task | Write an observable acceptance condition before starting | | Several parallel cloud tasks | Individually acceptable patches edit the same interfaces | Give each task an owned area and review the combined result | | Either workflow with broad access | A local credential or connected tool enables an unintended action | Prove the approval boundary with a harmless controlled operation | For a small team, the attention budget can be the deciding factor. Suppose desktop takes 12 active minutes and cloud takes 7, but preparing the cloud environment takes 90 minutes. The five-minute saving would recover that setup effort after 18 comparable accepted tasks. That is an illustrative break-even calculation, not a product measurement. If the work changes every week, the setup might never amortize; if the same maintenance task recurs, it might. ## A two-task trial beats a feature-count contest 1. Use the same starting commit for a reproducible bug. Require a small patch, a demonstrated fix, and no unrelated edits. 2. Try an interface adjustment whose acceptance depends on a screenshot or browser check. Record how often you intervene. 3. Give both workflows a time and spending limit. Keep failures in the denominator. 4. Review the patches without treating the agent’s completion message as proof. Use the [coding-agent trial scorecard](https://www.agentlist.io/articles/coding-agent-trial-scorecard) to record acceptance. Choose the workflow that repeatedly delivers acceptable changes within your attention budget. If desktop wins on visual work and cloud wins on isolated maintenance, keep that division. You do not need one tool to win every category. Agentlist editorial review. Sources checked October 12, 2026. This is a documentation-based buying guide, not a hands-on performance benchmark. Trial procedures and recommendations are our analysis. ## Compare the work that comes back. Use the fieldbook’s evaluation worksheet to record acceptance, review time, and what remains unverified. [Get the evaluation worksheet](https://www.agentlist.io/ebook?from=devin-vs-windsurf) --- Source: [Devin vs Windsurf: which coding workflow should you use? | agentlist.io](https://www.agentlist.io/articles/devin-vs-windsurf). This is the public page rendered as Markdown; interactive controls require the website.