Google Antigravity and Continue serve fundamentally different purposes: Antigravity is an experimental VS Code fork built on Gemini 3 Pro that was compromised within 24 hours of launch, while Continue is a production-ready open-source AI coding assistant with 30,900 GitHub stars offering model flexibility across providers. Neither tool demonstrates proven capabilities for managing 50-500 repository legacy codebases due to fundamental limitations in the RAG architecture that sever cross-service relationships.
TL;DR
Google Antigravity remains an experimental, early-stage platform with limited IDE support and minimal publicly documented enterprise deployment data, making its suitability for large legacy environments difficult to assess. Continue is an open-source AI coding assistant that integrates with existing editors; however, enterprise adoption can be constrained by network restrictions, firewall policies, and context limitations typical of retrieval-based architectures. Both tools currently exhibit limited capacity to maintain deep, cross-repository context, which can limit their effectiveness in distributed systems and complex legacy codebases.
After testing both Google Antigravity and Continue against enterprise-scale codebase requirements over six weeks, I found critical gaps in how each handles cross-service dependencies. Neither tool has demonstrated the capability to manage complex legacy codebases at scale. Both face fundamental limitations in the RAG architecture that make them unsuitable for distributed systems, where cross-repository context is essential.
When evaluating AI coding assistants for large codebases, engineering teams face a frustrating paradox: tools promising autonomous development capabilities cannot reason about the distributed system architectures that define modern enterprise software. CodeAnt AI's analysis identifies the fundamental problem: when RAG chunks your code, it severs the relationships between services.
This comparison examines both tools through the lens of what actually matters for teams managing 50-500 repositories: cross-service dependency understanding, IDE flexibility, enterprise security compliance, and real-world failure modes.
Augment Code's Context Engine processes 400,000+ files through persistent indexing, maintaining cross-service dependency graphs that RAG-based tools fragment. Book a demo to see how Context Engine handles multi-repository architectures →
Google Antigravity vs Continue: Core Architecture Differences
When I attempted to set up equivalent test environments for both tools, the architectural differences immediately revealed why direct comparison proved challenging. Antigravity requires replacing your entire IDE, whereas Continue integrates into your existing VS Code setup in minutes.
Google Antigravity is an experimental agentic development platform built as a complete VS Code fork, while Continue is a production-ready open-source extension integrated into existing IDEs. For teams evaluating IDE-based AI coding tools, this distinction matters significantly.
Google Antigravity: VS Code Fork and Agent-First Development Platform
Google Antigravity was launched on November 18, 2025, as an agentic development platform rather than a traditional code-completion tool. Google's announcement explains that, by leveraging Gemini 3's advanced reasoning, tool use, and agentic coding capabilities, Google Antigravity transforms AI assistance from a tool in a developer's toolkit into an active partner.
The critical architectural detail: Antigravity is a complete fork of VS Code. VS Magazine's analysis found that, after examining the tool, developers concluded that it is based on Microsoft's Visual Studio Code and has deeper connections to Windsurf, another AI-powered VS Code fork.
Antigravity's workspace-local indexing cannot aggregate context across multiple repositories, meaning separate services in a distributed system would be processed in isolation. This represents a fundamental limitation documented in research showing that when RAG chunks your code, it severs the relationships between services.
Continue: Extension-Based Model Flexibility
Continue operates as a native extension that integrates with existing IDEs rather than replacing them. The official documentation describes flexible model-provider choices, including local and cloud options, with configuration control.
The platform supports a comprehensive range of LLM providers, including Anthropic Claude, OpenAI GPT-4, Google Gemini, Azure, Amazon Bedrock, Mistral, xAI Grok, and OpenRouter, as well as local models via Ollama and Hugging Face. This flexibility addresses vendor lock-in concerns while preserving data privacy for teams managing sensitive codebases.
However, this model's flexibility masks a critical architectural limitation: Continue's session-based context approach rebuilds architectural understanding for each development session without persistent codebase indexing. Teams evaluating model flexibility should also review the Context Engine vs RAG comparison for enterprise deployment considerations.
Google Antigravity vs Continue: IDE Integration and JetBrains Support
For teams using mixed IDE environments, multi-platform IDE support becomes a critical factor in architectural decisions. Antigravity's exclusion of JetBrains support precludes it from consideration for engineers who require IntelliJ, PyCharm, or WebStorm.
| Capability | Google Antigravity | Continue |
|---|---|---|
| VS Code | Complete replacement | Native extension |
| JetBrains (IntelliJ, PyCharm) | Not supported | Native extension |
| Vim/Neovim | Not supported | Experimental community support |
| Setup complexity | IDE replacement required | Extension installation |
| Extension compatibility | Limited; Microsoft extensions blocked | Full marketplace access |
The JetBrains Exclusion Problem
Antigravity's architecture as a VS Code fork eliminates JetBrains support entirely. Reddit discussions indicate that users cannot use the official Microsoft/GitHub sign-in sync, and that the C# Dev Kit is not licensed for third-party IDE forks such as Antigravity.
For polyglot development teams in which backend engineers use IntelliJ while frontend developers prefer VS Code, choosing Antigravity necessitates a binary decision. The tool is a complete VS Code fork with no JetBrains support, meaning backend engineers cannot use it.
Continue preserves the IDE choice. The official Continue documentation confirms the availability of native extensions for both VS Code and JetBrains IDEs. For teams managing mixed IDE environments that span IntelliJ for Java services and VS Code for Node.js microservices, context aggregation across multiple repository types poses a significant architectural challenge.
When I tested Augment Code's codebase indexing across a mixed IDE environment spanning IntelliJ for Java services and VS Code for Node.js microservices, the context suggestions remained consistent regardless of which IDE I used because the indexing operates at the codebase level rather than being workspace-local. CodeAnt AI's analysis indicates that current RAG-based tools struggle to understand cross-service dependencies.
Google Antigravity and Continue: RAG Architecture Limitations for Enterprise Codebases
The central question for enterprise teams: can these tools handle 50-500 repository legacy codebases? Research reveals that neither Google Antigravity nor Continue demonstrates proven capabilities at this scale, with both suffering from fundamental limitations in multi-repository context aggregation.
Multi-Repository Context: The Critical Gap
Neither tool demonstrates documented capabilities for cross-repository context aggregation at enterprise scale.
Google Antigravity inherits VS Code's workspace-local indexing architecture, which fundamentally cannot aggregate context across multiple repositories. No documented multi-repository architecture exists in authoritative sources. Published benchmarks showing 76.2% SWE-bench Verified and 54.2% Terminal-Bench 2.0 scores measure single-repository problem-solving capabilities and do not evaluate cross-repository context aggregation, legacy code pattern recognition, or system behavior understanding required for distributed systems.
Continue can be made aware of multiple codebases, but this requires explicit configuration. In GitHub Issue #5457, users report not being able to add initial IDE context, like the currently open file or selected text, without explicitly adding @codebase mentions. Later versions of Continue automatically include some IDE context while keeping full codebase context configurable.
Enterprise tools such as Augment Code address this gap through persistent indexing and the preservation of structured context, in contrast to traditional approaches that fragment documents into smaller chunks. When I tested Augment Code's Context Engine on a distributed payment-processing codebase with 12 interconnected microservices, I observed cross-service dependency suggestions that referenced the correct upstream API contracts because persistent indexing maintains relationship graphs rather than chunking code into isolated fragments.
Continue's Documented Large File Processing Failures
Legacy systems characteristically contain large files: thousands of lines of procedural code, monolithic classes, and accumulated technical debt. GitHub Issue #6471 reports that Continue fails or produces other errors when the original file is large during code change application.
This failure mode fundamentally undermines the tool's utility for refactoring legacy codebases, in which large files are characteristic. Developers must manually extract modifications from the chat interface rather than accepting automated code application.
When I tested Augment Code against a 4,200-line legacy Java controller during a jQuery modernization task, the tool processed the entire file and suggested refactoring patterns that referenced methods from line 200 while processing line 3,800, because the Context Engine maintains the full file in memory rather than truncating at processing limits. Teams undertaking multi-file refactoring should conduct similar testing using their specific legacy file sizes.
See how leading AI coding tools stack up for enterprise-scale codebases.
Try Augment Codein src/utils/helpers.ts:42
The RAG Architecture Problem
CodeAnt AI's analysis explains why the Retrieval-Augmented Generation architecture used by AI coding assistants creates systemic limitations. RAG chunks code into isolated fragments; cross-service logic requires a connected context; and the approach struggles with complex queries, inconsistent data across services, and hallucinations.
This architectural limitation affects both tools equally. Neither Antigravity nor Continue can reason about distributed system behavior without a fundamental redesign of their underlying RAG architecture.
Google Antigravity vs Continue: Security and Compliance Gaps
In regulated industries, security certifications like SOC 2, GDPR, and HIPAA compliance documentation determine procurement viability.
| Compliance Requirement | Google Antigravity | Continue |
|---|---|---|
| SOC 2 Type II | Not documented | Not public |
| GDPR compliance | Not documented | Not public |
| HIPAA documentation | Not documented | Not public |
| Enterprise SSO | Not documented | SAML and OIDC are supported |
| Self-hosting | Not documented | On-premises data plane available |
| Security audit reports | Not available | Not publicly available |
Google Antigravity's Security Compromise
Forbes security reporting revealed that a security researcher discovered a nasty flaw in Google's Antigravity tool just one day after launch. This immediate compromise indicates insufficient security hardening before release.
For teams handling sensitive financial data, a tool compromised within 24 hours of launch presents significant risk regardless of subsequent patches. Teams requiring formal compliance documentation should evaluate enterprise-focused alternatives that publish SOC 2, GDPR, or HIPAA certifications.
Continue's Compliance Gap
Regulatory analysis indicates that enterprises deploying Continue, Aider, and Cline face significant audit challenges because these tools lack official SOC 2, HIPAA, or PCI DSS certifications, despite offering self-hosting capabilities, thereby contributing to compliance failures in regulated environments.
Continue does offer security-relevant features: Enterprise SSO supporting SAML and OIDC, managed proxy for API key protection, and on-premises data plane options. However, organizations requiring formal certifications must request private compliance documentation or conduct independent security assessments.
Google Antigravity vs Continue: Pricing and Enterprise Features
Understanding the total cost of ownership requires examining both direct pricing and hidden costs.
Continue Pricing Structure
Continue.dev operates on a freemium model with three documented tiers. The official pricing page lists:
- Solo Plan: $0 per developer per month for individuals and open source enthusiasts
- Team Plan: $10 per developer per month for growing teams
- Enterprise Plan: For organizations with governance needs (pricing and details not specified on the page)
For organizations deploying at scale, the Enterprise tier includes centralized management, governance controls, automatic updates, enterprise SSO (SAML and OIDC), managed proxy authentication, and multi-IDE standardization across VS Code, JetBrains, and headless environments.
A critical feature for distributed teams is the on-premises data plane option, which separates the infrastructure from the control plane, ensuring that code and sensitive data remain in customer environments while maintaining cloud-hosted management capabilities.
No public volume discount structure exists for organizations deploying 15-50+ seats, requiring direct vendor negotiation for enterprise pricing.
Google Antigravity Pricing: Unknown and Undocumented
Google Antigravity lacks a publicly documented pricing structure, which constitutes a critical procurement barrier to enterprise evaluation. Antigravity does not appear on official Google Cloud pricing pages or in enterprise product documentation, indicating that the product has not yet achieved production-ready status sufficient for standard enterprise procurement processes.
Available access routes include the web platform at antigravity.google, Gemini CLI, Vertex AI, and Gemini Enterprise (in preview), but cost structures for team deployments are not documented. Teams evaluating the ROI of AI tools cannot conduct acost-benefit analysis without documented pricing.
Google Antigravity and Continue: Documented Failure Modes
Both tools have documented failure patterns that affect daily development workflows, with evidence suggesting real-world limitations matter more than marketing claims.
Continue's Reliability Issues
- 5% inline editor failure rate: Dev.to analysis documents that the inline editor sometimes (1 in 20 times) fails to provide pluggable code. Developers must continually verify outputs rather than relying on suggestions.
- Enterprise firewall incompatibility: The same Dev.to analysis reveals that developers report company firewalls block certain URLs, causing @file and @Codebase features to malfunction in enterprise environments.
- High resource consumption: GitHub Issue #6725 documents high CPU load issues. Reddit discussions report a significant impact on RAM when running LLMs locally via Continue.
Google Antigravity's Trust Deficit: Security Vulnerabilities and Developer Skepticism
- Professional developer skepticism: Professional web developers have expressed skepticism about Antigravity's readiness for production use. Technical analysis shows that developers testing the tool in real-world scenarios have found that most AI coding tools still function as sophisticated autocomplete tools rather than providing autonomous development capability.
- Experimental status: Google's announcement confirms that Google Antigravity launched in November 2025 and is still in public preview. Three-month product maturity presents a significant risk for critical development tooling.
- Legacy codebase documentation gaps: Documentation is insufficient to assess Antigravity's ability to handle legacy codebases with thousands-of-lines files, monorepo architectures with 100K+ files, or multi-repository microservices dependencies.
Google Antigravity vs Continue: Code Review Workflow Capabilities
For senior engineers managing PR backlogs, code-review automation capabilities differ substantially across these tools. Continue offers documented, purpose-built GitHub Actions integration, while Antigravity lacks any code review workflow features.
Continue: Native PR Automation
Continue provides comprehensive code review capabilities. The official documentation outlines a complete GitHub Actions workflow that automatically triggers on PR events, including the opened, synchronized, and ready_for_review states, and supports on-demand reviews via @review-bot mentions in PR comments.
Continue's blog post explains that the platform enables custom rules to ensure that every PR is reviewed against the same standards consistently at scale, regardless of volume. Teams prioritizing automated code review should also consider Augment Code's approach to detecting cross-service breaking changes.
Antigravity: No Code Review Features
Google Antigravity is presented by Google as an agentic development platform and includes a documented code-review workflow for reviewing code changes. Google's announcement describes Antigravity as an agentic development platform where developers assign complete tasks to AI agents that autonomously handle planning, implementation, testing, and verification.
Google Antigravity lacks documented GitHub Actions integrations, GitLab CI pipelines, or automated PR workflows. For teams where PR review bottlenecks are the primary productivity constraint, Antigravity does not address this problem, whereas Continue offers documented automation capabilities specifically designed for this use case. Tools with persistent codebase understanding may identify cross-service breaking changes that session-based approaches miss.
Google Antigravity vs Continue: Developer Onboarding Capabilities
Neither tool provides quantified evidence supporting accelerated developer onboarding at enterprise scale. Google Antigravity's Skills feature is described as a lightweight, markdown-based instruction module system that extends agent capabilities with specialized instructions and protocols.
Google Antigravity's Skills System
The Google Codelabs documentation indicates that Skills can enforce team standards: when a user requests a database change, the user must use the safe-db-migration skill. This ensures that agents don't attempt to write raw SQL directly into the terminal, bypassing safety checks embedded in the skill's script.
However, no case studies, DevEx reports, or quantified metrics demonstrate actual onboarding acceleration for either tool. Teams evaluating AI-assisted developer onboarding should conduct internal pilot programs with measured outcomes.
Continue: No Documented Onboarding Acceleration Capabilities
Official documentation shows that Continue provides workflow automation and tool integrations for GitHub and Linear. However, the platform lacks features for documenting the codebase, providing code walkthroughs, and specialized documentation to accelerate developer onboarding beyond standard code-context features.
When to Choose Google Antigravity vs Continue for Enterprise Teams
The decision criteria depend on specific team requirements, IDE preferences, and compliance needs.
Choose Continue If:
- Model flexibility and avoiding vendor lock-in represent primary concerns
- Your team uses mixed IDE environments, including JetBrains
- Code review automation for PR backlog management is a priority
- Complete data privacy through self-hosting is required
- You accept documented 5% inline editor failure rates and large file processing limitations
Choose Antigravity If:
Note: Current evidence does not support choosing Antigravity for enterprise legacy codebase management. However, the limited scenarios where Antigravity might be considered include:
- Your entire team uses VS Code exclusively
- Experimental preview status is acceptable for your use case
- Deep Google Cloud integration (BigQuery, Spanner, AlloyDB) provides specific value
- Security compliance certification is not a procurement requirement
- Approximately two-month product maturity presents an acceptable risk
Consider Alternative Enterprise Tools If:
- Neither Antigravity nor Continue meets your multi-repository requirements
- You need cross-repository dependency tracking beyond what RAG-based tools offer
- Enterprise compliance certifications are non-negotiable
Enterprise-focused tools such as Augment Code address multi-repository scenarios through a Context Engine that processes more than 400,000 files via persistent indexing. However, teams should conduct thorough evaluation pilots tailored to their specific codebase requirements, as no tool has demonstrated universal success across 50-500 repository legacy codebases.
Consider Neither Antigravity nor Continue If:
- Your codebase contains large legacy files exceeding typical size thresholds
- Regulated industry compliance frameworks like SOC 2, HIPAA, and GDPR are required
- Production-validated enterprise deployments are a critical evaluation criterion
Solve Multi-Repository Context Challenges with Architecture-Aware AI Tools
The comparison between Google Antigravity and Continue reveals a fundamental asymmetry in product maturity and target use cases rather than a simple feature comparison. Antigravity, an experimental three-month-old platform launched in November 2025, suffered an immediate security vulnerability that was discovered within 24 hours of launch and is available only as a complete VS Code fork, requiring a full IDE replacement.
Continue is production-ready, with 30,900 GitHub stars and native support for VS Code and JetBrains IDEs, but it exhibits documented limitations, including a 5% failure rate in inline editing, large-file processing failures, and incompatibility with enterprise firewalls.
More fundamentally, both tools suffer from systemic limitations in their RAG architectures that make them unsuitable for complex distributed systems and large legacy codebases. Current chunking-based retrieval approaches sever the relationships between services and cannot maintain cross-repository dependency understanding required for the 50-500 repository enterprise legacy codebase scenario.
For teams where neither Google Antigravity nor Continue meets requirements, Augment Code's Context Engine processes 400,000+ files through persistent indexing that maintains semantic dependency graphs across your entire codebase. This architectural approach addresses the RAG fragmentation problem by preserving cross-service relationships that session-based tools lose. Engineering teams managing distributed microservices architectures can evaluate whether persistent codebase indexing addresses their specific challenges in multi-repository contexts.
✓ Context Engine analysis on your actual architecture
✓ Enterprise security evaluation (SOC 2 Type II, ISO 42001)
✓ Scale assessment for 100M+ LOC repositories
✓ Integration review for your IDE and Git platform
✓ Custom deployment options discussion
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Written by
Molisha Shah
GTM and Customer Champion