Cursor vs Google Antigravity: Which Fits Your Enterprise Team's Reality?

After examining how each tool actually works in practice, the meaningful question isn't which tool is better: it's whether they solve the same problem at all. The instinct to compare Cursor and Google Antigravity head-to-head makes sense: both position themselves as AI-powered development environments, both target professional developers, and both promise to accelerate how teams write and maintain code.

The instinct to compare Cursor and Google Antigravity head-to-head makes sense: both position themselves as AI-powered development environments, both target professional developers, and both promise to accelerate how teams write and maintain code.

This distinction matters because the evaluation criteria shift depending on which problem you're actually trying to solve. Teams looking for immediate productivity gains within established processes need different things than teams exploring what autonomous development agents might unlock.

According to Gartner, 75% of enterprise software engineers are predicted to use AI code assistants by 2028, up from less than 10% in early 2023. The tooling decisions made today will shape development workflows for years.

Augment Code achieves 70.6% on SWE-bench while processing 400,000+ files with full architectural context, avoiding the RAM consumption issues that plague Cursor at scale. Evaluate for your enterprise →

Cursor and Google Antigravity: Core Capabilities

Before diving into the detailed comparison, understanding the fundamental architecture of each tool clarifies why they serve different organizational needs. Cursor and Google Antigravity represent two distinct philosophies in AI-assisted development: the AI-enhanced editor approach versus the agent-first, autonomous platform approach.

Cursor is an AI-powered code editor built as a Visual Studio Code fork (official documentation) with native integration of advanced language models (Claude 3.5 Sonnet, GPT-4, and Gemini 2.0 Pro/Flash) designed for AI-first development workflows. It provides semantic codebase indexing using vector-based search across your entire indexed codebase, multi-file editing through its proprietary Composer agentic model (2.0 announcement), SOC 2 certified with zero data retention options, and advanced debugging capabilities, including runtime instrumentation for cross-stack troubleshooting.

Recent releases added Browser Mode for real-time CSS editing and Plan Mode for task breakdown visualization (2.2 Changelog), while maintaining full compatibility with VS Code extensions critical for existing enterprise tooling investments.

Google Antigravity represents something different: an agent-first platform where AI autonomously plans, executes, and verifies multi-step development tasks across editor, terminal, and browser (Google Antigravity docs). It's less about assisting your workflow and more about delegating work to autonomous agents. The platform features a 1-million-token context window through its Gemini 2.0 foundation and native integration with Google Cloud data services via Model Context Protocol (MCP) servers.

Cursor vs Google Antigravity: Why This Comparison Matters in 2026

The timing of this comparison creates an unusual evaluation situation in which one tool has years of production use while the other remains largely unvalidated. Understanding this maturity gap is essential for making informed deployment decisions.

Antigravity launched in November 2025 (Google Developers Blog), roughly eight weeks ago as of this writing. The cursor has accumulated 2+ years of production use, enterprise deployments, and documented limitations. Google Antigravity has architectural promise but minimal real-world validation.

For teams making decisions now, the choice between these tools requires acknowledging fundamental limitations in both platforms. Cursor demonstrates enterprise-grade documentation, SOC 2 certification, and validated use cases for specific workflows, such as legacy code test generation (85% time reduction), but exhibits documented performance degradation at scale, with memory exhaustion (100GB+ RAM consumption) and indexing delays that hinder rapid codebase understanding. Google

Antigravity, launched only 8 weeks ago with zero enterprise case studies and no publicly available security documentation, lacks sufficient validation for enterprise deployment. Critically, Google's own developers are not permitted to use it internally, and the company acknowledges certain security limitations in its terms of use. Rather than a simple maturity asymmetry, both tools present distinct risk profiles:

Cursor is production-viable for targeted use cases but unreliable at true enterprise scale; Antigravity remains fundamentally unvalidated. Organizations evaluating either platform should conduct representative pilots with their actual 50-500 repository environment, implement mandatory human code review and automated security scanning, or consider tools explicitly positioned for large enterprise codebases like Windsurf.

Cursor vs Google Antigravity: Feature Comparison at a Glance

This comparison table provides orientation for the key architectural and capability differences between Cursor and Google Antigravity. The sections that follow explain why these differences matter in practice for enterprise teams.

Enterprise Readiness: Cursor vs Antigravity Maturity Gap

The most significant difference between these tools isn't architectural; it's evidentiary. Understanding the validation gap helps teams accurately assess deployment risk.

Cursor has one fully validated enterprise case study with quantified metrics: Salesforce Engineering documented an 85% reduction in time spent on legacy code coverage for legacy codebases with insufficient coverage while working toward a company-wide 80% code coverage mandate.

Key implementation details from Salesforce's deployment:

• Mandatory oversight: Every generated test was manually reviewed and validated
• AI-generated documentation: JavaDoc comments provided context for review
• Constrained scope: Focused specifically on test generation for legacy code coverage
• Measurable results: Productivity gain was repeatable within this specific context

A separate 20-developer Java team documented their structured adoption approach on the Cursor forum, describing the infrastructure they built:

• Code generation templates for common patterns
• MQ operation examples
• Comprehensive project knowledge bases
• Cursor Rules files per repository

Their questions about whether this level of documentation was necessary indicate the significant setup investment required for enterprise adoption.

Antigravity, by contrast, has no documented enterprise case studies as of January 2026. Despite launching in November 2025, the tool shows no verified team implementations or production deployments in available sources. This isn't necessarily a criticism of the tool's potential; it reflects the product's brief 8-week market presence, which has been insufficient time for enterprise evaluation and adoption cycles. However, it does mean the evidence available for evaluation is fundamentally limited.

More concerning: According to developer discussions on Reddit r/singularity, Google does not permit its own developers to use Antigravity for internal development work. If accurate, this internal policy would signal potential unresolved concerns about production reliability, security, or quality; however, Google has not officially confirmed this restriction.

Additionally, Google's own terms of use acknowledge that Antigravity has certain limitations, though specific technical details remain undisclosed. Until Google clarifies its internal use policy and provides comprehensive security documentation, enterprise teams should treat this as a material risk factor and engage the vendor directly for verification.

Security and Compliance: Cursor vs Antigravity Documentation

For teams in regulated industries or with strict security requirements, the contrast between these tools is stark. Cursor's verified SOC 2 certification and documented data handling policies provide clarity, while Google Antigravity's lack of publicly available compliance documentation requires direct vendor engagement before evaluation can proceed.

Security Posture Comparison

Cursor provides:

• SOC 2 Type II certification with reports available at trust.cursor.com
• GDPR compliance documentation
• Privacy mode enabling zero data retention with enforcement at the team level
• Contractual agreements with OpenAI, Anthropic, Google Cloud Vertex API, xAI, and other model providers preventing those providers from retaining customer inputs or outputs

The architecture uses a dual-infrastructure approach:

• Parallel service replicas for privacy mode and standard mode
• Request routing based on privacy headers
• File paths obfuscated with client-stored encryption keys
• No plaintext stored for privacy mode users

Additional encryption measures:

• Enterprise-grade encryption for all data in transit
• Encryption across all infrastructure components
• Customer Managed Encryption Keys (CMEK) for enterprise customers
• Cloud Agent data encrypted using customer-provided keys

Antigravity's security posture is harder to evaluate because public documentation is limited. Google acknowledges in its terms of use that Antigravity has certain limitations, but does not specify what they are. A security researcher discovered a vulnerability within 24 hours of launch; not unusual for new software, but indicative of the maturity gap and the fact that Google does not permit its own developers to use the tool internally.

For enterprise procurement processes that require compliance certifications, security whitepapers, and detailed data-handling policies, Cursor offers SOC 2 Type II certification reports, GDPR-related compliance information, and security documentation upon request via its Trust Center, along with publicly documented zero-data-retention agreements with model providers.

Google Antigravity, by contrast, lacks publicly available enterprise security documentation, compliance certifications, or detailed privacy policies, requiring direct engagement with Google enterprise sales for access to security documentation, data handling specifications, and compliance certifications necessary for due diligence.

Augment Code provides SOC 2 Type II and ISO/IEC 42001 certifications, with native integrations across VS Code, JetBrains, and the CLI. Compare enterprise security options →

Performance at Scale: Cursor vs Antigravity for Large Codebases

Neither tool publishes comparative benchmarks for 50-500 repository environments with measured indexing speeds, query latencies, or context utilization metrics. However, Cursor's longer market presence reveals specific documented performance failures at scale that teams should understand before deployment.

Engineers report excessive RAM consumption (100 GB+ in extended sessions), causing system instability in large monorepos, and the platform degrades performance for files exceeding 500 lines. These limitations provide partial visibility into behavior at enterprise scale, whereas Google Antigravity lacks any publicly documented performance data at any scale.

Documented Cursor performance constraints:

• Memory consumption: 100GB+ RAM in extended sessions with large monorepos causes system instability
• File size limits: Performance degrades on files exceeding 500 lines
• Indexing latency: 10-minute refresh cycle via semantic vector-based comparison may create latency issues in fast-moving codebases with frequent commits across distributed teams

The indexing mechanism checks for file changes by comparing Merkle trees against the Turbopuffer database. Manual optimization through .cursorignore files (large codebases docs) enables the exclusion of independent modules and prevents unnecessary indexing of entire monorepos. .cursorignore is a configuration file that lets teams control which files Cursor indexes and exposes to AI features for security and general performance reasons, but there is no documented evidence that it provides specific, proven scaling improvements for large monorepos.

Antigravity's Gemini 2.0 foundation provides a 1-million-token context window, according to Google's official documentation, roughly 750,000 words or 3,000-4,000 pages of code. Theoretically, this offers significant capacity for understanding large codebases. But no enterprise has validated this capability at scale. The rate limit structure (Reddit discussion notes 5-hour refresh versus Cursor's monthly reset) could favor high-velocity teams, but that advantage remains speculative without production performance data.

Given Cursor's documented performance limitations at enterprise scale (100GB+ RAM consumption, system freezing on files over 500 lines) and Antigravity's lack of enterprise validation after only 8 weeks in market, organizations managing 50-500 repository environments may find neither tool provides sufficient large-scale support. Teams with true large-scale requirements should either conduct extensive pilot testing with representative codebases or evaluate tools explicitly designed for enterprise infrastructure.

Integration Approaches: Cursor vs Antigravity Ecosystem Strategies

Cursor and Google Antigravity take fundamentally different approaches to integration. Understanding these differences helps teams assess compatibility with existing toolchains and workflows.

Cursor is a fork of VS Code and supports many VS Code extensions, but compatibility is not complete, and some tools that VS Code users depend on are unavailable or require workarounds. GitLab integration can work via the standard GitLab Workflow extension, but both GitHub and GitLab are primarily supported through Cursor's native integrations rather than exclusively through standard extensions. The trade-off is that Cursor is a standalone editor; organizations that have standardized on JetBrains IDEs cannot use It and would need to complete an IDE migration to adopt it.

Cursor provides programmatic APIs for custom integration:

• Admin API for team management
• Analytics API for usage data
• AI Code Tracking API for per-commit metrics
• Cloud Agents API for agent lifecycle management

These APIs enable custom integration development, but Cursor only states that all APIs are rate-limited per team, with limits that reset every minute; it does not document specific numeric requests-per-minute values for each API. No native CI/CD integrations exist; connecting Cursor to Jenkins, GitHub Actions, or GitLab CI requires custom development using the documented APIs.

Antigravity takes a different approach through Model Context Protocol (MCP) servers, which enable direct integration with Google Cloud data services: BigQuery, AlloyDB, Cloud Spanner, Cloud SQL, and Looker (Google Cloud Blog). For organizations deeply integrated with Google Cloud, this MCP-based integration enables AI agents to interact with databases, generate analytics code, and access enterprise data platforms within the development workflow. This represents a unique architectural advantage for Google Cloud-native organizations compared to competing platforms.

Antigravity's integration with traditional IDE workflows and source control platforms is largely undocumented on official channels, creating uncertainty about deployment compatibility. However, the platform provides documented integrations with Google Cloud data services through MCP support and integrates with Coder's cloud development environment.

Critical gaps remain:

• VSCode/JetBrains IDE compatibility unclear
• GitHub/GitLab source control integration specifics are undocumented
• Native CI/CD pipeline support is absent
• Whether Antigravity operates as a standalone editor, extension, or platform layer orchestrating multiple environments isn't clearly specified

For VS Code-standardized organizations, Cursor provides immediate compatibility. For Google Cloud-native organizations, Antigravity offers potential data service integration through MCP connections, though the tool launched only 8 weeks ago (November 2025) and lacks sufficient enterprise documentation on data handling, security compliance, and deployment options to support procurement decisions. For JetBrains-dependent organizations, neither tool integrates; alternatives should be evaluated, though comparable tools supporting JetBrains IDEs are not comprehensively covered in current research.

Pricing Comparison: Cursor vs Antigravity Cost Transparency

Understanding the total cost of ownership requires transparent pricing models. This section examines what each vendor discloses publicly and what requires sales engagement.

Pricing Breakdown by Team Size (Cursor)

Google Antigravity: Individual tier ($0), Developer tier (via Google One), Team/Enterprise pricing not publicly available

Cursor's pricing is fully public (Teams Pricing docs): $40 per user per month for teams, with $20 in included API usage credits per seat. Additional usage incurs a $0.25 per million tokens "Cursor Token Fee." Configurable spending limits and per-user caps help control costs.

For a 15-developer team using Cursor's Teams plan at $40 per user per month, the base annual cost is $7,200. For 50 developers, $24,000. Each seat includes $20 in API usage credits per month. Beyond included usage, Cursor charges variable overage costs based on the underlying provider's request or token rates (e.g., OpenAI, Anthropic), which vary based on team usage patterns. Teams can configure monthly team-wide spend limits to control costs. For larger deployments, Cursor's Enterprise plan offers custom pricing with pooled usage across all team members rather than per-user tracking, providing different cost optimization strategies for scaling organizations.

Google Antigravity's official pricing page currently shows only two plans (official pricing): an Individual plan at $0/month and a Developer plan available via Google One; it does not list Team or Organization tiers, nor does it describe Team pricing as connected to Google Workspace subscriptions or Organization pricing as tied to Google Cloud contracts. No public per-seat pricing is published; any quote requires direct sales engagement.

For organizations with existing Google enterprise agreements, no bundling advantages for Antigravity are currently documented or confirmed. However, both Antigravity and Cursor provide publicly available pricing details on their websites, allowing teams to understand total costs without first engaging sales.

Enterprise Setup: Cursor vs Antigravity Implementation Requirements

Deploying AI coding assistants at enterprise scale requires understanding the implementation overhead and infrastructure investments each tool demands.

Implementation Timeline Comparison

According to Cursor's Deployment Patterns docs, organizations deploying Cursor at enterprise scale configure policies through Mobile Device Management (MDM) systems, including Group Policy on Windows, configuration profiles on macOS, and JSON policy files on Linux. The documentation specifies that "policy values override any corresponding Cursor settings from all levels," making it critical for administrators to understand policy precedence rules and communicate expected behavior to users to prevent confusion.

When new models become available, Cursor doesn't automatically enable them for all enterprise teams; administrators must explicitly approve access through model and integration management controls. This prevents unexpected usage and costs but requires active team-level configuration and oversight.

Cursor provides enterprise controls, such as an UpdateMode policy and MDM-managed settings, but its public documentation does not explicitly recommend that organizations with non-admin users manage updates through existing software deployment pipelines rather than automatic updates, nor does it document how to disable automatic updates via MDM across macOS, Windows, and Linux. GitHub and GitLab integrations require team-level configuration when service accounts are needed for programmatic API consumption and CI/CD pipeline automation, as Cursor supports both platforms through standard VSCode extensions, including the native GitLab Workflow extension.

The learning curve advantage is real: teams already using VS Code can leverage existing editor knowledge. But fully utilizing Cursor's capabilities—Composer for multi-file edits, Agent Mode for autonomous execution, context controls, and project rules- requires substantial organizational investment beyond training.

Enterprise teams must establish a structured adoption infrastructure:

• Code generation templates for common patterns
• Comprehensive project knowledge bases
• .cursorrules files per repository

A 20-developer enterprise Java team implementing Cursor found the setup sufficiently complex that they actively sought community guidance on structuring large project knowledge bases, indicating significant implementation overhead before realizing productivity gains

Google Antigravity has publicly accessible implementation documentation. While third-party educational content exists, Google has now published official Antigravity documentation, including implementation-plan guides and integration procedures, so enterprise teams can plan reliable implementations without necessarily engaging directly with Google.

Available information does not identify any research concluding that Antigravity lacks official guides, enterprise configuration documentation, or verified setup procedures that make independent implementation planning impossible.

Cursor vs Antigravity: Which Tool Fits Your Team?

Based on the maturity gap, security posture, and performance characteristics examined throughout this comparison, this section provides clear decision criteria for teams evaluating these tools.

Choose Cursor if:

• You need an AI coding assistant deployable within the next 3 months
• Your organization standardizes on VS Code or can migrate to it
• Your security and compliance processes require SOC 2 Type II certification and documented data handling policies
• You have specific, constrained use cases where productivity gains are validated (particularly test generation for legacy code coverage with mandatory human oversight)
• You want transparent pricing for budget planning ($40/user/month with documented usage credits and overage fees)
• You're willing to invest in setup infrastructure: MDM policies, templates, knowledge bases, rules files

Consider Antigravity later if:

• Your organization can wait 6-12 months for product maturity
• You're deeply integrated with Google Cloud data services and want native connectivity
• You're exploring what agent-first development patterns might unlock for your team
• You have an existing Google Workspace or Cloud enterprise agreement that might provide bundling advantages
• You're willing to engage directly with Google sales to understand pricing, security posture, and deployment options

Reconsider both tools if:

• You require air-gapped or on-premise deployment: neither offers this
• Your organization standardizes on JetBrains IDEs: neither integrates
• You operate at true enterprise scale and need validated performance: consider running your own extensive pilots with your actual codebase, as there are currently no publicly documented case studies or benchmarks validating Windsurf specifically at the 50–500‑repository scale.

Enterprise Codebases Need Proven Tools, Not Promising Demos

Here's the uncomfortable reality this comparison reveals: Cursor has two years of production usage but chokes on the codebases that need AI assistance most, the sprawling monorepos, the legacy systems, the 500-file service meshes. Google Antigravity has a theoretical scale (1M token context window sounds impressive) but zero enterprise validation and a security vulnerability discovered within 24 hours of launch. Google won't even let its own engineers use it internally.

Your codebase can't wait for Antigravity to mature. And it can't afford Cursor's 100GB+ RAM consumption to crash your development environment mid-refactor.

Augment Code was purpose-built for the codebases that break other tools. The Context Engine consistently retrieves the correct dependency chains, call sites, type definitions, tests, and related modules, the raw material needed for deep reasoning. No other system demonstrates comparable accuracy or completeness in context assembly at the scale of 400,000+ files.

The difference shows in production: 70.6% SWE-bench accuracy versus the industry average of 54%. 59% F-score on code review, catching security issues and cross-layer dependencies that would cause production incidents. First-pass suggestions that work because they understand your architecture, not just your syntax. Test it on your actual codebase →

✓ Scales to 400,000+ files without memory exhaustion

✓ Two years of enterprise deployments, battle-tested, not beta

✓ ISO/IEC 42001 and SOC 2 Type II certified

✓ Native VS Code, JetBrains, and CLI integration

✓ Remote Agent executes multi-file tasks in the background, no local resource strain

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