Capy Review (2026): Pricing, Verdict & Alternatives
Capy lets engineering teams run up to 25 parallel AI coding agents in sandboxed VMs, planning, building, and reviewing PRs across Claude, GPT, and Gemini.
Capy is built for engineering teams that want to delegate whole workstreams instead of pairing with one AI at a time. Up to 25 agents work concurrently in isolated sandboxes, each opening its own pull request for human review before merge. The differentiator is the three-stage Captain, Build, and Review pipeline, not just a single autocomplete-style assistant, making it best for teams of three or more shipping several features at once.
Capy is a cloud coding platform from Scrapybara, launched April 2026, that runs up to 25 AI coding agents in parallel, each in its own sandboxed VM and git branch. A Captain agent plans and specs the work, Build agents write and test code, and a Review agent checks pull requests before a human merges, across Claude, GPT, Gemini, and Grok models.
Maker: Scrapybara · Autonomy: semi autonomous · Maturity: GA
Underlying models: Claude Opus 4.6, Claude Sonnet 4.6, GPT-5.2, GPT-5.2 Codex, Gemini 3 Pro, Grok 4.1 Fast, Kimi K2.5, Qwen 3 Coder
About Capy
Capy is a cloud-based AI coding platform built by Scrapybara, Inc., a San Francisco startup founded in 2024 by Nalin Semwal and Justin Sun, who met working on AI agents at MultiOn. Instead of pairing a developer with one AI assistant at a time, Capy lets an engineering team hand off entire workstreams to a fleet of agents that plan, write, test, and open pull requests in parallel. The platform launched its cloud IDE on April 2, 2026, and is backed by a $500K seed round from Y Combinator, BoxGroup, and CRV after going through YC's Fall 2024 batch. The system runs on a three-agent pipeline. A read-only Captain agent reads the codebase, refines a task into a spec, and breaks it into tickets. Build agents then execute the actual work inside an isolated Ubuntu VM on its own git branch: editing files, installing dependencies, running commands, and committing changes. A Review agent checks the resulting diff and leaves feedback before a human approves the merge, so nothing ships without a person in the loop. Capy is model-agnostic, letting a task run on Claude, GPT, Gemini, Grok, Kimi, or Qwen depending on what the job needs, rather than locking a team into a single vendor's model. Capy is built for engineering teams of three or more who are shipping several features at once and want to run workstreams concurrently instead of serially. Reviewers have pointed out it is less useful for a solo developer who wants a tight, real-time pairing experience like Aider, since Capy's model is closer to delegating a batch of tickets and checking in on results later. It fits teams that already think in terms of parallel branches and PR queues. Pricing starts with a Pro plan and scales through usage-credit packs up to a custom Enterprise tier with bring-your-own API keys, SSO, and dedicated support (see pricing FAQ for exact tiers). The platform is cloud-only, with no CLI or terminal mode, and is SOC 2 Type II certified with zero-data-retention agreements across its model providers. Since its April 2026 cloud IDE launch, Capy has continued adding model support (including newer Claude, GPT, and Gemini releases) and expanding integrations with GitHub, Slack, and Linear. It has not published SWE-bench, GAIA, or other standard agent benchmark scores, which is a gap next to more benchmark-forward rivals like Devin.
Pricing
Pro is $20/mo ($16/mo billed annually) and includes 3 seats, with extra seats at $10/mo each. Usage-credit packs add bonus credit: $100 for $105, $500 for $550, and $2,000 for roughly $2,300. Enterprise pricing is custom and includes bring-your-own API keys, custom VM sizing, and SSO/SAML.
Key Features
- Captain planning agent: A read-only agent reads the codebase and turns a request into a refined spec broken into tickets before any code is written.
- Parallel Build agents: Up to 25 Build agents can run concurrently, each in its own sandboxed Ubuntu VM and git branch, editing files and running commands.
- Automated Review agent: A dedicated Review agent checks each diff and leaves feedback before a human approves the pull request for merge.
- Model-agnostic task routing: Teams can assign a task to Claude, GPT, Gemini, Grok, Kimi, or Qwen depending on the job, instead of being locked to one model vendor.
- SOC 2 Type II certified sandboxing: Every task runs in an isolated VM under a SOC 2 Type II program with zero-data-retention agreements with model providers.
Strengths
- Supports up to 25 concurrent coding agents in isolated sandboxed VMs, the main differentiator reviewers cite versus single-agent tools.
- SOC 2 Type II certified with zero-data-retention agreements across Anthropic, OpenAI, and Google, which matters for regulated teams.
- Full plan, build, and review pipeline (Captain, Build, Review) automates more of the PR workflow than a single autocomplete-style assistant.
Weaknesses
- Cloud-only with no CLI or terminal mode, unlike competitors that offer a local or terminal-based workflow.
- Independent reviewers estimate parallel agent teams burn roughly 7x the tokens of a single session, so the $20 entry tier can run out well before a full 25-agent workload is done.
- No published SWE-bench, GAIA, or other standard benchmark scores, making it harder to compare raw coding performance against rivals like Devin.
Frequently Asked Questions
What is Capy and what does it do?
Capy is a cloud-based AI coding platform built by Scrapybara, Inc., a San Francisco company founded in 2024 by Nalin Semwal and Justin Sun. Instead of one developer pairing with one AI assistant, Capy lets a team delegate entire workstreams to a fleet of agents that plan, code, test, and open pull requests. Its cloud IDE launched on April 2, 2026, and is marketed as one of the first platforms built specifically for running many coding agents at once. Each task runs through a three-stage pipeline: a Captain agent plans the work, a Build agent executes it in an isolated VM, and a Review agent checks the result before a human merges it. Up to 25 of these tasks can run concurrently. The company went through Y Combinator's Fall 2024 batch and is backed by a $500K seed round.
How much does Capy cost in 2026?
Capy's Pro plan is $20 per month, or $16 per month if billed annually, and includes 3 seats with extra seats at $10 per month each. Beyond the Pro plan, usage is sold in credit packs that include a bonus: a $100 pack delivers about $105 of usage, a $500 pack about $550, and a $2,000 pack roughly $2,300, so the bonus grows with pack size. There is no free tier. Enterprise pricing is custom and covers bring-your-own API keys, custom VM sizing, SSO/SAML, and dedicated support. Reviewers have flagged that the $20 entry tier can be consumed quickly if a team runs many parallel agents at once, since credits do not roll over and overage is billed at a 1:1 rate.
Is Capy fully autonomous?
No, Capy is semi-autonomous. Its Build agents can independently write code, install dependencies, run commands, resolve merge conflicts, and fix CI failures inside their own sandboxed VM without asking for help at every step. But a human is still expected to review the diff and approve the pull request before anything merges into the main codebase, so it is not a zero-touch system. This makes it less hands-off than platforms like Devin that push further toward end-to-end delegation, and more hands-off than a real-time pairing tool like Aider. The Review agent adds an automated check layer, but final merge authority stays with a person.
What AI model powers Capy?
Capy is model-agnostic rather than built on a single proprietary model. Teams can assign a task to models from the Claude family (including Opus and Sonnet), GPT family (including GPT-5.2 and GPT-5.2 Codex), Gemini 3, Grok 4.1 Fast, Kimi K2.5, or Qwen 3 Coder, choosing whichever fits the task's complexity and cost profile. This lets a team route a quick fix to a cheaper, faster model and a harder refactor to a stronger reasoning model in the same workspace. Capy does not train or fine-tune its own foundation model; its value is in the orchestration layer (Captain, Build, Review) that wraps whichever model is selected, not the model itself.
What are the best alternatives to Capy?
Cursor is a strong alternative for a single developer who wants a familiar editor with AI assistance built in, rather than a team-oriented parallel-agent platform. Devin is worth considering if you want a more benchmarked, end-to-end autonomous engineer and are comfortable with its roughly $500 per month pricing. Windsurf suits teams that want an integrated local IDE experience with AI features rather than a fully cloud-based multi-agent workflow. Choose Capy specifically when the bottleneck is running several coding tasks at once across a team, not just getting help on one task at a time.
Who is Capy best for?
Capy is built for engineering teams of three or more who are juggling multiple features, fixes, or workstreams at the same time and want to run them in parallel rather than one after another. A startup shipping several small features before a release, for example, could assign each to its own Capy task and review the resulting pull requests together. It is not a great fit for a solo developer who wants tight, real-time back-and-forth with an AI on a single file, since Capy's model is closer to delegating a batch of tickets than pairing live. Teams without an existing GitHub PR workflow will also get less value from it.
How does Capy compare on benchmarks?
Capy has not published SWE-bench, GAIA, WebArena, or other standard coding-agent benchmark scores as of mid-2026. This is a real gap compared to competitors like Devin, which publicize benchmark results to support autonomy claims. Capy's public case for quality instead rests on its architecture (separate planning, building, and review stages) and its SOC 2 Type II certification rather than a leaderboard score. Teams evaluating Capy against benchmarked competitors should expect to run their own side-by-side test on a representative ticket rather than relying on a published number.
How do you get started with Capy?
To start, sign up at capy.ai and connect your GitHub account so Capy can access the repositories you want it to work on. From there, create a task by describing what you want done; the Captain agent will read the relevant code and turn it into a spec before any Build agent starts working. You can assign which model family handles the task depending on its complexity. Once a Build agent finishes, the Review agent checks the diff and the task moves to a needs-review state for a human to approve and merge. New users are on the $20 per month Pro tier by default, which includes 3 seats, so a small team can be testing real tickets within the first session.