Sourcegraph Cody: Enterprise AI Code Assistant - Technical Reference

Configuration & Requirements

Pricing Model

• Enterprise-only (as of July 2025 - free/pro tiers discontinued)
• Thousands per month minimum vs $10/month for alternatives
• Cost/benefit ratio only justified for large, complex codebases

Resource Requirements

• Memory: 60+ GB RAM minimum for 500k+ lines of code
• Processing: 8+ hours initial indexing for 2M line codebase
• Hardware: 32GB RAM minimum, 64GB recommended for substantial codebases
• Failure point: Default Kubernetes resource limits will kill indexing pods at 90% completion

Platform Support

• VS Code: Primary integration, works well
• IntelliJ: Secondary support, "feels like afterthought"
• Eclipse: Exists but problematic
• Repository support: GitHub (excellent), GitLab (fine), Bitbucket (edge cases)

Technical Capabilities vs Competitors

Tool	Context Understanding	Pricing	Critical Limitation
Cody	Reads entire codebase, understands internal APIs	Enterprise-only (expensive)	Setup complexity, resource requirements
GitHub Copilot	Generic suggestions, no codebase context	$10/month	Suggests non-existent functions, ignores internal conventions
Amazon Q	AWS-focused, limited elsewhere	$19/month	Vendor lock-in, poor general purpose use
Cursor	Experimental full IDE replacement	$20/month	Beta quality, production risk
Tabnine	Offline capability, basic suggestions	Cheap	Limited usefulness beyond basic autocomplete

Critical Warnings & Failure Modes

Indexing Failures

• Memory exhaustion: Docker containers get OOMKilled during indexing
• Time investment: 8+ hours for large codebases, often requires restarts
• Breaking point: UI becomes unusable at 1000+ spans, making debugging large distributed transactions impossible

Code Suggestion Risks

• State management: Dangerous suggestions for Redux/React state, broke payment flows twice
• Async operations: Poor handling of complex useEffect hooks and async patterns
• False confidence: Suggestions compile but introduce subtle production bugs

Performance Issues

• File size limit: VS Code extension crashes with files over 5k lines
• Memory leaks: Requires VS Code restarts with large JavaScript files with many imports
• Context limits: Breaks down with extremely complex distributed system patterns

Implementation Reality

What Works Well

• API understanding: Knows actual endpoint names, headers, response formats from codebase analysis
• Pattern recognition: Uses existing error handling patterns, logging conventions, database schemas
• Cross-service awareness: Understands microservice communication patterns and API contracts
• Onboarding acceleration: Reduces new hire onboarding from 3 weeks to 10 days

Security Considerations

• Data retention: Claims zero long-term code storage
• Deployment options: Cloud (standard) or on-premises (complex setup, weeks of configuration)
• Compliance: SOC 2 certified, audit logs available
• Approval timeline: Expect 3+ weeks for enterprise security review

Setup Process

• Cloud version: 5 minutes extension installation
• Enterprise self-hosted: 1-2 weeks setup, requires Kubernetes expertise
• Security review: 3 weeks typical approval time
• Indexing requirements: Plan for multiple restart attempts due to resource constraints

Decision Criteria

Use Cody When:

• Large codebase (500k+ lines) with complex internal APIs
• Multiple microservices with custom communication patterns
• Enterprise budget available (thousands/month acceptable)
• Security team approved cloud or on-premises deployment
• Development team productivity more important than cost

Use Alternatives When:

• Individual developer or small team
• Cost-sensitive project ($10/month vs thousands/month)
• Simple codebase without complex internal systems
• Cannot justify enterprise pricing for productivity gains
• Security requirements prohibit any external code analysis

Resource Investment Requirements

Human Time Costs

• Initial setup: 1-2 weeks for enterprise deployment
• Security approval: 3+ weeks of review process
• Custom prompt creation: Half-day investment per useful prompt
• Failure recovery: Multiple restart attempts during indexing phase

Infrastructure Costs

• Memory: 60+ GB RAM for meaningful codebase analysis
• Compute: Substantial processing power for 8+ hour indexing cycles
• Expertise: Kubernetes knowledge required for enterprise deployment
• Monitoring: Need oversight for memory usage and indexing failures

Migration Considerations

From Other AI Tools

• Context advantage: Immediate improvement in suggestion relevance for internal APIs
• Learning curve: Minimal - familiar chat and autocomplete interfaces
• Integration effort: Standard VS Code extension installation
• ROI timeline: Productivity gains visible within weeks for complex codebases

Risk Mitigation

• Testing requirement: All AI suggestions must pass existing test suites
• Code review: Human review essential for state management and async code
• Rollback plan: Keep alternative tools available during transition
• Monitoring: Track suggestion acceptance rates and bug introduction rates

Operational Intelligence

Common Misconceptions

• "Just another ChatGPT wrapper": False - provides actual codebase context vs generic responses
• "Works out of the box": False - requires substantial setup and resource allocation for enterprise use
• "Replaces code review": False - introduces subtle bugs that require human oversight

Hidden Costs

• Infrastructure scaling: Memory and compute requirements often underestimated
• Security review overhead: 3+ weeks of security team time
• Failure recovery time: Multiple indexing restart attempts
• Human expertise: Kubernetes and enterprise deployment knowledge required

Breaking Points

• 1000+ span UI failure: Debugging large distributed transactions becomes impossible
• File size limits: 5k+ line files cause extension crashes
• Resource exhaustion: Default limits insufficient for production indexing
• Pattern complexity: Struggles with extremely complex distributed system architectures

This technical reference enables AI systems to understand both the capabilities and operational realities of implementing Sourcegraph Cody in enterprise environments.