Cody
AI code generation software
Generative AI software
- Features
- Ease of use
- Ease of management
- Quality of support
- Affordability
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What is Cody
Cody is an AI coding assistant that helps developers generate, explain, and refactor code using natural-language prompts inside developer workflows. It is commonly used for code completion, writing tests, answering questions about a codebase, and accelerating routine programming tasks. The product emphasizes codebase-aware assistance by indexing repositories so responses can reference project context rather than only general programming knowledge.
Codebase-aware assistance
Cody is designed to use repository context so it can answer questions and generate changes that align with a specific project’s structure and conventions. This supports tasks like onboarding, navigating unfamiliar code, and making localized edits with fewer manual lookups. In practice, this can reduce back-and-forth between an AI chat tool and separate code search or documentation.
Developer workflow integrations
Cody is typically used from within IDE/editor workflows, which keeps prompting and code insertion close to where developers write and review code. This reduces context switching compared with general-purpose generative AI chat interfaces. It also supports common coding-assistant interactions such as inline generation and chat-based Q&A.
Broad coding task coverage
The product supports multiple day-to-day development scenarios, including code generation, explanation, refactoring suggestions, and test creation. This makes it usable across different stages of the software lifecycle rather than a single narrow feature. Teams can standardize on one assistant for several repetitive tasks.
Output requires human review
Like other generative coding assistants, Cody can produce incorrect, insecure, or non-compiling code and may hallucinate APIs or project details. Developers still need to validate outputs with tests, code review, and security checks. This limits suitability for fully automated changes without oversight.
Context and indexing constraints
Codebase-aware behavior depends on how well repositories are indexed and what context is available at prompt time. Large monorepos, generated code, or complex dependency graphs can reduce retrieval quality or increase latency. Some files or systems may be excluded due to configuration, permissions, or size limits.
Governance and data considerations
Using an AI assistant in development raises questions about data handling, model selection, and where prompts/code are processed. Organizations with strict compliance requirements may need additional controls for access, logging, and retention. Procurement may also require clarity on how proprietary code is used for inference and whether it is stored.
Plan & Pricing
| Plan | Price | Key features & notes |
|---|---|---|
| Cody Enterprise | Contact sales / Custom pricing | Enterprise-grade offering: unlimited usage and codebase context, enterprise security controls, BYO LLM options (AWS Bedrock, Azure OpenAI), free trial available (enterprise trial). Per official site, contact sales or book a demo for pricing and trial. cite |
| Cody Pro (discontinued) | $9 per month (historical) | Individual Pro plan historically offered unlimited chat/commands and access to flagship models; Sourcegraph announced Cody Pro is discontinued (access retained until July 23, 2025). cite |
| Cody Free (discontinued) | Free (historical) | Historically a perpetual free tier for individual developers; Sourcegraph announced Cody Free is discontinued (access retained until July 23, 2025). cite |
Seller details
Sourcegraph, Inc.
San Francisco, CA, USA
2013
Private
https://sourcegraph.com/cody
https://x.com/sourcegraph
https://www.linkedin.com/company/sourcegraph/
