Giga AI — context engineering for AI coding assistants

• Giga AI

  This page

  PricingRival leads

  • Subscription tiers rather than credits, split by codebase size
  • Annual billing lowers the monthly rate on every tier
  • A free trial and a money-back window instead of a permanent free plan
  • Enterprise is a call, with custom deployment and team context
  • No separate inference bill — it is meant to improve the AI subscription you already have

• Repomix

  PricingRival leads

  • Free and open source under the MIT licence
  • No account, no hosted service and no usage metering
  • You spend only the compute you already have
  • Web app, CLI, browser extension and MCP server all included

• Gitingest

  PricingRival leads

  • Free and open source
  • No account and no payment step anywhere in the flow
  • Hosted web version, plus a Python package and a browser extension
  • Self-hosting is the escape hatch if you want the whole thing on your own hardware

• Greptile

  PricingRival leads

  • A free starter allowance covering one active developer
  • Paid tiers are priced per seat, with credit allowances per seat
  • Enterprise adds self-hosting and custom deployment
  • Repositories are unlimited; the meter runs on review activity instead

1. Giga AI — official site
2. Giga AI — pricing
3. Giga AI — privacy policy
4. Giga AI — terms of service
5. Repomix — official site
6. Gitingest — official site
7. Greptile — official site

Free tier

No

Pricing summary

There is no permanent free plan here, and that is the first thing to price in. Giga sells subscriptions — an entry tier for smaller codebases and a larger one for bigger ones, monthly or yearly, with a free trial and a money-back window standing in for a forever-free option. Enterprise is a call rather than a checkout, and it is where private deployment and shared team context live. What actually separates the two self-serve tiers is scope, not features: the number of files a codebase can contain and how many projects you can analyse at once both rise as you pay more. That makes the tier choice a function of your repository, not of your ambition, and it is the number worth checking against your own project before you subscribe, because a codebase that outgrows its tier pushes you up a price band rather than degrading quietly. Two further costs belong in the arithmetic. The first is the assistant: Giga never meant to replace Cursor, Claude Code or Copilot, so this is a subscription on top of the one you already carry — the argument for it has to be that the assistant you are paying for becomes materially more useful, not that you can cancel something else. The second is the AI subscription's own usage: fewer wrong-file rewrites and fewer re-prompt loops spend fewer tokens, which is where the vendor's "saves twenty hours a month" headline actually comes from, and it is a marketing figure rather than a measurement — treat the time saving as the hypothesis you are testing during the trial. Live band names, current allowances and the exact guarantee window belong on the official pricing page rather than in a summary like this one.

Reviewed on 19 September 2026 · Giga AI — pricing

• Whole-repository analysis instead of a file at a time

  The premise is that the ordinary limits of an assistant — a few open files, whatever you paste, a search that only fires when the model thinks to run it — are a context problem rather than a capability problem. Giga scans the entire codebase and produces a description of it, which is then made available to the assistant. Nothing about the assistant changes; what changes is what it knows before it answers.

• Several views of the project, not one summary

  The output is deliberately plural: the components that exist, the API endpoints, the data structure, the main functions, and how they connect. Those maps answer different questions — a component inventory is useless when the question is about a field name, and a schema is useless when the question is where a button lives — and having all of them available is the difference between an assistant that knows the project and one that knows a file.

• Summaries that update as the code moves

  The analysis is re-run as the project changes rather than being a document you write once and forget. This is the part that distinguishes it from a prompt you paste, and it is the one to test during a trial: a context layer that drifts out of date is worse than none, because the assistant will treat an obsolete description as fact.

• It improves the assistant you already use

  Cursor, Claude Code, Codex, VS Code, Copilot, Windsurf and more are named as targets, and the product is explicit that it is not a replacement for any of them — no new editor, no new chat interface, no change to how you prompt. For anyone already settled into a tool, that is the whole proposition: keep your setup and make it less wrong.

• Language and framework agnostic

  The analysis reads structure and patterns rather than syntax, so it is claimed to work across JavaScript, Python, Java, Go, Rust and the rest, without per-stack configuration. That matters for the common case of a project that mixes a typed front end with a scripting back end, where a context tool tied to one ecosystem would see half the repository.

• A memory layer and a history dashboard

  Beyond the project maps, the paid tiers include what the product calls self-learning AI memory and a chat-history insights dashboard. Read that as conveniences around the core feature rather than reasons to buy: they help a team see how the assistant is being used and keep useful conventions around, but the value being paid for is still the accuracy of the answers.

• MCP access and shared team context

  MCP access is included across the tiers, which means the same context can be exposed to an agent or a workflow rather than only to an editor chat, and the enterprise tier adds shared context across a codebase and aligned automation across teams. For a small team this is the feature that turns one person's good setup into everyone's default.

• Cloud analysis with a stated no-storage policy

  The repository is read on Giga's servers. The company's stated position is that code is not stored and not trained on, that sensitive paths can be excluded through .gitignore conventions, and that no credentials are required for the analysis. Those are policy commitments rather than architectural guarantees, which is the distinction to keep in mind when the code in question is a client's or an employer's.

1. 1

   Analyse before the change, not after the mess

   The value arrives when the assistant is asked to touch something structural — a refactor, a new route, a migration — because that is where ignorance of the surrounding code costs the most. Running the first analysis as part of setting a project up, rather than after the first session where the AI duplicated a helper, is the difference between using it as a tool and using it as a repair.

2. 2

   Re-analyse when the shape changes, not on every save

   The summaries exist to be current; they do not need to be re-derived after every edit. The signal to re-run is structural: new directories, a schema migration, a dependency swap, a new API surface. Treating re-analysis as a ritual after every save adds overhead without changing an answer, while postponing it through three weeks of restructuring is how a context layer turns into a liability.

3. 3

   Read the generated files, and keep them in the repository

   The output is text about your own project, and reading it once is genuinely instructive — it surfaces the conventions you follow by habit and the ones you only follow in half the codebase. Keeping the files under version control also makes changes to them reviewable, so when the assistant's behaviour shifts, the cause is a diff rather than a mystery.

4. 4

   Stop restating conventions in every prompt

   The practical payoff is subtractive. Instructions that used to be repeated — where the API client lives, how errors are shaped, which layer owns validation — belong in the project rules the assistant already reads. Removing that preamble shortens prompts, and it removes the failure mode where the convention is stated in one chat and forgotten in the next.

5. 5

   Exclude what should not be read

   Environment files, generated bundles, vendored code, anything with a credential in it and anything under a client confidentiality clause should be out of the analysis, using the same ignore conventions the repository already has. Doing this before the first run takes a minute; discovering afterwards that a token was inside the scanned set is the sort of thing that ends a trial.

6. 6

   Do not let the summary replace the diff

   A project map is a description of what the code was meant to be, and the diff is what actually changed. Code review still has to be done on the change itself, with the summary as background rather than as evidence — teams that start trusting the overview over the patch are using it backwards, and it is the one way this class of tool makes quality worse rather than better.

• Solo builders whose side project outgrew the chat window

  This is the audience the product was written for and it shows: the founder's own account is of watching an assistant break a project as it grew past the point where the whole thing fit in a conversation. The natural customer is one person maintaining a real application — a few dozen components, a database, some integrations — who cannot afford to re-explain the architecture every session and does not want to stop using the assistant they like.

• Builders working without a traditional engineering background

  People shipping with agents rather than writing every line themselves feel context loss more acutely, because they cannot always tell a bad suggestion from a good one in a file they have never read. A written description of the project's structure is unusually valuable to that audience: it turns the codebase back into something they can hold an opinion about, and it reduces the rework loops that come from accepting plausible-looking output.

• Small teams that need one shared version of the truth

  Once more than one person prompts an assistant against the same repository, conventions start drifting — each teammate's chats carry their own assumptions, and the assistant's answers diverge accordingly. A shared context layer pulls them back together, and it is the point at which a per-developer setup stops being a personal productivity trick and starts being team infrastructure.

• Developers who move between two or three assistants

  Anyone who keeps Cursor open for editing, asks Claude Code to run a refactor, and uses Codex or Copilot in between has the same problem three times over. Because the context lives outside any one assistant, the same project understanding follows you across tools — and that portability is a stronger argument than the per-assistant features, since it is the one thing a built-in indexing layer cannot offer.

• People inheriting a repository or picking up a new stack

  The first week in an unfamiliar codebase is spent building a mental model that the assistant also lacks. Analysis files give both of you the same starting picture — where the entry points are, what the data model looks like, how the pieces connect — which shortens the phase where every question needs a senior colleague and every change risks a hidden dependency.

A hosted analysis service with editor and MCP integrations

Giga is not a local binary. Analysis happens on their infrastructure, the results are consumed through extensions for the assistants you already run, and MCP access exposes the same context to agents and workflows. There is no offline mode and no self-hosted edition, so an air-gapped or strictly regulated environment is out of scope rather than a configuration question.

Language-agnostic by design

The claim is that structure and patterns are read rather than syntax, which is why no per-language setup is documented and why mixed JavaScript, Python, Go or Rust repositories are supported within one project. The corollary is that the analysis is only as good as the conventions it can infer — on a codebase with no discernible structure, there is less to summarise.

Where the code goes, and what is promised about it

Your source is uploaded to be analysed. The published commitments are that code is not stored, not used for training, and that no API keys are required; sensitive paths are excluded through the ignore conventions the repository already uses. Verify the current wording in the privacy policy and terms before pointing it at anything confidential, because those documents — not the marketing page — are what you would rely on.

Tiers capped by codebase size and project count

The self-serve tiers are drawn by how large a codebase can be analysed and how many projects can be handled at once, with the entry tier aimed at smaller codebases and the popular tier at medium to large ones. The headline features are largely the same across them, so the upgrade decision is a scope decision. The exact ceilings are deliberately not quoted here: they are the numbers most likely to have changed since this page was reviewed.

What it does not do

It does not plan or execute work — no agents, no commands, no autonomous edits. It does not review code, gate CI or triage bugs, which is the neighbouring job that dedicated review products own. It does not run locally. And it does not replace an assistant: remove Cursor, Claude Code or Copilot from the picture and there is nothing left for it to improve.

An early-stage vendor, and vendor-reported numbers

Giga is a young product from a small team, backed by Y Combinator and published by Giga Next Inc., which matters if you are considering it for a codebase you will maintain for years: features, tiers and terms move quickly at this stage. The figures on its own pages — the hours saved per month, the percentage of bugs removed, the user count — are marketing claims, not measurements, and should be read as the outcome you are testing during the trial.