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Keboola

Launched 2014via Nathan Latka Podcast
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The Spark

The team behind Keboola ran a cloud migration agency helping companies like Red Bull transition to the cloud. As they repeated the same data integration work for client after client, they realized they were solving the same problem over and over. "We were lazy," the founder joked. "Because we had done it so many times, there has to be a better way." They started building internal automations, and by 2014, they made the leap: this should be a product, not just an internal tool.

Building the First Version

Unlike many startups, Keboola chose not to raise venture capital early on. "Once you raise, you better know what you want to do with the money," the founder explained. In 2014, they didn't need servers to buy—AWS made infrastructure cheap. They had paying customers from their agency and could bootstrap. They built an end-to-end platform addressing what Snowflake hadn't: the 20+ different tools needed to move data in and out of the data warehouse. Keboola offered one login, 1,400 integrations (built by 400 developers), automation capabilities, and a data science layer.

Finding the First Customers

Their first few years of growth came almost entirely through referrals. Customers who used Keboola grew their own companies—some became unicorns like ProductBoard and the Rohri Group. The founder recalled: "People started to use us. They started to grow their own companies, right?" But they also did something unconventional for a young SaaS company: they partnered with organizations like Czech IT Girls to build community. Their "Data Girls" program trained over 20,000 women globally in data skills. One customer, Rossum AI (on track to become a unicorn), called the founder and said: "I'm trying to hire a head of our data team. This is the fourth candidate who said they'd only join if we have a Keboola license." That's the power of community.

What Worked (and What Didn't)

Transitioning from agency to product forced hard choices. They had to give up lucrative professional services revenue and build a partner ecosystem instead. "In the first year, we made 0.7 of our target, but our partners made four times that money," the founder shared. However, they later learned a critical lesson: SMB companies can rely on partners, but enterprise customers need professional services. UiPath proved the point—47% of their recurring revenue came from professional services when they went public.

During COVID, Keboola was overexposed to hospitality and retail clients who went bankrupt. They also held one massive contract with an Asian financial institution that lost 90% of its business in the first month. But this taught them resilience and the importance of diversification.

They also made financial mistakes. Coming from Eastern Europe with a "cash is king" mentality, they avoided financial instruments, loans, and venture debt. "If we would have known how to use it sooner, we would have scaled even faster," the founder admitted. They should have hired a CFO earlier to navigate options like venture debt and EU subsidies.

Where They Are Now

In response to COVID and the need to scale beyond direct sales, Keboola opened a freemium tier, unlocking product-led growth. Last year, 3,000 companies created Keboola accounts; this year, 4,000. Of the 3,000 from last year, 1,000 engaged (connected data, built pipelines). 400 use it daily on the free tier (with 300 free minutes per month). Already, 100 paid, and 10 of those converted to paid plans. "That's the power of product-led growth," the founder said. Their enterprise strategy and partner ecosystem, built over five years, finally materialized. The platform now processes massive volumes of raw data into business insights for thousands of companies monthly.

Why It Worked
  • The founders had already solved the core problem repeatedly as an agency, which meant they built a product addressing a genuine, recurring pain point rather than a theoretical one.
  • Starting with existing agency clients as initial customers eliminated cold-start risk and provided immediate product-market validation before scaling beyond their network.
  • By bootstrapping without early venture capital, they maintained focus on profitability and customer value rather than chasing growth metrics, which naturally created conditions for sustainable word-of-mouth growth.
  • Building community through programs like Data Girls created organic demand for their product by developing a talent pool that actively wanted to use their platform, turning adoption into a hiring and retention advantage for customers.
  • Positioning as a horizontal platform solving 20+ integration use cases gave them defensibility against point solutions and made their product increasingly valuable as customers added more data tools to their stack.
How to Replicate
  • 1.Start by solving a specific, recurring problem within your own business or client work repeatedly until you can articulate the exact friction points and build credibility as an expert before launching the product.
  • 2.Identify your first customer segment from people who already know and trust you (existing clients, agency customers, or past collaborators) and use their feedback to refine the product before scaling outbound efforts.
  • 3.Bootstrap or minimize external funding in the early stage so you can focus on unit economics and customer acquisition through referrals rather than being pressured to accelerate unprofitable growth.
  • 4.Build community programs (training, certifications, user groups) that create lasting value for your target audience independent of immediate sales, which generates organic demand and customer loyalty.
  • 5.Design your product to be part of a larger ecosystem that customers need rather than a standalone tool, so adoption creates increasing switching costs and natural expansion within existing customer accounts.

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