Sourcegraph vs Devin

Code Intelligence vs. Code Autonomy

The fundamental difference between Sourcegraph and Devin is whether AI should make developers smarter or replace them for certain tasks. Sourcegraph's entire value proposition rests on the insight that understanding existing code is just as important as generating new code. Its code graph indexes millions of lines across repositories, providing cross-references, dependency tracking, and semantic search that make every AI interaction grounded in your actual codebase—not generic training data.

Devin takes the opposite bet: that for many engineering tasks, the bottleneck isn't understanding code but executing on it. Given a well-defined task, Devin autonomously plans an approach, writes code across multiple files, sets up environments, runs tests, and iterates on failures. This is the trajectory from vibe coding—where humans direct AI through prompts—to full agent autonomy where AI handles the entire workflow.

Neither approach invalidates the other. In practice, many organizations will use both: Sourcegraph to maintain deep codebase understanding for their human developers, and Devin-style agents for parallelizable, well-scoped tasks. The question is which capability is your current bottleneck.

The Amp Factor: Sourcegraph's Agentic Pivot

Sourcegraph's launch of Amp in early 2026 signals a strategic pivot toward agentic coding. Amp is not just an AI assistant—it's a full agentic coding tool with three modes: Smart for unconstrained frontier model use, Rush for fast narrowly-defined tasks, and Deep for extended reasoning on complex problems. With unconstrained token usage and support for models like Claude Opus 4.6 and GPT-5.4, Amp directly competes with tools like Cursor and GitHub Copilot.

However, Amp still operates within the human-in-the-loop paradigm. The developer initiates, reviews, and accepts changes. This contrasts sharply with Devin's fire-and-forget model where you assign a task and come back to a pull request. For teams that value control and understanding over raw throughput, Amp's approach—powered by Sourcegraph's unmatched code intelligence—may deliver better outcomes than autonomous agents that occasionally go off-track.

Multi-Agent Orchestration and Scale

Devin's multi-agent capabilities represent a genuinely novel approach to software development. The ability to spin up multiple parallel Devins, each working in its own cloud IDE, and even have a supervisory Devin orchestrate other Devins, points toward the multi-agent future of software engineering. This architecture enables teams to tackle dozens of tasks simultaneously—migration tickets, bug fixes, test coverage improvements—in a way that no human-in-the-loop tool can match.

Sourcegraph's approach to scale is different: rather than multiplying agents, it multiplies developer effectiveness. Batch Changes lets teams apply automated code modifications across hundreds of repositories simultaneously. Code Insights tracks codebase-wide metrics over time. The Deep Search feature uses specialized subagents to find relevant files across massive codebases. It's infrastructure-level scaling rather than agent-level scaling.

For organizations considering the SaaSpocalypse thesis—where AI agents can build custom software autonomously, undermining per-seat SaaS economics—Devin's multi-agent model is the more direct embodiment. But Sourcegraph's infrastructure is what makes any AI agent effective in real-world codebases.

Quality, Trust, and the PR Merge Rate Question

Cognition reports that 67% of Devin's PRs are now merged, up from 34% a year ago. That's impressive progress—but it also means a third of Devin's work still gets rejected. For teams with strict code quality standards, this creates a review burden that partially offsets the productivity gains from autonomous generation.

Sourcegraph's approach sidesteps this trust problem entirely. Because Cody and Amp operate as assistants—suggesting code that developers review before committing—the quality floor is determined by the human developer, not the AI. The AI's role is to surface the right context, suggest informed completions, and help developers understand unfamiliar code faster. Mistakes are caught at suggestion time, not at PR review time.

Devin's own Devin Review feature—which analyzes PRs, groups logical changes, and explains code hunks—is Cognition's acknowledgment that autonomous code still needs human verification. The question is whether it's more efficient to have AI assist a human writing code or to have a human review AI-written code. The answer depends on task complexity and your team's risk tolerance.

Pricing and Value Economics

Cognition's decision to slash Devin's price from $500/month to $20/month with Devin 2.0 was a watershed moment for autonomous AI coding. The Core plan at $20/month plus $2.25 per ACU (Agent Compute Unit) makes Devin accessible to individual developers and small teams. The Team plan at $500/month includes 250 ACUs and adds API access and priority support.

Sourcegraph offers Cody Pro at $9/month with a free tier available, making it the more affordable entry point for AI-assisted coding. Amp's unconstrained token model is compelling for developers frustrated by usage limits on other tools. Enterprise pricing for the full Sourcegraph platform (Code Search, Batch Changes, Code Insights) is custom but represents a significant infrastructure investment.

The value calculation differs fundamentally: Sourcegraph's cost scales with the number of developers using the platform, while Devin's cost scales with the number of tasks you assign it. For teams with many small, well-defined tasks, Devin's per-ACU model can deliver extraordinary ROI. For teams where developer productivity across a complex codebase is the bottleneck, Sourcegraph's per-seat model is more predictable.

Integration with the MCP Ecosystem

Both tools are positioning themselves within the emerging ecosystem of AI agent protocols. Sourcegraph offers an MCP server that exposes its code intelligence capabilities to any MCP-compatible agent or tool. This means other AI coding assistants—including potentially Devin—could leverage Sourcegraph's code graph for better context.

Devin integrates through Slack, GitHub, and its own API, fitting into existing developer workflows rather than requiring developers to adopt a new IDE. The multi-Devin orchestration model also aligns with the broader trend toward agent orchestration patterns where specialized agents coordinate through shared protocols.

This interoperability angle may be the most important long-term consideration. Sourcegraph's code intelligence could become the foundation layer that autonomous agents like Devin build on—making these tools complementary rather than competitive in a mature AI development stack.