Augment Code vs Sourcegraph Cody: Two Context-First AI Assistants for Large Codebases

Augment Code and Sourcegraph Cody are the two AI coding assistants that have taken large-codebase context most seriously. While tools like Cursor and GitHub Copilot work primarily from open files and local context, both Augment and Cody were built around the premise that the hard problem in enterprise codebases isn't model quality, it's knowing which code to look at in the first place. The comparison between them is less about fundamental philosophy and more about execution, pricing, and where their architectural choices diverge.

The shared premise and where they differ

Both tools index your codebase and use that index to improve every interaction. Both target engineering teams where codebase complexity makes general AI assistance frustrating. Both have enterprise security postures designed for regulated industries.

Where they differ is in how the indexing works, what the context retrieval does with that index, and significantly, how they're priced.

Augment is a newer company that built its context engine from scratch specifically for AI-assisted coding. It doesn't have a legacy code search business behind it. The context engine indexes the entire codebase at a deeper semantic level than file-based tools, and the VS Code and JetBrains plugins surface that context in inline completions and chat. The Auggie CLI adds terminal-based agentic workflows.

Cody stands on Sourcegraph's decade of code search infrastructure. Sourcegraph has been building graph-based code indexing since 2013, and Cody uses that graph as its retrieval layer. When you ask Cody a question, it traverses the symbol graph to pull in structurally relevant context, not just files that are textually similar. The result is cross-file and cross-repo context that's grounded in how the code actually relates, not just how it reads.

Context quality: what actually happens when you ask a question

The context retrieval experience is the heart of the comparison.

Cody's code graph traversal is most powerful when relationships between symbols matter. Ask it how a function is used across the codebase and it traces call sites through the graph. Ask how an interface is implemented and it finds every implementing class. For developers new to a codebase or working across unfamiliar service boundaries, this structural awareness surfaces answers that would take manual grep work to find otherwise.

Augment's context engine takes a different approach, focusing on deep semantic indexing that captures the meaning and purpose of code patterns rather than just structural relationships. In practice, Augment tends to be faster at answering questions about proprietary patterns and internal conventions that only exist in your codebase. It indexes higher-level concepts, not just symbol references, which makes it useful for questions like "what's the standard way we handle authentication in this codebase" where a graph traversal alone doesn't give you the full picture.

Neither approach dominates the other in every situation. Cody is stronger when structural code relationships are the key to the answer. Augment is often stronger when the question requires understanding organizational patterns and conventions beyond what the code graph can represent.

Pricing: the most obvious difference

This is where the comparison gets complicated.

Augment:

• Free tier for individuals
• Teams: approximately $50/user/month
• Enterprise: custom pricing

Cody:

• Free tier for individuals
• Pro: approximately $9/user/month
• Enterprise Starter: approximately $59/user/month (includes full Sourcegraph code search)
• Enterprise: custom pricing

For pure assistant functionality, Cody Pro at $9/month versus Augment Teams at $50/month is a significant gap. A 10-person engineering team pays roughly $108/month for Cody Pro versus $500/month for Augment Teams. That's a real budget conversation.

The justification for Augment's pricing is the argument that its context quality and enterprise integration are worth the premium for teams where those capabilities drive meaningful productivity gains. Augment is explicitly positioned as a premium enterprise tool, not a budget option. The question is whether the quality gap is large enough to justify roughly 5x the per-seat cost of Cody Pro.

For teams that need the full Sourcegraph code search platform alongside the AI assistant, Cody Enterprise Starter at $59/user/month is actually more expensive than Augment Teams. But that comparison includes a different product bundle.

Enterprise features compared

Both tools have credible enterprise security stories. Augment has SOC 2 Type II compliance and an explicit policy of not training on customer code. The context engine runs with privacy-respecting architecture that many enterprise legal teams can accept.

Cody's Enterprise BYOL deployment is potentially more flexible for organizations with strict data residency requirements, because model traffic stays inside your infrastructure entirely. If your compliance requirement is that code never leaves your network, Cody can satisfy that in a way Augment's current architecture cannot.

Cody Enterprise also includes the full Sourcegraph code search platform, which is a powerful standalone tool for code navigation, cross-repo search, and automated refactoring at scale. Teams that would benefit from code search independently may find the bundled value of Cody Enterprise compelling even at its higher price point.

Model flexibility

Cody has a model picker that lets users choose between Claude Opus 4.7, Claude Sonnet 4.6, GPT-5, Gemini 3, and others per session. This flexibility means you can optimize for different tasks: a more capable model for complex architectural questions, a faster model for quick lookups.

Augment's model story is less transparent. The product abstracts the underlying model from the user, which simplifies the experience but limits control. For organizations that have strong preferences about model providers, Cody's explicit model picker is a concrete advantage.

Agentic capabilities: Auggie vs the CLI

Augment's Auggie CLI is a terminal-based agentic tool that can execute multi-step coding tasks from the command line. It's positioned for senior developers who want to delegate complex tasks rather than just get assisted with active coding.

Cody's agentic capabilities are more limited in the free and Pro tiers. Enterprise Cody has some automation features, but Augment's Auggie CLI is a more developed agentic offering at the current state of the products.

For teams evaluating AI agents for autonomous task execution, Augment has a more mature story in this area.

Comparison table

Who should use Augment

Augment makes sense for enterprise engineering teams where the $50/user/month price is justifiable and the context engine's quality on internal conventions and proprietary patterns is a daily productivity driver. It's particularly well-suited to teams where new engineer onboarding time is costly and where codebase complexity regularly creates frustration with general AI tools.

The Auggie CLI is also a draw for teams that want terminal-based agentic task execution from a tool that understands their full codebase.

Who should use Cody

Cody makes sense for teams where structural code graph reasoning is the primary need, for JetBrains-first organizations where Cody's JetBrains support is solid, and for teams where the $9/month Pro price makes broad deployment practical.

For teams with data residency requirements that require all model traffic to stay on-premises, Cody Enterprise's BYOL deployment is the right choice. And for organizations that would benefit from the full Sourcegraph code search platform alongside AI assistance, Cody Enterprise bundles real value.

The honest summary is that both are good tools for the same core problem. Cody is cheaper and has more flexible enterprise deployment options. Augment arguably produces better contextual answers on complex internal codebases and has more developed agentic capabilities. The right choice depends on budget, security requirements, and which quality axis matters more for your team's actual daily work.

For related comparisons, see Augment vs Cursor for how Augment stacks up against the most popular general-purpose AI editor, and Cody vs GitHub Copilot for Cody's comparison with the market leader.