We detected 47 customers using Sourcegraph and 91 companies that churned or ended their trial. The most common industry is Software Development (40%) and the most common company size is 11-50 employees (40%). Our methodology involves discovering URLs with known URL patterns through web crawling, certificate transparency logs, or modifications to subprocessor lists.
About Sourcegraph
Sourcegraph provides a code intelligence platform that enables developers and AI agents to search, understand, and automate changes across massive codebases at enterprise scale. The platform includes AI-powered tools like Deep Search for code discovery and Amp for autonomous code generation and refactoring.
🔧 What other technologies do Sourcegraph customers also use?
Source: Analysis of tech stacks from 47 companies that use Sourcegraph
Commonly Paired Technologies
i
Shows how much more likely Sourcegraph customers are to use each tool compared to the general population. For example, 287x means customers are 287 times more likely to use that tool.
I noticed that Sourcegraph users are modern, developer-first companies that invest heavily in their engineering teams and internal tooling. The combination of infrastructure-as-code (Pulumi), AI-powered development tools (Cursor), and premium deployment platforms (Vercel Pro) tells me these companies prioritize developer productivity and are willing to pay for best-in-class tools. This isn't a budget-conscious buyer. They're focused on shipping faster and building better products.
The pairing of Cursor and Sourcegraph is particularly revealing. Both tools accelerate how developers understand and write code, suggesting these companies are competing on speed to market and need their engineers working at maximum efficiency. The presence of Pulumi alongside Docker Hub shows sophisticated DevOps practices, where infrastructure is managed programmatically and containers are central to their deployment strategy. Slab appearing frequently makes sense too because companies with complex codebases need equally robust internal documentation to help developers navigate and understand their systems.
The full stack reveals product-led growth companies in their scaling phase. BambooHR signals they're past the startup chaos and have enough employees to need proper HR systems, but they're not enterprise giants. Vercel Pro usage suggests they're building modern web applications, likely SaaS products, where frontend performance matters. These companies are probably in the 50-500 employee range, well-funded, and growing quickly. They're product-led rather than sales-led, investing in engineering excellence to drive growth rather than large sales teams.
👥 What types of companies is most likely to use Sourcegraph?
Source: Analysis of Linkedin bios of 47 companies that use Sourcegraph
Company Characteristics
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Shows how much more likely Sourcegraph customers are to have each trait compared to all companies. For example, 2.0x means customers are twice as likely to have that characteristic.
Trait
Likelihood
Country: US
5.8x
Company Size: 11-50
3.6x
I noticed that Sourcegraph customers are predominantly software-first companies building complex technical products. These aren't traditional businesses that happen to use software. They're companies where code is the core product: SaaS platforms (Uscreen, Innago, Tobi), developer tools (codeValet), financial infrastructure (Payflows), logistics technology (Forto, Zuum), and digital health systems (Carasent, RLDatix). Even the seemingly non-tech companies like Spreetail describe themselves through their "home-grown software" and technical capabilities.
These companies span the full growth spectrum, but the majority sit in a specific middle zone: 50-200 employees with Series A to Series C funding. I see companies like Eluvio (39 employees, Series A, $20M), Conduct (20 employees, Seed, $12.3M), and Aceable (240 employees, Series C, $50M). There are outliers like RLDatix (1,500+ employees) and smaller startups, but the sweet spot is post-product-market-fit companies scaling their engineering teams and dealing with the resulting codebase complexity.
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