Arnav Sahu
It is crazy how giving away your code for free has become a competitive business advantage. Successful companies such as Hashicorp, JFrog, Elastic, MongoDB and Gitlab have demonstrated the power of open source models.
Unlike typical enterprise software companies, open source startups must go through two journeys for finding product-market fit: First, building a product that users would download and use for free, and then building features that users would pay for.
Effectively, open source startups need to build two product road maps and companies. Since successful open source projects might have hundreds or thousands of free users, they have potential customers of varying shapes and sizes. The challenge for open source startups then becomes how to define the ideal customer profile (ICP) for users who would potentially pay and finding how to reliably and repeatedly convert free users to paying customers.
In the early days of achieving product-market fit (PMF), it is critical for open source startups to identify and serve a narrow ICP and find how to repeatedly acquire and close paying users. Revenue traction alone is not a sign of product-market fit.
The beauty of open source software is that anyone can download and use it for free. This enables open source companies to acquire a vast, free user base. Unlike freemium models (like Zoom or Slack), users can see the source code and configure it to their own environments. This is especially helpful for influencing enterprise infrastructure buying decisions, where it can be hard to convince a large customer to run their infrastructure while relying on a young startup.
However, if the customer already has many developers who use the free version of the software and can see the code, and test and configure it to their needs, it becomes easier for the CIO or CTO to trust the startup. Users have already gone through a six- or 12-month journey with the software.
In this regard, the go-to-market journey for an open source company is often less about acquiring new customers and more about conversion sales — upselling add-on paid features to existing free users.
In their early days, startups often need to serve customers that have a similar set of common characteristics, as a narrow ICP definition will help them focus. This set of characteristics could include the size of the customer (the number of employees, whether it is a small, medium or enterprise-sized company, etc.); vertical (technology, financial services, etc.); the common problems faced; the common set of software tools used; and user personas.
Here are a few good examples of a narrow ICP: Engineering managers and directors who work in Series B to Series D technology startups; a company that has 50 to 150 engineers; or a company that frequently deploys code each week and uses modern continuous integration/deployment tools.
Since open source startups have thousands of free users already, they can grow revenue quickly in the early days, yet not achieve product-market fit or repeatability. One of the most common pitfalls in the early days is believing that such startups have product-market fit if they have strong revenue growth and scale but no concrete definition of ICP. This is especially true if they serve the enterprise segment.
Large, old-school enterprise customers often have use cases, problems, integrations and tech stacks that are unique to their needs. For example, a large enterprise customer might pay an early-stage startup to integrate their open source software into their tech stack, which may be outdated or bespoke. Similarly, they might pay for security and analytics features that only apply to their needs.
In the early days, an open source startup might go through its list of free users, convert four to five large, old-school enterprise customers and achieve $3 million to $4 million in annual recurring revenue (ARR) due to high contract values. However, the startup still might not have product-market fit as those large customers did not have a common set of characteristics.
Once such a startup moves on to serve its next 10 customers, it will often realize that its product failed to get traction because the problems, integrations and use cases of its early, large enterprise customers weren’t representative of the broader market.
Enterprise deals also require much longer sales and implementation cycles to close, and feedback can often be slower to arrive. In my view, in the early days, sometimes it is far more impressive to sign 10 similar customers worth $10,000 each ($100,000 ARR) with a common set of characteristics than signing five dissimilar enterprise customers worth $100,000 each ($500,000 ARR).
The other big pitfall in the early days is serving both the enterprise and small to midmarket companies at the same time. Bigger enterprise customers have a completely distinct set of needs than smaller customers. They might need customizations, integrations, security, auditing, control and have a different tech stack than smaller customers.
As a result, startups do not find repeatability in the sales process because both sets of customers need different things. Of course, over time, successful open source companies grow and can serve both simultaneously. But at the beginning, focus is critical to iterating on the product and finding repeatability.
Open source companies are in a unique position because they often have thousands of free users on their platform. The playbook to build in the early days is identifying who is a good customer and who may not be.
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