Atlarix
Freemium
Engineering teams needing private/local AI coding assistance
Generative IDE
PaidAPISelf-HostedAgentic
Most AI code editors will happily rewrite half your codebase the moment you tab-complete the wrong suggestion — no confirmation, no diff, no way back. GIDE puts a gate on every edit before it touches a single line.
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Summary
GIDE is a desktop AI code editor built for teams that cannot or will not send source code to a third-party API. It runs local models fully offline, supports cloud models (Claude, GPT, Gemini) via your own API key, and gates every proposed change through an explicit accept/reject step before writing to disk. The agent reads files, runs tool calls, and shows diffs — all inside the editor. The gate is not optional decoration: the vendor states plan confirmation before edits is a core design constraint. Where it breaks: teams expecting background auto-apply will fight the confirmation model, and the toolchain is new enough that community-sourced workarounds are sparse.
Bottom line: The right pick for an air-gapped team that needs local inference with zero API leakage and is willing to approve every change; a friction point for teams that want a 'just fix it' agent that runs unattended.
Pricing Plans
Subscription- Price
- $19.99/mo
Developer
$19.99per month
Ornith 9B bundled, unlimited local inference, plan-gate, refactoring engine
- 30-day free trial
- Email support
Business
$99.99per month
Everything in Developer plus BYOK cloud models, MCP client, context memory, GIDE Wiki
- Priority email support
Enterprise
Custom
Everything in Business plus air-gapped deployment, SSO/SAML, custom compliance
- Dedicated onboarding
- 1-day support SLA
View full pricing on generativeide.com →
Pricing may have changed since last verified. Check the official site for current plans.
Find your fit
Before you choose Generative IDE
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Pros
- Runs local models fully offline at zero token cost, so teams in air-gapped or regulated environments get AI assistance without any source code touching an external API.
- Write gate requires explicit accept or reject before any file is modified, so a runaway agent cannot silently overwrite production logic while you are looking at another window.
- Model-agnostic design lets you swap between local inference and cloud models (Claude, GPT, Gemini via your own API key) without changing your workflow, so a cost spike or model-quality issue is not a migration project.
- Semantic repo retrieval reads only the files the task actually requires, so context windows stay focused and the agent is not padding every prompt with irrelevant code.
- Self-hosted option and available API mean teams can deploy a consistent toolchain across developer machines without depending on a vendor-controlled cloud service going down or changing pricing.
Cons
- The write gate confirmation model adds a manual step to every discrete edit. Teams running large-scale mechanical refactors — renaming a symbol across 200 files, for example — will click through approval prompts repeatedly or look for a way to batch-accept, which the current docs do not clearly describe. Teams that need unattended bulk changes will evaluate competitors with auto-apply modes instead.
- There is no perpetual free tier. The trial ends, and teams mid-integration who have not yet hit a real production workflow will be asked to commit before they have enough signal. Teams that need a longer evaluation runway before budget approval will stall here.
- MCP integration for external tools is listed as a feature, but the product page provides no detail on which MCP servers are supported or what the setup path looks like. Teams expecting plug-and-play connections to their existing toolchain will spend time on undocumented integration work before they get value from that layer.
About
- Platforms
- macOS, Windows, Linux
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- September 8, 2026
Best For
Who it's for
- Privacy-conscious developers
- Air-gapped or regulated teams
- Users preferring local inference
What it does well
- Offline AI coding in secure environments
- Multi-file refactoring with previews
- Project memory and architecture visualization
- Connecting to external tools via MCP
Integrations
Frequently Asked Questions
- Is Generative IDE free?
- Generative IDE is a paid tool ($19.99/mo). A 30-day free trial is available.
- Does Generative IDE have an API?
- Yes. Generative IDE exposes a developer API. See the official documentation at https://generativeide.com for details.
- Can I self-host Generative IDE?
- Yes. Generative IDE supports self-hosting on your own infrastructure.
- What platforms does Generative IDE support?
- Generative IDE is available on: macOS, Windows, Linux.
Curated lists that include this category
GIDE is a model-agnostic AI code editor that runs on your machine. The core workflow is a chat-driven agent loop: you describe a task, the agent reads the files it needs, proposes a diff, and waits for you to accept or reject before writing anything. The vendor describes this as a ‘write gate’ — it is on by default and the demo sessions on the product page show the approval prompt appearing between every discrete edit. Local models run at zero token cost; cloud models connect via API keys you supply yourself.
The differentiating feature is the combination of offline-first architecture and a plan-confirmation system. The vendor states GIDE is air-gap ready for regulated industries, which means the network dependency is optional rather than baked in. On local models, there are no per-token charges and no data leaves the machine. The agent uses semantic retrieval across the repo — glob patterns, file outlines, targeted reads — rather than loading the entire project into a context window at once, which is how it handles larger codebases without ballooning prompt size.
GIDE fits teams in security-sensitive environments: regulated industries, defense-adjacent work, or anyone whose legal team has flagged code-to-cloud data flow. It also fits developers who want to stay in control of what changes — the approval gate is a feature, not a workaround. Where it breaks: the confirmation-first model is slow for bulk mechanical changes where you trust the agent completely. There is no perpetual free tier, so teams evaluating it hit the trial cutoff before a full production integration cycle. MCP (Model Context Protocol) connectivity is described for external tool integrations, but teams needing deep CI/CD pipeline hooks will be building that glue themselves.
The editor ships with a CLI installer for macOS, Linux, and Windows. Version 2.3.6 is documented on the product page. The API is available, and self-hosting is supported — relevant for teams that need to standardize tooling across multiple developer machines without relying on a vendor-managed cloud instance.
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