Challenge

Ahrefs is a Singapore-based SaaS company that provides comprehensive SEO tools and marketing intelligence powered by big data. Since 2011, they've been crawling the entire web daily to maintain extensive databases of backlinks, keywords, and website analytics that help businesses with SEO strategy, competitor analysis, and content optimization. Today, they're trusted by 44% of Fortune 500 companies.

Building and operating a web crawler at internet scale presents extraordinary challenges. Ahrefs needs to index billions of web pages continuously, process petabytes of data in real-time, and turn this massive dataset into actionable insights for thousands of customers worldwide. The technical demands are staggering: their systems must handle 500 billion backend requests per day while maintaining over 100PB of storage.

As a self-funded company, Ahrefs couldn't solve these challenges by throwing unlimited resources at the problem. They needed maximum efficiency from a small team — systems that could run reliably for months without intervention, code that could be understood and maintained by a lean engineering organization, and performance that could compete with tech giants despite having a fraction of their headcount.

The question wasn't just whether they could build a web-scale crawler, but whether they could do it sustainably with the constraints of a bootstrapped company.

Result

Over a decade later, Ahrefs operates one of the world's most sophisticated web crawling operations. Their OCaml-powered systems maintains an index of 492.7 billion pages across 500.4 million domains.

This technical achievement translates directly to business success. Ahrefs has grown into a $100M+ ARR company with 150 employees managing 4000+ servers—all while maintaining their original philosophy of operational efficiency. They've become the sector leader in SEO tools, proving that the right technology choices can create sustainable competitive advantages.

The reliability of their OCaml systems is perhaps most impressive: programs written years ago continue running without surprises, requiring minimal maintenance from their engineering team. This "boring" reliability has allowed Ahrefs to focus engineering effort on building new features and capabilities rather than fighting infrastructure fires.

Their success demonstrates that OCaml can power not just technical excellence at massive scale, but sustainable business growth in highly competitive markets.

Solution

Ahrefs built their crawling infrastructure around OCaml's strengths, creating a distributed system that balances performance, reliability, and maintainability. OCaml serves as the primary language for all crawling and data processing systems, compiled natively for maximum performance across their 4000+ servers.

Their architecture treats data consistency as paramount. Defining shared data structures (using ATD (Adjustable Type Definitions), and now moving to melange-json), they ensure type safety throughout their processing pipeline — from initial web crawling to final data storage. This approach catches schema mismatches at compile time rather than at runtime, crucial when processing billions of pages daily.

Their storage layer combines ClickHouse, MySQL, Elasticsearch. The key insight was designing these systems to work together seamlessly through shared OCaml types rather than complex API layers.

Ahrefs maintains their own libraries and frameworks rather than relying on generic solutions. This "build it ourselves" philosophy requires more initial investment but delivers systems perfectly tailored to web crawling demands. Their 1.5 million lines of OCaml code represent years of accumulated domain expertise encoded in reliable, maintainable software.

The result is a unified system where improvements to crawling algorithms, data processing pipelines, or storage efficiency can be implemented quickly and deployed confidently across their entire infrastructure.

Why OCaml

• Low maintenance burden: OCaml systems built years ago continue running without intervention, allowing engineers to focus on new development rather than troubleshooting production issues.
• Static typing catches errors: At petabyte scale, compile-time type checking prevents data format inconsistencies and runtime failures that would be expensive to debug in production environments processing large volumes of web data.
• Language expressiveness reduces development time: OCaml's abstractions enabled building domain-specific systems efficiently rather than adapting existing frameworks. Small teams could develop complex crawling and data processing systems with relatively few lines of code.
• Performance: Native compilation provides the throughput needed for processing billions of daily requests while maintaining code readability for long-term maintenance.
• Cost-effective specialized tooling: OCaml made it practical to build custom systems tailored to specific requirements rather than using general-purpose solutions, which aligned with their business constraints of limited engineering resources.