Google Antigravity vs JetBrains AI: Which Enterprise AI Coding Tool Handles Large Codebases Better?

Google Antigravity and JetBrains AI Assistant represent fundamentally different approaches to AI-assisted development: Antigravity is an agentic development platform launched in November 2025 with multi-surface orchestration capabilities but lacks publicly documented enterprise specifications for pricing, security certifications, and multi-repository handling. JetBrains AI Assistant offers mature IDE integration, SOC 2 certification, and zero-data-retention options, but it has over 5,000 documented issues in its official bug tracker.

After spending three weeks evaluating both tools across our enterprise codebase, the philosophical difference became clear immediately. Google Antigravity wants to replace your workflow. JetBrains AI wants to enhance it.

• Agent-first development: Google Antigravity enables AI agents to autonomously plan and execute complex, end-to-end software tasks across editor, terminal, and browser environments
• IDE-integrated assistance: JetBrains AI Assistant provides deep native integration with language-specific customization, agent mode capabilities, and local model support

When I first launched Antigravity, the agent immediately started planning a feature implementation I'd described in a single sentence. According to Google's official documentation, "agents don't just suggest code; they plan entire features, execute across multiple surfaces, verify their own work, and learn from your feedback." That's exactly what I observed: the tool wanted to own the entire workflow. For teams exploring autonomous development workflows, this approach feels genuinely different.

JetBrains AI took the opposite approach. The moment I opened IntelliJ, suggestions appeared inline without disrupting my existing patterns. For teams already invested in JetBrains IDEs, this significantly reduces friction. However, the tool's VSCode support, launched in May 2025 as a public preview, remains immature, with only 19,389 installs five months post-launch, which limits options for mixed-IDE teams.

The question isn't which tool is objectively better. It's the philosophy that matches how your team actually works. Teams evaluating SOC 2 compliance requirements will find JetBrains documentation more complete, while teams prioritizing innovation may accept Antigravity's documentation gaps.

Google Antigravity vs JetBrains AI at a Glance

This comparison table highlights key dimensions on which Google Antigravity and JetBrains AI diverge, based on official documentation.

Google Antigravity vs JetBrains: AI Context and Codebase Understanding

When I tested context handling across both platforms using our 200K-file legacy monorepo, the differences were immediately apparent. Antigravity's large context window lets me reference distant dependencies in a single prompt. JetBrains identified local patterns more quickly but lost track of cross-module relationships.

Google Antigravity leverages Gemini's massive context windows of 1-2 million tokens, which theoretically allows processing approximately 30,000 lines of code simultaneously. According to Google's developer blog, Sourcegraph's Cody product was tested with 1M token context using Gemini 1.5 Flash. In practice, I found the large context helpful for understanding broad architectural patterns but less useful for precise refactoring.

JetBrains AI removed its previous restrictive 3.5 KB context limit, now allowing full utilization of underlying model context windows. According to JetBrains YouTrack documentation, "We've removed the limits on our side regarding the context size." However, JetBrains defines concrete AI Credit quotas with a 30-day reset cycle, and I burned through credits faster than expected during intensive refactoring sessions.

Here's what I learned: raw context window size tells only part of the story. Experienced developers emphasize that "you don't provide the entire codebase as context. You engineer what context it will get when needed." This matches my experience. Context engineering matters more than raw context size, and selective retrieval of project-specific dependencies proves more effective than indiscriminate full-codebase analysis.

For teams evaluating AI coding assistants for large codebases, understanding how AI coding tools break at scale helps set realistic expectations for both tools.

Google Antigravity vs JetBrains AI: Enterprise Security and Compliance

During my evaluation, I hit a wall trying to complete our enterprise procurement checklist. JetBrains had answers for most security questions. Antigravity didn't.

JetBrains AI Assistant holds SOC 2 certification and offers no data retention options, which satisfied our compliance team. However, the ISO 27001 status remains unconfirmed in public documentation, and I had to escalate to JetBrains sales to get clarity. Google's AI coding tools provide ISO 27001 and ISO 27017 certifications and are pursuing FedRAMP authorization, but Antigravity specifically lacks publicly documented security certifications, given its November 2025 launch.

A critical finding for regulated industries: Google Antigravity does not offer on-premise deployment options. When I asked about air-gapped deployment, the answer was simply "not available." JetBrains supports on-premise deployment through integration with OpenAI-compatible LLM servers (llama.cpp, vLLM, LMStudio) for air-gapped environments.

JetBrains' cloud architecture can be configured to use third-party LLM providers such as Anthropic, Google, or OpenAI via a Bring Your Own Key model. Google Antigravity currently lacks publicly documented security certifications and data-handling policies, as it remains in a free public preview. For teams with strict data-residency requirements, this documentation gap is significant.

Organizations with strict data confidentiality requirements should evaluate whether cloud-based code processing aligns with their security policies before deployment. When evaluating enterprise AI security controls, both tools require direct vendor engagement for complete security documentation.

Where Antigravity lacks security documentation and JetBrains routes code through multiple third parties, Augment Code offers SOC 2 Type II certification with on-premise deployment options. Evaluate enterprise security features →

Google Antigravity vs JetBrains AI: IDE Integration and Developer Workflow

In my testing, JetBrains' native IDE integration provided immediate access to language-specific refactoring tools. I could invoke AI-assisted rename refactoring without context switching, and the suggestions felt attuned to my project's patterns. The tool provides language-specific customization, agent mode for project-aware conversations, and the ability to "add files, folders, images, symbols, or other elements for enhanced AI context," according to IntelliJ IDEA Documentation.

Full IDE coverage includes CLion, DataGrip, DataSpell, GoLand, IntelliJ IDEA, PhpStorm, PyCharm, Rider, RubyMine, RustRover, and WebStorm. For teams evaluating enterprise-scale codebases, this broad IDE support reduces fragmentation.

For VSCode users, the landscape shifts significantly. JetBrains launched VS Code support in May 2025 as a public preview extension, which remains less mature than its native JetBrains IDE integrations. When I tried the extension, I immediately noticed it "does not provide language support features like code highlighting, code analysis, or refactoring." The tool is also geographically restricted, with "not working in China" noted in the Public Preview documentation.

Google Antigravity required more adaptation from my traditional IDE-centric workflow. The Agent Manager spawns and orchestrates multiple AI agents across workspaces in parallel, which felt powerful but disorienting at first. The browser-based approach enables parallel agent workflows, but I found myself missing keyboard shortcuts and IDE integrations I'd built muscle memory around.

Neither tool provides native Vim/Neovim support. For our team, where three senior developers use Neovim exclusively, this created an immediate fragmentation problem.

Google Antigravity vs JetBrains AI: Documented Limitations

During my evaluation period, I encountered documented issues with both platforms that enterprise teams should anticipate.

JetBrains AI Assistant Limitations

I had three authentication failures in my first week. It turns out this is a known pattern: JetBrains AI Assistant has over 5,000 open issues in its official bug tracker. Documented problems include recurring authentication failures, such as "Login issues with AI Assistant in VSCode every morning," cost inefficiency where "API rate-limit / API errors consume tokens even when no response is returned," and code formatting failures where "Code blocks in AI Assistant response are split incorrectly."

According to The Register, the February 2024 controversy over JetBrains' unremovable AI Assistant raised concerns about enterprise control. Developers characterized the forced installation as "bloatware" and "a risk to corporate intellectual property." Our security team flagged this during evaluation.

Google Antigravity Limitations

Google Antigravity is still in early public preview with documented stability issues. Early users report crashes, slow performance, and occasional file issues. A security researcher discovered a vulnerability within 24 hours of launch, and Google reportedly does not yet permit its own developers to use the tool internally.

Access complexity compounds these concerns. The platform requires a Google account sign-in and currently lacks detailed privacy documentation, SOC 2 certification, or comprehensive data residency options. Enterprise security features and audit logs are mentioned but not yet implemented in the preview version.

Neither tool provides native Vim/Neovim support. Both lack published benchmarks for multi-repository context handling at enterprise scale.

Google Antigravity vs JetBrains AI: Pricing Comparison

In my experience navigating enterprise procurement, I found both vendors' pricing models frustrating to evaluate without direct sales engagement.

JetBrains operates a credit-based licensing system with four published tiers:

Credits reset every 30 days and are shared across all licenses under the same customer account. When users exceed their monthly allocation, organizations must purchase top-up credits at undisclosed rates. I burned through my AI Pro credits in about two weeks of active use, which would make budget planning difficult at scale.

Google Antigravity is currently free in public preview with "generous rate limits" that refresh every five hours. No paid tiers or enterprise pricing have been announced. This free access is strategic: Google is collecting usage data to improve the platform. Enterprise teams should note that the free preview status means there are no contractual SLAs, no security certifications, and no guaranteed service levels.

Cost Predictability Concerns

The JetBrains credit system introduces significant cost unpredictability. API rate-limit failures and errors consume tokens without providing value, which I experienced firsthand when network issues caused failed requests that still counted against my quota. Enterprise teams managing multi-file refactoring projects should account for credit-consumption patterns during evaluation.

Google Antigravity vs JetBrains AI: Which Tool Fits Your Team?

Based on my testing and the documented capabilities of both platforms, here's how I'd match your primary need to the right tool.

Choose Google Antigravity If:

Your team is ready to adopt agent-first development workflows with multi-surface orchestration across editor, terminal, and browser environments. You value autonomous task execution over single-IDE integration. Your organization can operate with cloud-based code processing without requiring published security certifications. You have resources to evaluate an extremely new product lacking publicly documented enterprise specifications.

The agent-first approach makes sense for teams building greenfield applications where autonomous planning and execution can accelerate feature delivery. If you're comfortable with rapid iteration and willing to provide feedback to shape a maturing product, Antigravity's approach is genuinely compelling.

Choose JetBrains AI Assistant If:

Your team is deeply invested in JetBrains IDEs and values deep native IDE integration. IntelliJ IDEA, PyCharm, and other JetBrains products offer mature workflows that AI Assistant enhances rather than replaces. You need SOC 2 certification with documented compliance controls to meet enterprise procurement requirements.

Cost predictability concerns can be managed through credit monitoring via the JetBrains Console, though you'll need to track usage carefully. Your development team should be prepared to actively manage a multi-repository context through custom indexing, since native support for 50-500 repository environments is not documented.

Teams with established JetBrains standardization will see the fastest time-to-value. VSCode users should note the significant capability gap in the preview extension.

Neither Tool If:

You require an on-premise deployment without a JetBrains IDE dependency. Your team includes Vim/Neovim users since neither tool supports these editors. You manage 400K+ file repositories where architectural understanding becomes critical. A multi-repository context at enterprise scale is essential, and you cannot wait for vendor specifications.

Bridge the Gap Between Innovation and Stability

The Google Antigravity vs JetBrains AI decision exposes a fundamental gap in the AI coding assistant market: Antigravity's agent-first architecture represents genuine innovation but remains in free public preview with no enterprise specifications, security certifications, or published pricing. JetBrains AI's mature IDE integration offers proven workflows but faces 5,000+ documented issues.

For enterprise teams managing legacy codebases, neither tool provides native specifications for multi-repository context handling at the 50-500 repository scale. Both require direct vendor engagement to obtain critical missing specifications.

Augment Code eliminates this trade-off with deep, project-wide context awareness that outperforms both tools for multi-file refactoring and complex codebase navigation. The Context Engine processes over 400,000 files through semantic dependency analysis, and the recently launched Remote Agent feature enables advanced workflows that neither Antigravity nor JetBrains currently match. The 70.6% SWE-bench score validates this approach, while SOC 2 Type II and ISO 42001 certifications meet enterprise compliance requirements. Broad IDE support spans VSCode, JetBrains, and Neovim.

For teams that need both development acceleration and codebase-wide understanding, the choice isn't between Antigravity and JetBrains.

Book a demo to see how Augment Code handles your codebase →

✓ Deep project-wide context engine analysis

✓ Enterprise security evaluation (SOC 2 Type II, ISO 42001)

✓ Multi-file refactoring capabilities demonstration

✓ Remote Agent feature for advanced workflows

✓ Integration review for VSCode, JetBrains, or Neovim

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