Sourcegraph Cody vs Cursor vs Augment Code for Enterprise Development

Enterprise codebases with 400,000+ files overwhelm token-based AI assistants because context windows cannot capture cross-service dependencies that span repositories. Testing across enterprise environments reveals Augment Code's semantic dependency analysis, Cursor's 1M Max Mode, and Sourcegraph Cody's verified ISO 27001 compliance serve distinct enterprise needs. Choose based on codebase scale, IDE standardization requirements, and security verification priorities.

Augment Code's Context Engine processes 400,000+ files through semantic dependency analysis, enabling complete architectural understanding that reduces AI hallucinations by 40%. See the Context Engine in action →

After testing all three tools across a large, distributed enterprise architecture, I found the differences weren't apparent from the documentation alone. They emerged during cross-service refactoring scenarios in which context quality directly determined whether suggested changes would break production systems or integrate safely with existing authentication flows.

My team assessed Augment Code, Cursor, and Sourcegraph Cody against our enterprise monorepo and multi-repository microservices environment. We focused on authentication service refactoring, payment processing updates, and database migration scenarios where a single missed dependency could cascade into production incidents.

What enterprise teams struggle with most during AI coding assistant evaluation isn't feature comparison. It's understanding how each tool's context architecture actually behaves in the face of real codebase complexity. Marketing materials promise a comprehensive understanding, but the practical question remains: when you modify a shared authentication token structure, does the AI recognize downstream services based on that exact signature?

This comparison examines three decisive dimensions: context architecture (how each tool builds understanding of large codebases), IDE integration requirements (because forcing 500 developers to change editors creates adoption friction), and security verification (since claims without attestation reports don't satisfy compliance requirements).

Key evaluation dimensions for enterprise AI coding assistants:

• Context, architecture, capacity and dependency understanding
• IDE compatibility with existing development environments
• Security certification verification and compliance status
• Deployment flexibility (SaaS, VPC, self-hosted)
• Pricing model transparency and predictability
• Multi-repository support across distributed systems

The 2025 Stack Overflow Developer Survey reveals 84% of developers now use AI coding tools, yet positive sentiment declined from 70% to 60% between 2023 and 2025. A McKinsey analysis found that high-performing organizations achieve 16-30% improvements in team productivity, but only when they "make wholesale changes to their operating model."

Sourcegraph Cody vs Cursor vs Augment Code at a Glance

When evaluating AI coding assistants for enterprise deployment, six decision factors matter most: context architecture, IDE compatibility, security certification, deployment flexibility, pricing transparency, and multi-repository support. This comparison table summarizes how each platform approaches these dimensions.

Key insight: Each tool optimizes for different enterprise constraints: Augment Code for semantic understanding, Cursor for AI-first workflows, and Sourcegraph Cody for integration with existing search infrastructure.

Sourcegraph Cody vs Cursor vs Augment Code: Context Architecture

Understanding how each tool approaches context architecture reveals why performance varies dramatically across enterprise codebases.

Augment Code

Augment Code's Deep Context Threading architecture builds "a map of what connects to what" across your codebase. The system distinguishes behavioral code from "ceremony" code and tracks frequently changed files to prioritize context, identifying complex interdependencies that isolated code analysis misses.

Testing in our enterprise monorepo: initial indexing required substantial processing time, but the investment proved worthwhile during the authentication service refactoring. The Context Engine identified multiple downstream services that rely on token validation signatures, including services in separate repositories that our team hadn't documented in the architectural diagrams.

When asked to modernize a jQuery payment form, the Context Engine proposed incremental changes that maintain the existing event structures, since it analyzed service dependencies that require specific signatures. The semantic dependency graph recognized that our legacy payment processor integration depended on specific jQuery event timing that naive modernization would break.

Subsequent updates are processed incrementally within minutes rather than requiring a full reindex, keeping the architectural understanding current throughout sprint cycles.

Cursor

Cursor implements surgical context targeting via the @ symbol (e.g., @code, @file, @folder). Official documentation confirms 200,000 tokens in Normal Mode, scaling to 1 million tokens with Max Mode for models like Gemini 1.5 Pro and Claude 3.7 Sonnet.

Using @folder to include our payment service directory, I targeted 40,000 lines with precision. The interface made context selection explicit and controllable. However, connections to external services required manual @file additions, meaning I needed prior knowledge of our dependency structure to include relevant context.

The limitation became apparent during the development of the multi-repository feature. Authentication flows spanning three services required manually identifying and including each dependency before posing my refactoring question.

Cursor's June 2025 transition to credit-based pricing introduced the Teams plan at $40/user/month with $20 included usage. The VS Code-only limitation remains a deployment barrier for heterogeneous environments.

Sourcegraph Cody

Sourcegraph Cody leverages a search-first RAG architecture with 1 million-token context via Gemini 1.5 Flash, automatically identifying relevant files using vector embeddings across repositories.

When asked about authentication implementation, Cody leveraged Sourcegraph's code search infrastructure to identify relevant context across multiple repositories without manually specifying code paths. For teams already using Sourcegraph, familiar query patterns are translated directly to the AI context specification.

Retrieval quality depended heavily on the existing Sourcegraph indexing configuration. Teams with well-tuned deployments see immediate benefits; teams new to Sourcegraph face a learning curve for optimal configuration.

JetBrains support is now generally available with analytics and guardrails for enterprise customers.

Struggling with cross-repository dependencies? Augment Code's Context Engine builds semantic dependency graphs across 400,000+ files. Explore Context Engine capabilities →

Sourcegraph Cody vs Cursor vs Augment Code: IDE Integration

For organizations with 500+ developers across multiple IDE preferences, integration architecture determines adoption velocity.

Augment Code supports VS Code, JetBrains (2024.3+), and Vim/Neovim with feature parity across environments. The JetBrains version requirement created initial friction during our evaluation, as several team members needed to upgrade their IDEs before participating in the pilot. During testing, Context Engine responses, code suggestions, and chat interactions behaved identically across editors. This parity enabled knowledge sharing about effective prompting strategies across our mixed-IDE team.

Cursor operates as an AI-first IDE built on VS Code. The purpose-built experience offers deep AI integration unavailable through plugins, but its incompatibility with the existing IDE standardization requires a complete migration. For our organization, this meant Cursor couldn't participate in our primary evaluation because mandating IDE changes for 400 developers wasn't feasible. Teams lose access to certain enterprise VS Code plugins, and organizations with fleet management tooling face reconfiguration requirements. For teams with extensive plugin ecosystems or JetBrains standardization, adoption barriers are substantial.

Sourcegraph Cody provides VS Code and JetBrains plugins that integrate without requiring IDE changes. Cross-IDE compatibility meant our diverse team could evaluate without workflow disruption. The Web IDE access added flexibility for code review scenarios, allowing developers to engage AI assistance during pull request reviews without launching full development environments. The JetBrains GA status particularly impacted our evaluation, as IntelliJ-focused backend developers could participate alongside VS Code frontend developers.

Sourcegraph Cody vs Cursor vs Augment Code: Security and Compliance

Enterprise security requirements reveal gaps between vendor claims and verifiable documentation.

Augment Code claims ISO/IEC 42001 and SOC 2 Type II certifications with flexible deployment options, including VPC and air-gapped configurations. When I requested certification documentation, the response included attestation letters but required an NDA before I could access the full SOC 2 Type II reports. Enterprise buyers should request attestation reports directly.

Cursor's Trust Center documents a "continuously monitored and 3rd-party audited security program" with 35+ controls across infrastructure, organizational, product, and internal security categories. Privacy Mode provides zero plaintext code storage with AWS U.S.-based infrastructure only.

Sourcegraph provides the most transparently verified posture with ISO/IEC 27001:2022 certification, independently audited and certificates available through their Security Portal. The Security Portal made certificates immediately accessible, allowing our security team to complete the initial review within one business day. Self-hosted deployment provides complete data sovereignty for regulated industries.

Sourcegraph Cody vs Cursor vs Augment Code: Which Should You Choose?

Get AI That Understands Your Distributed Architecture

Your team needs AI that understands your codebase's structure and suggests changes that align with architectural constraints. The choice between these three platforms ultimately depends on which constraint matters most to your organization.

Augment Code's Context Engine maintains semantic understanding across entire repositories, achieving strong benchmark performance on industry-standard evaluations. The 70.6% SWE-bench score reflects the combination of sophisticated reasoning with comprehensive codebase context.

What this means for your team:

• Context that scales: Process large codebases through semantic analysis with persistent architectural understanding that survives across development sessions
• Security certifications: ISO/IEC 42001 and SOC 2 Type II (request reports directly for verification before procurement)
• Enterprise deployment flexibility: SaaS, VPC isolation, and air-gapped configurations for regulated industries
• Architectural analysis: Identify cross-service dependencies and issues spanning multiple components before they reach production
• IDE flexibility: Support for VS Code, JetBrains, and Vim/Neovim means no forced migration for development teams

Augment Code's Context Engine achieves 70.6% SWE-bench score vs GitHub Copilot's 54%. Request a demo for your codebase →

✓ Context Engine analysis on your actual codebase

✓ Enterprise security evaluation and certification verification

✓ Scale assessment for your specific repository size

✓ Integration review for IDE and Git platform compatibility

✓ Custom deployment options discussion

before procurement decisions.

Related Guides

• Context Engine vs RAG: 5 Technical Showdowns for Code AI
• GitHub Copilot vs Sourcegraph Cody: Which Gets Your Codebase
• Cursor vs Sourcegraph Cody: Embeddings and Monorepo Scale
• AI Coding Assistants for Large Codebases: A Complete Guide
• Context Engine vs Context Windows