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AI Coding Tools

Windsurf

Windsurf’s Cascade agent, assessed by a team that codes in AI IDEs every day — not sold to you, weighed up with you.

Overview

Windsurf is an AI-native code editor — a fork of VS Code, originally built by Codeium — that puts an agent at the centre of the editing experience rather than bolting autocomplete onto the side. Its headline feature is Cascade: an agentic flow that tracks what you are doing and holds a live picture of the codebase, so it can make coordinated multi-step changes across several files instead of one-line suggestions. It indexes your repository for context, offers inline AI as you type, and wraps the whole thing in a clean, deliberately uncluttered IDE. The pitch is that the AI stays “in flow” with you, picking up on your actions rather than waiting to be told everything from scratch.

We use AI IDEs daily — this is how we actually write software — so what we tell you about Windsurf comes from living in these tools, not from a feature comparison table. Windsurf sits in a small, fast-moving category alongside Cursor, the other leading AI-native editor, with GitHub Copilot as the autocomplete incumbent most teams already have, and terminal agents such as Claude Code coming at the same problem from a different direction. Each of these is genuinely good at something and genuinely frustrating at something else, and the honest answer to “which one” depends far more on your codebase, your team, and your review discipline than on any single benchmark.

Our role is not to sell you Windsurf. It is to help you evaluate whether an agentic AI IDE earns its place in your workflow, run a fair trial across the real contenders, and put the guardrails and review habits in place that make these tools a net gain rather than a source of confident, plausible, wrong code sitting in your repository. We are candid that this is an evolving space where the winners are not yet settled — and we will tell you when the pragmatic choice is the tool you already have.

Best for — Teams evaluating or adopting an agentic AI IDE who want the multi-file, codebase-aware editing Windsurf’s Cascade offers — and want it trialled honestly against Cursor and their existing tools first.

Why teams choose Windsurf

  • Coordinated multi-file changes

    Cascade’s value is that it works across the codebase, not one file at a time — adding a feature and updating every call site, or carrying a refactor through the modules it touches. When the change genuinely spans several files, this is materially faster than editing each by hand, provided you review what it did.

  • Context without constant setup

    Because Windsurf indexes the repository and tracks your recent actions, you spend less time spoon-feeding the model file paths and background. It picks up the surrounding code and the direction you are heading, which is the difference between an assistant that helps and one that needs managing.

  • A familiar editor, not a new one

    Built on VS Code, Windsurf keeps the keybindings, extensions and layout your team already knows, so the learning curve is the AI workflow rather than the editor itself. That lowers the adoption cost compared with a tool that reinvents the whole environment.

Why businesses choose Windsurf

  • You want agentic, multi-file editing inside a familiar VS Code-based IDE, and Cascade’s codebase awareness fits how your team actually works.
  • You are choosing between Windsurf and Cursor and want a fair, hands-on trial on your own code rather than a decision made from marketing claims.
  • You prefer a clean, in-flow editor that keeps the AI unobtrusive over one that constantly surfaces suggestions.
  • You want the tool adopted properly — with review discipline, data-handling settings and a rollback plan — rather than dropped on the team and left to cause quiet damage.

What we build with Windsurf

The capabilities this technology is genuinely strong at — and what we most often build with it.

  • Cascade — the agentic flow

    Cascade is Windsurf’s distinguishing feature: an agent that keeps track of your actions and the codebase state to plan and apply coordinated multi-step edits across files. Rather than answering one question at a time, it works towards the change you asked for and shows its steps, which is powerful precisely because it is doing more than a single suggestion — and therefore needs more careful review.

  • Codebase indexing and context

    Windsurf indexes your repository so the AI can retrieve relevant code as context instead of relying only on the file you have open. Good indexing is what lets the agent make changes that are consistent with the rest of the codebase, and it is one of the clearest differences in quality between AI IDEs.

  • Inline AI and autocomplete

    Alongside the agent, Windsurf offers inline completions and edits as you type — the fast, low-commitment assistance that most closely resembles the Copilot experience. We help teams tune how heavily they lean on this versus the agent, because the two suit different kinds of work.

  • Multi-file edits with review

    When Cascade proposes a change spanning several files, Windsurf presents it as a reviewable set of edits rather than silently applying them. We treat this review step as non-negotiable: it is the gate between the agent’s speed and your codebase’s integrity.

  • A VS Code-based environment

    Because Windsurf is a VS Code fork, the extension ecosystem, settings and keybindings your team relies on largely come with it. This keeps the adoption cost to learning the AI workflow rather than relearning an entire editor.

  • In-editor chat and commands

    Windsurf keeps chat, edits and context together in the editor, so you can ask about code, request a change, and see it applied without leaving the file you are in. The design intent is to keep you in flow rather than context-switching between tools.

Use cases

  • Feature work across several files

    Adding a capability that touches a model, an endpoint, a UI component and its tests — the kind of change where the coordination is the effort. Cascade’s multi-file editing is aimed squarely at this, with your review as the safeguard.

  • Refactoring and renames

    Carrying a rename, an interface change or a structural refactor through every place it touches. An agent that holds the codebase in context can do the mechanical spread quickly, leaving you to check intent rather than chase call sites by hand.

  • Onboarding into an unfamiliar codebase

    Using the indexed context and chat to understand how an existing system fits together before changing it — asking where a behaviour lives and how a flow works, which is often the slowest part of joining a project.

  • Evaluating AI IDEs for a team

    Running Windsurf against Cursor and your existing setup on real tickets to decide what, if anything, to standardise on — a structured trial rather than a hunch, which is exactly the work we help teams run.

When Windsurf is the right choice

  • Your team is comfortable working in a VS Code-style editor and wants an agent that can carry out multi-file changes — a refactor, a new endpoint plus its tests, a rename that ripples through several modules — rather than just completing the current line. Cascade’s codebase awareness is where Windsurf feels most distinct.
  • You are evaluating AI IDEs seriously and want a considered, hands-on comparison against Cursor before you standardise a team on one. Both are strong; the differences are real but subtle, and worth trialling on your actual code rather than a demo repo.
  • You value a polished, low-friction editor experience and want the AI to stay in the background until you need it, rather than a tool that constantly interrupts. Windsurf leans towards a clean, in-flow feel.
  • You have a modern codebase in a mainstream stack — TypeScript, Python, React, Node — where the indexing and context features have plenty to work with. The value drops on very large monorepos or unusual, poorly-documented languages where the model has less to draw on.
  • Wrong for: a team with no appetite for reviewing AI-generated changes carefully, or one working on code where a confidently-wrong edit is expensive to catch. An agentic IDE amplifies both good and careless engineering; without review discipline it will happily ship you a mess that looks right.

Windsurf: pros and cons

Strengths

  • Cascade’s agentic, codebase-aware editing handles multi-file changes well, which is where single-line autocomplete tools leave you doing the coordination by hand.
  • A clean, polished IDE built on VS Code, so existing extensions, themes and keybindings mostly carry over and the environment feels familiar from day one.
  • Strong repository indexing and inline context mean the AI needs less manual prompting to understand what it is working on than lighter tools do.
  • The in-flow design keeps the AI unobtrusive until called on, which suits engineers who want assistance without a constant stream of interruptions.

Trade-offs

  • It is a newer entrant in a fast-moving, consolidating market. Adoption, ownership and long-term viability are genuinely worth weighing — standardising a whole team on any single AI IDE right now is a bet on an unsettled category.
  • Indexing proprietary code means your source is being processed for context, which raises real data-handling and privacy questions. These are manageable with the right plan and settings, but they must be designed for rather than assumed away.
  • Like every agentic tool, Cascade can make confident, plausible, wrong edits — and a multi-file change is harder to eyeball than a one-line suggestion. Without disciplined review it will introduce subtle bugs that read as correct.
  • It is a paid subscription per seat on top of whatever you already run, and switching editors carries a real team-adoption cost in habits, muscle memory and lost time before the tool pays back.

How it fits your codebase

The core of any AI IDE is its context pipeline: how it indexes your repository, what it retrieves for a given task, and how much of your code it sends to the model to reason over. Windsurf’s Cascade leans on this indexing to make coordinated edits, so the practical quality you get depends heavily on your codebase being something the indexer can make sense of — reasonably structured, in a mainstream language, not a sprawling undocumented monorepo where useful context is hard to surface.

We treat the tool as one part of an engineering system, not a replacement for one. That means it sits inside your existing version control, review and CI, and every agent-generated change lands as a normal, reviewable diff that must pass the same checks as any other. The agent’s ability to touch many files at once is its strength and its risk; the architecture that makes it safe is the ordinary one — small commits, real review, tests that run — applied without exception to AI-authored code.

Because Windsurf is a VS Code fork, it slots into most existing setups without much disruption: the same extensions, linters and formatters, the same repository layout. That familiarity is a genuine advantage over tools that impose a new environment, and it keeps the decision reversible — if the trial does not pay off, stepping back to plain VS Code is a small move rather than a migration.

In practice

The performance that matters here is not raw editor speed but the quality and latency of the agent’s work: how often Cascade produces a change you can accept with light edits versus one you have to unpick, and how quickly it responds while indexing a large repository. On a well-structured codebase in a common stack the hit rate is high and the round-trip feels fast; on a very large or unusual codebase, indexing is heavier and the suggestions are less reliably on target.

The honest measure is time-to-correct-change, not time-to-first-suggestion. An agent that produces a multi-file edit in seconds but needs twenty minutes of careful review and correction has not saved you twenty minutes. We help teams track the real figure — how much agent output ships with minor edits versus major rework — because that is what tells you whether the tool is earning its subscription, and it varies a lot by codebase and by how the team uses it.

Data handling and privacy

The central consideration is that indexing your repository means your proprietary source code is being processed to build context, and agent requests send code to a hosted model. That is not a reason to avoid the tool, but it is a reason to be deliberate: understand what is indexed and retained, choose the plan and settings that match your data posture, and check the training and retention terms rather than assuming them. For teams with sensitive code or regulatory obligations, these questions decide whether the tool is usable at all, and they should be answered before rollout, not after.

Beyond data handling, agent-generated code is untrusted until reviewed. The same discipline applies as to any AI-authored change: it can introduce an insecure pattern, a leaked secret, or a subtly wrong access check with complete fluency. We insist that AI-written code passes the same review, secret-scanning and CI gates as human-written code — no exceptions for output that happens to look polished — and we help teams set the organisational settings and policies that keep the tool inside their compliance boundary.

Rolling out to a team

Scaling an AI IDE across a team is mostly an adoption problem, not a technical one. The cost is per seat, and the real spend is the human time to shift habits — engineers work differently with an agent, and the payback only arrives once the team has built the review reflexes and prompting instincts that make the tool safe and fast. Rolling it out without that investment tends to produce either abandoned licences or, worse, unreviewed AI code quietly accumulating in the codebase.

We favour a measured rollout: a small group trials Windsurf against the alternatives on real work, the team agrees where the agent is trusted and where it is not, and the review and data-handling policies are set before the tool spreads. Because it is a VS Code fork, the decision stays reversible and the per-seat model scales cleanly — but in a consolidating market we would rather help you standardise deliberately, with an exit in mind, than commit a whole organisation to one AI IDE on the strength of an early demo.

Windsurf integrations & ecosystem

The technologies we most often pair with it — each links to how we work with it.

How we work

We start by being honest about whether you need an agentic AI IDE at all, and if so, we run a structured trial rather than picking a winner from marketing. Windsurf goes head-to-head with Cursor, with Copilot as the incumbent baseline and Claude Code representing the terminal-agent alternative, all on your real tickets and your real codebase. The output is a clear read on which tool fits your team and your code — and sometimes the answer is that the tool you already have is good enough.

Once a tool is chosen, we adopt it properly: agreed review discipline for agent-generated changes, the data-handling settings that match your obligations, and a rollout that starts small and expands only once the habits are in place. Because we use these tools every day to run our own work, our guidance comes from operating them, not observing them — including the unglamorous parts, like where the agent reliably gets things wrong and needs watching. We would rather you adopt one AI IDE well than three badly.

The service behind it

Delivered throughAI Development

What we build with Windsurf

The disciplines this technology most often shows up in — from a first build to taking over and stabilising an existing one.

How we deliver

  1. 01

    Discover

    We map the system, the constraints and the business it serves — including the parts nobody documented.

    Architecture brief

  2. 02

    Architect

    Decisions get made, written down and defended before a line of production code exists.

    Decision records

  3. 03

    Build

    Short cycles against working software. You see progress in the product, not in a status deck.

    Shipping increments

  4. 04

    Operate

    Monitoring, incident response and iteration. The system is alive, so the engagement is too.

    Runbooks & SLOs

Industries we use Windsurf in

Domain knowledge changes what gets built. A few of the sectors we know before the first meeting.

Also in AI Coding Tools

Why teams choose us for Windsurf

  • We use AI IDEs every day

    This is not a tool we researched for a report — we write software in AI IDEs daily, including the agentic multi-file editing Windsurf is built around. Our view of where it helps and where it disappoints is first-hand and blunt, not repeated from a vendor page.

  • Even-handed, not selling a favourite

    We have no stake in you choosing Windsurf over Cursor, Copilot or Claude Code. We will run a fair trial and tell you what actually fits your team and codebase — including when the answer is the tool you already have, or no agent at all.

  • Adoption done with discipline

    The risk with agentic tools is confident, wrong code shipping unreviewed. We bring the review habits, data-handling settings and measured rollout that turn an AI IDE into a net gain rather than a quiet source of bugs and compliance exposure.

Typical timeline

  1. 01

    Scoping and baseline

    A few days agreeing what you want an AI IDE to do, capturing how the team works today, and defining how we will judge whether a tool actually helps — the success criteria come before the trial.

  2. 02

    Hands-on trial

    One to two weeks running Windsurf against Cursor and your existing setup on real tickets, measuring how much agent output ships with light edits versus heavy rework.

  3. 03

    Decision and policy

    A clear recommendation, plus the review discipline and data-handling settings needed to adopt the chosen tool safely — or the honest conclusion that your current tooling is enough.

  4. 04

    Rollout and enablement

    A measured rollout to the wider team with enablement on the agent workflow, review habits embedded, and a check-in to confirm the tool is paying back rather than quietly costing.

How pricing works

  • A fixed-price AI IDE evaluation — a structured, hands-on trial of Windsurf against Cursor and your existing tools, ending in a clear recommendation and the reasoning behind it.
  • An adoption engagement to roll a chosen tool out properly: review policies, data-handling settings, team enablement and a measured rollout plan.
  • Ongoing senior engineering where we build your product using AI IDEs day to day, with the tool as part of how the work gets done rather than a separate deliverable.
  • Note that the Windsurf subscription itself is a per-seat cost paid to the vendor, separate from our fee — we help you decide whether those seats are worth buying and how many.

Hire Windsurf engineers

Need Windsurf capacity on your own team? We embed named senior engineers into your existing team — reporting to your leads, working in your rituals — so you add capacity without a hiring cycle.

Hire Windsurf engineers

Common questions

Should we choose Windsurf or Cursor?

Both are strong AI-native editors and the differences are real but subtle — Windsurf leans on its Cascade agent and a clean, in-flow feel, while Cursor is the other leading contender with its own strengths. The honest answer depends on your codebase, your stack and how your team likes to work, which is why we run a hands-on trial of both on your real tickets rather than deciding from feature lists. Whichever you pick, the value comes from adopting it with discipline, not from the badge on the editor.

What is Cascade, and how is it different from ordinary autocomplete?

Cascade is Windsurf’s agentic flow: instead of completing the current line, it tracks your actions and the state of the codebase to plan and apply coordinated changes across multiple files. That makes it genuinely more capable than autocomplete for feature work and refactors — and genuinely more in need of review, because a multi-file change is harder to check at a glance than a one-line suggestion. The power and the risk are the same thing.

Is it safe to point Windsurf at our proprietary codebase?

It is manageable, but it needs deciding rather than assuming. Indexing means your source is processed to build context and agent requests send code to a hosted model, so you should understand what is indexed and retained, choose the plan and settings that match your data posture, and read the training and retention terms. For teams with sensitive code or regulatory obligations we answer these questions before rollout, because they can determine whether the tool is usable at all.

Will the agent write code we can trust without checking?

No — and no AI IDE should be used that way. Cascade can produce confident, plausible, wrong edits, and because it changes several files at once, the mistakes are harder to spot than a single bad suggestion. We treat every agent-generated change as untrusted until reviewed, passing the same review, secret-scanning and CI gates as human-written code. Used with that discipline the tool is a real accelerator; used without it, it ships you subtle bugs that read as correct.

Is it worth switching our whole team to a new AI IDE?

Sometimes, but not automatically. This is a fast-moving, consolidating category, standardising on any one tool is a bet on an unsettled market, and switching editors carries a real cost in habits and lost time before it pays back. Because Windsurf is a VS Code fork the move is reversible and the disruption is modest, which lowers the risk — but we would rather help you trial it on a small group, measure whether it actually saves time, and roll out deliberately with an exit in mind than commit everyone on the strength of a demo.

Building on Windsurf?

A technical conversation with the engineers who would do the work. If we are not the right fit, we will say so on the call.

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