Amazon Q Developer vs. Google Antigravity: Which AI Coding Tool Handles Enterprise Codebases?

Every AI coding tool claims to "understand your codebase." Most don't. They understand the file you've opened, maybe the directory, occasionally the repository. But rarely are the architectural relationships that matter for enterprise development.

Amazon Q Developer functions as an IDE assistant optimized for AWS infrastructure with 200,000-token context windows. Google Antigravity (launched November 2025) operates as an agent-first platform in which autonomous agents orchestrate tasks across the editor, terminal, and browser.

One enhances AWS infrastructure work within a single repository. The other explores autonomous development for greenfield projects. Both have value.

Neither replaces cross-repository code intelligence. If your team manages multiple repositories where services depend on each other, stop reading now. Neither Amazon Q Developer nor Google Antigravity will solve your problem.

Explore Context Engine for cross-repository understanding →

In brief:

• Amazon Q Developer targets AWS-focused teams needing IDE-integrated code completion with infrastructure tooling
• Google Antigravity serves organizations exploring autonomous agent orchestration for complex workflows
• Neither tool addresses cross-repository codebase understanding for enterprise environments managing 50-500 repositories. Evaluate Augment Code for multi-repository intelligence

Amazon Q vs. Google Antigravity at a glance

Amazon Q Developer targets AWS-focused teams needing IDE-integrated code completion with infrastructure tooling. Google Antigravity serves organizations exploring autonomous agent orchestration for complex workflows.

Here’s what to evaluate when comparing workspace assistants vs. agent platforms:

• Repository scope: Single workspace or cross-repository intelligence?
• Primary function: Infrastructure assistance vs autonomous task execution?
• Maturity status: Production-deployed vs experimental preview?
• Integration complexity: AWS IAM required vs Google Cloud project setup?
• Compliance clarity: Verified certifications vs inherited cloud compliance?

Here’s how the two tools compare:

However, the most crucial dimension is repository scope. Amazon Q Developer cannot aggregate context across multiple repositories because of its workspace-local indexing architecture.

Google Antigravity focuses on building new features rather than understanding existing codebases. For teams managing 50-500 repositories needing cross-repository intelligence, neither tool addresses the core requirement.

Product philosophy (workspace assistance vs. agent orchestration

Amazon Q and Antigravity aren't competing implementations of the same idea. They represent different theories about where AI fits into the development workflow, and both share the same blind spot.

Amazon Q Developer

I installed Amazon Q, expecting AWS-specific enhancements to standard IDE assistance. The workspace-local architecture became obvious immediately: it indexes only the repository you've opened in your IDE, stored locally at /Users/[username]/.aws/amazonq/cache/.

The @workspace feature lets you include code chunks as context, which works well for single-repository questions. I asked Amazon Q to "explain our CloudFormation stack configuration" — it analyzed the infrastructure code in my open workspace. It provided valuable AWS-specific insights into resource dependencies and security group configurations.

Then I tested cross-repository scenarios. Our authentication service lives in one repository, our API gateway in another, our user service in a third. I asked Amazon Q: "How does our JWT token validation work across these three services?"

It analyzed only the repository I'd opened (the auth service), completely missing that the API gateway transforms tokens before passing them downstream, and the user service validates signatures differently. The architectural blindness isn't a bug; it's fundamental to the workspace-local design.

The platform demonstrates documented expertise in AWS infrastructure tasks, including CloudFormation, the CDK, and Kubernetes. But the 20GB per-customization limit and 10MB file-size constraint mean that large monorepos or enterprises with many repositories hit fundamental architectural barriers.

Google Antigravity

Antigravity operates differently: instead of indexing code for understanding, it orchestrates agents to execute tasks autonomously.

I tested this by asking it to "build a user management system with authentication and role-based permissions." The Manager View showed the agent planning for 12 minutes, then autonomously working for 47 minutes: writing code across 15 files, configuring database tables, setting up auth routes, testing in the built-in browser.

The autonomous execution impressed me. But when I tried pointing it at our existing codebase ("refactor our authentication service to use OAuth2 instead of JWT"), Antigravity couldn't analyze our current implementation because it's designed to build new, not understand existing.

The agent tried to generate an OAuth2 implementation from scratch, following best practices, but that would've required rewriting dependent services the agent couldn't see. This isn't a limitation you can work around with better prompting. It's fundamental to the agent-first design that prioritizes creation over comprehension.

During my three weeks of testing, the preview status showed that the platform crashed twice, agent generation stalled for 8+ minutes three times, and that error recovery required manual intervention. Expected for experimental software, but it's why teams can't deploy Antigravity for production workflows yet.

Context management (local indexing vs. agent orchestration)

How tools handle "context" determines whether they can answer questions about your actual codebase versus questions about code in general. Amazon Q's approach breaks down at repository boundaries. Antigravity's approach doesn't attempt to cross them.

Amazon Q Developer

Amazon Q's 200,000-token context window sounds impressive until you realize it's limited to the workspace. It can process code within a single repository, but according to AWS documentation, "no mechanism exists for cross-repository context aggregation or enterprise-wide code search."

I tested this limitation directly in an e-commerce repo spanning 37 repositories: payment processing, inventory management, user accounts, order fulfillment, and shipping integration — each in a separate repo. I asked Amazon Q to "explain how a customer order flows through our system from checkout to delivery."

It analyzed the order service repository I'd opened, including the API endpoints, database schema, and business logic. But it couldn't see:

• The payment processor in a different repository
• The inventory system that reserves products
• The fulfillment service that creates shipments
• The notification service that sends confirmation emails

The answer was technically accurate for the single repository, but architecturally incomplete for understanding our actual system. This is the fundamental constraint teams hit when evaluating Amazon Q for enterprise use: workspace-local indexing cannot scale to multi-repository intelligence.

The documentation confirms that individual files over 10MB are ignored during indexing, and that the 20GB per-customization limit means large codebases must be split across multiple customizations — each with a separate, non-aggregated context.

Google Antigravity

Antigravity doesn't attempt to solve the problem of understanding the codebase. The platform's knowledge base feature helps agents learn from completed tasks, but it's designed to improve task execution rather than analyze the existing architecture.

When I asked Antigravity to "improve error handling across our application," it analyzed only the code it had generated in previous sessions. It couldn't analyze our production codebase because accessing existing multi-repository systems isn't part of the agent-first workflow.

The Model Context Protocol (MCP) integration provides secure access to Google Cloud data services (BigQuery, Spanner, Cloud SQL), but this is about data access for agent workflows, not code intelligence for legacy systems.

For teams needing to understand how their existing services connect (where changes ripple through distributed systems), agent orchestration can't replace semantic dependency analysis of actual codebases.

Developer workflow (IDE extension vs. standalone platform)

Installation time differs by hours. Learning curve differs by weeks. But the critical difference is architectural: one integrates into existing workflows, the other requires workflow redesign.

Amazon Q Developer

Amazon Q installation took 20 minutes via the VS Code Marketplace: install the extension, authenticate with AWS, and start indexing. The workflow stayed familiar — developers code in their existing IDE, and AI provides inline suggestions.

But the complexity of the AWS integration became apparent during the GitLab setup.

Connecting Amazon Q to our GitLab repositories required:

1. Configure AWS IAM OIDC identity provider
2. Create IAM roles with specific trust policies
3. Set up GitLab application integration
4. Configure repository access permissions

Total deployment time: 2 days with AWS infrastructure team support. For teams without existing AWS infrastructure maturity, this timeline extends further.

The GitHub integration (currently in preview) proved simpler: marketplace installation with OAuth authentication. But the preview status means enterprise teams can't rely on it for production workflows that require stability guarantees.

When I asked three developers to try Amazon Q on their daily work, feedback varied by use case:

• Infrastructure developer: "Really helpful for CloudFormation templates and AWS-specific patterns."
• Application developer: "Suggestions are okay, but not better than Copilot for application logic."
• Full-stack developer: "Can't see relationships between my frontend and backend repos — keeps suggesting changes that would break the API contract."

Google Antigravity

Antigravity is a standalone application (a VS Code fork) with a dual-interface design: Editor View for traditional coding and Manager View for agent orchestration.

Installation took 8 minutes: download the application, authenticate with a Google account, and configure the workspace. But learning the workflow took 50 minutes of documentation reading before I felt comfortable delegating tasks to agents.

The conceptual shift: you describe outcomes, agents handle implementation.

For our senior developers who know exactly what code they want to write, this abstraction layer slowed them down rather than helping.

For junior developers, experiences varied:

• One found it helpful for implementing features where they weren't sure of the approach
• Another got frustrated when agents hit errors: "I don't know how to fix it — that's why I asked the agent to do it"

The preview status showed in daily use: crashes requiring restart, generation stalls requiring manual intervention, and agent errors needing guidance that the junior developer couldn't provide.

These aren't criticisms of experimental software. They are reasons teams can't deploy Antigravity for production work that requires reliability.

Deployment maturity (partial production vs. experimental preview)

Security teams need explicit answers to procurement questions. Both platforms provide ambiguity instead. Amazon Q through documentation gaps, Antigravity through preview status.

Amazon Q Developer

Our security team's audit revealed documentation gaps that blocked procurement approval.

Amazon Q Business (a different product) achieved SOC 1, 2, and 3 compliance in December 2024. But Amazon Q Developer's compliance documentation provides only "generic references" and does not explicitly confirm SOC 2, ISO 27001, HIPAA eligibility, or FedRAMP authorization.

The data residency limitation raised additional concerns: Amazon Q processes all data only in US regions (N. Virginia or Oregon). For our European operations requiring GDPR compliance with EU data residency, this geographic limitation was a blocker.

The platform documentation states ambiguously: "Your content is not used to improve AWS services." Our security team interpreted this as "no explicit contractual guarantee that code won't be used for training"—insufficient for procurement approval in regulated industries that require explicit no-training contracts.

GitHub integration remains in preview (not general availability), so enterprise teams cannot deploy it for production workflows that require stability SLAs.

Google Antigravity

Antigravity operates in public preview (launched November 2025) with no disclosed timeline for production readiness or enterprise security certifications.

Our security team couldn't audit it because the documentation doesn't exist yet:

• No SOC 2 or ISO 27001 certifications for Antigravity itself
• Preview status with no contractual data handling guarantees
• Free access indicating experimentation phase rather than commercial service
• No production SLAs or uptime commitments

The platform inherits Google Cloud's compliance framework (ISO 27001, ISO 27017, ISO 27018, SOC 2), but these apply to the underlying infrastructure, not Antigravity as a specific product with its own security attestation.

During my three-week testing period, the experimental nature showed: 2 complete crashes, 3 instances of 8+ minute generation stalls, and multiple error recovery scenarios requiring manual intervention.

For teams in regulated industries requiring formal security certifications before procurement approval, neither tool meets enterprise standards yet.

Security and compliance (ambiguous vs. preview)

Enterprise procurement fails at the first unanswered security question. Amazon Q's compliance documentation doesn't explicitly confirm the certifications teams need. Antigravity doesn't yet have compliance documentation.

Amazon Q Developer

The compliance gap became a procurement blocker for our team.

When our security team asked for SOC 2 Type II certification, Amazon Q Developer documentation directed us to "AWS services in Scope by Compliance Program" without explicit confirmation. SOC 2 compliance exists for Amazon Q Business (a different product), but doesn't extend to Q Developer.

The code privacy guarantee raised concerns: the documentation states, "Your content is not used to improve AWS services," but provides no contractual training guarantee, unlike competitors. Our legal team interpreted this as insufficient for regulated environments requiring explicit contractual guarantees.

Data residency presented another challenge: US-only processing (East/West regions) means that European customers who require GDPR-compliant EU data processing cannot use the service. This geographic limitation ruled out Amazon Q for our European operations.

A tracked CVE vulnerability (CVE-2025-8217) affected the VS Code extension version 1.84.0 when malicious code was injected through a compromised GitHub token. While the malicious code was present in the distributed extension, a syntax error prevented execution, and no customer environments were impacted. AWS patched this in version 1.85.0.

Google Antigravity

Antigravity's preview status means enterprise security infrastructure doesn't exist yet.

No SOC 2 certification, no ISO documentation, no contractual data handling terms for enterprise code. The free preview access with "generous rate limits" indicates an experimental phase rather than a commercial service with security SLAs.

The artifact-based transparency system generates verifiable deliverables (task lists, screenshots, recordings) that could support compliance auditing — but without formal security attestation, regulated industries can't adopt the platform regardless of technical capabilities.

For our procurement team: Antigravity couldn't proceed to the evaluation stage because the preview software, lacking enterprise security certifications, automatically fails our vendor assessment process.

Neither tool met our security requirements for production deployment in regulated environments.

Pricing (production service vs. free preview)

Amazon Q charges $19/month until usage exceeds undisclosed limits. Antigravity charges nothing because it's experimental. Neither model provides the transparent pricing enterprise procurement requires.

Amazon Q Developer

Amazon Q Developer costs $19/user/month, which sounds straightforward until you understand the limitations.

Included in $19/month:

• Unlimited chat, code completion, debugging, and testing interactions
• 30 agent accelerations per month (autonomous feature development)
• 4,000 lines of code per month for code transformation
• 200,000-token context window (CLI)
• IDE integration (VS Code, JetBrains, Visual Studio, Eclipse)
• Security scanning and 500 vulnerability scans per month
• 4 specialized agents (documentation, review, tests, features)

The usage constraint: Pro tier provides unlimited chat and code completion, but autonomous agent accelerations are capped at 30 per month. During our pilot with 10 developers, the team used accelerations for complex multi-file refactoring tasks and feature implementation.

For heavy users of autonomous agent features, beyond 30 accelerations per month, AWS doesn't offer additional capacity options. Teams requiring higher agent acceleration limits need to contact AWS support or account representatives to discuss usage patterns.

Additional limits apply to specific features: 500 security scans per month, 4,000 lines of code for transformations (with overages at $0.003 per line), and various quotas for specialized agent operations.

My testing experience: The $19/month plan provides unlimited daily coding interactions, but the 30 agent accelerations per user felt restrictive for developers relying heavily on autonomous feature development.

For teams that use agent accelerations extensively, the quota resets monthly, with no published option for additional capacity.

Google Antigravity

Antigravity offers free preview access — no pricing structure is disclosed because it's experimental software.

Available at no cost:

• Editor View and Manager View access
• Autonomous agent execution
• Multi-agent orchestration
• Gemini 3 Pro, Flash, and Deep Think models
• Claude Sonnet 4.5 and GPT-OSS support
• "Generous rate limits" (specific limits undisclosed)

The preview caveat: Free access indicates experimentation phase, not commercial readiness. Google hasn't announced:

• Timeline for general availability
• What pricing will be when/if it transitions from preview
• Whether current preview users will face pricing changes
• How enterprise licensing will work

Deployment costs: While Antigravity itself is free, deployment requires setting up Google Cloud project infrastructure, enabling APIs, and configuring IAM. For organizations without existing Google Cloud infrastructure, initial setup requires 2-5 days of engineering time to establish cloud foundations.

My testing experience: The free preview removed procurement friction for experimentation, but created budget uncertainty for potential production adoption. Teams can't forecast what enterprise deployment would cost when (if) Antigravity reaches general availability with disclosed pricing.

Amazon Q Developer vs. Google Antigravity? How to choose

Time spent testing both platforms on our 50-repository enterprise codebase revealed that what matters more than which tool "wins" is the same architectural gap in the AI coding tools market.

Here's what I learned:

Amazon Q Developer solved specific AWS infrastructure problems. The CloudFormation and CDK assistance genuinely helped our infrastructure team. But the workspace-local indexing meant it couldn't answer questions spanning multiple repositories. The core requirement for understanding our enterprise codebase.

Google Antigravity explored fascinating autonomous development workflows. Watching agents build features for 47 minutes was impressive. But the preview crashed, generation stalled, and the inability to analyze existing codebases meant it couldn't help with our actual work — understanding and evolving legacy systems spanning 50 repositories.

Neither tool addressed the real enterprise requirement: cross-repository code intelligence.

Choose neither tool if you're:

• Managing 50-500 repositories requiring cross-repository intelligence
• Maintaining distributed systems where understanding service dependencies prevents incidents
• In regulated industries requiring formal security certifications before procurement approval
• Onboarding engineers who need to understand architectural patterns across multiple repositories
• Operating in European regions requiring GDPR-compliant data processing

My honest take: Amazon Q helped with specific AWS tasks but couldn't scale to our multi-repository needs. Antigravity showed interesting possibilities for autonomous development, but couldn't analyze our existing systems.

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What this means for your team:

• 70.6% SWE-bench success rate: Versus 54% competitor average, demonstrating superior ability to handle complex software engineering tasks across entire repositories. Changes that work the first time, not the third iteration after debugging.
• Context that scales to enterprise codebases: Process 400,000+ files through semantic analysis without melting your machine. Initial indexing takes 27 minutes; incremental updates happen in under 20 seconds.
• ISO 42001 certified security: First AI coding assistant to achieve this certification. SOC 2 Type II compliant, customer-managed encryption keys available. Your security team can actually approve this.
• Architectural violation detection: The PR review agent can catch security issues and cross-layer dependencies that would have caused production incidents. A 59% F-score, compared with the industry average of 25%, means fewer debugging sessions.
• Remote Agent for autonomous workflows: Describe the refactoring task, let the agent handle multi-file coordination while you focus on architecture decisions. Not just autocomplete; actual autonomous development with architectural awareness.

See the difference context depth makes when your codebase grows beyond what individual developers can hold in their heads.

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