How Confluent went from 0 to $11B in 7 Years: The Open Source Masterclass!

Did you know Kafka was originally developed at Linkedin? Linkedin open sourced Kafka in 2011. In 2014, Jay Kreps cofounded Confluent as “managed and value added Kafka” and within 7 years, Confluent did a successful IPO at a valuation of $11.4 billion. This is an incredible success story with valuable lessons for DevTool Companies with Enterprise offerings to Open Source users.

By
Piyush Agarwal
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Case Study
5
min read

In 2014, Jay Kreps and his co-founders Neha Narkhede and Jun Rao made a bold move. They left their stable, high-profile jobs at LinkedIn to pursue a dream: build Confluent, a commercial platform that would revolutionize how organizations process and act on real-time data. At its heart was Apache Kafka, an open-source project they had developed at LinkedIn three years earlier.

At the time, the idea of building a business on an open-source platform seemed audacious. Yet, within just seven years, Confluent went public with a staggering valuation of $11.4 billion. Today, it’s a cornerstone of data infrastructure for companies like Walmart, ebay and intel.

So, how did Confluent achieve this? Let’s take a closer look at their journey and uncover the strategies that turned an open-source project into an enterprise juggernaut.

The Foundation: Apache Kafka

The story begins at LinkedIn, where Jay Kreps was grappling with a monumental problem: managing massive volumes of real-time data generated by the platform’s millions of users. Existing solutions were either too slow, not scalable, or incapable of handling the complexity of LinkedIn’s use cases.

Kreps and his team envisioned a new way forward - treating data as a continuous stream rather than discrete batches. This idea gave birth to Apache Kafka in 2011, an open-source distributed event streaming platform that allowed systems to process real-time data at scale.

Within years, Kafka became the backbone of modern data-driven enterprises, adopted by tech giants like Netflix, Spotify, and Uber. But while Kafka’s open-source model made it widely accessible, it posed challenges for enterprise adoption. Companies needed reliable support, advanced features, and an easier way to manage Kafka. This gap in the market was the catalyst for Confluent.

The Early Days: Building a Business Around Open Source

When Confluent was founded in 2014, it was more of an idea than a business. The team set up shop in a humble office behind a dentist’s clinic in Mountain View. Their mission? To turn Kafka into a full-fledged data streaming platform that enterprises could depend on.

But the journey was far from smooth. Early investors were skeptical of the idea of building a company around open-source software. Critics suggested pivoting to vertical solutions or focusing on competing with entrenched players like IBM. Kreps and his team stuck to their vision.

The first challenge was defining their Ideal Customer Profile (ICP). They quickly realized that organizations not already using Kafka were unlikely to adopt Confluent. Selling to non-Kafka users was inefficient and unproductive. This hard-earned lesson sharpened their focus: their market was the Kafka user base, and their goal was to help these users derive maximum value from the technology.

The Power of Content: Growing Kafka, Growing Confluent

Confluent understood early on that the success of their business depended on the success of Kafka. The more widely Kafka was adopted, the larger Confluent’s market would become.

To achieve this, they invested heavily in Kafka evangelism:

  • Educational content: Tutorials, quickstart guides, and documentation made Kafka more accessible to developers.
  • Community building: Events like Kafka Summit brought together developers, architects, and decision-makers to discuss best practices and innovations.
  • Developer Relations (DevRel): Led by Tim Berglund, the DevRel team focused on growing Kafka’s adoption while maintaining its open-source ethos.

This focus on education and community-building had dual benefits:

  1. It expanded Kafka’s reach - in turn increasing Confluent's Total Addressable Market (TAM)
  2. It positioned Confluent as the leading authority on Kafka

Product Strategy: Balancing Open Source and Commercial Value

One of the toughest balancing acts for Confluent was navigating the line between Kafka (free and open-source) and their commercial offering.

Their solution was strategic:

  • Free tier: They offered a robust free version of their cloud product, which provided a frictionless entry point for Kafka users. This approach avoided alienating the developer community
  • Value differentiation: Confluent focused on providing enterprise-grade features that went beyond what Kafka could offer, including multi-cloud support, security enhancements, and advanced stream governance. They didn’t just position Confluent as “Kafka, but better”—they made it indispensable for large-scale enterprise use.

To reinforce this, they published detailed comparisons like:

Confluent Cost Estimator

Image Source

Data-Driven Targeting: Finding the Right Accounts

One of Confluent’s biggest hurdles was identifying companies that used Kafka since open-source adoption doesn’t leave a paper trail. Moreover, not all companies using Kafka would be open to buying Confluent. They solved this challenge by aggregating intent signals from all possible channels and using that to feed into their sales and marketing strategies. Some of the signals that mattered to Confluent were:

  • Job postings: Companies hiring for Kafka-related roles
  • Social profiles: LinkedIn profiles listing Kafka skills
  • Content engagement: Activity on Confluent’s technical content like Kafka-related blogs, webinars, and tutorials 
  • Team: No of Kafka developers in a company
  • Product Trials: Engagement with Confluent’s free Cloud or self-hosted products

This intelligence enabled Confluent to target prospects with precision, optimizing their sales and marketing efforts.

The Role of Sales: Not Just Developers

While Confluent relied heavily on a developer-first motion, they knew that closing enterprise deals required engaging decision-makers. This is where their Sales Development Representatives (SDRs) came in. Reporting directly to the marketing team for better alignment, SDRs reached out to key personas within target accounts.They categorized their audience into 4 key personas: Developers, Architects, DevOps, and Technical Executives and targeted them with dedicated messaging that worked for each

  • Developers: Focused on technical resources and hands-on support.
  • Architects: Showcased scalability and integration capabilities.
  • DevOps: Highlighted reliability and ease of management.
  • Executives: Emphasized cost savings and strategic benefits.

This multi-persona approach ensured that Confluent’s value proposition resonated across all levels of an organization.

Scaling Up: The Cloud Transformation

As Confluent grew, they faced a pivotal decision: move to the cloud. This was no small feat. Their on-premise product was thriving, but Kreps recognized that the future lay in cloud-native solutions.

The transition required overhauling their engineering, sales, and marketing efforts. It was a painful but necessary transformation that opened the door to larger customers and new revenue streams. Today, Confluent Cloud accounts for a significant portion of their growth, with subscription revenue increasing by 27% year-over-year.

Lessons for DevTool Companies and Best Practices

Confluent’s success story is packed with actionable insights for DevTool companies:

(1) Define your ICP Sharply. For DevTool companies, ICP goes beyond just Company size and HQ Location. 

Best Practices:

  • Use technographic data to identify your audience (e.g., stack data, GitHub repositories)
  • Use data like Engineering team size and mix, Nature of business to further sharpen your ICP
  • Realignment: Pivot quickly if early outreach shows misalignment.

(2) Invest in Content to grow Top of the Funnel:  The role of good quality content cannot be over emphasized, especially when targeting developers

Best Practices:

  • Build educational resources that solve real pain points.
  • Leverage existing communities for organic reach.
  • Consistently align content with the developer’s journey (from setup to scale).

(3) Define your Value Proposition clearly to distinguish from Open Source: Have a clear value proposition and communicate it effectively 

Best Practices:

  • Continuously update your messaging to emphasize product advantages.
  • Use quantifiable proof points (e.g., cost savings, efficiency metrics).
  • Highlight case studies demonstrating the success of enterprise customers.

(4) Invest in Sales Intelligence to drive Revenue: Great sales intelligence not only streamlines sales and marketing efforts, but can also result in higher conversions by enabling reach outs to the right person, at the right time with the right messaging

Best Practices:

  • Use intent data platforms and analytics tools to streamline lead qualification.
  • Monitor both direct (product interaction) and indirect (community engagement) signals.
  • Continuously refine the lead scoring model to adapt to changing buyer behaviors.

(5) Do targeted SDR outreach: Reaching out to Developers and Tech Leaders is not a sin. However, getting the messaging right focussing on adding value to them is important

Best Practices:

  • Compliment developer first motion with engagement with the decision makers
  • Align outreach scripts with persona-specific pain points.
  • Avoid generic pitches—ensure every interaction adds tangible value.

Confluent didn’t just build a business—they built a movement. For DevTool companies looking to scale, their journey offers a roadmap for turning open-source success into enterprise dominance.

How Reo.Dev Can Help You Build a Winning Go-to-Market Strategy

Confluent harnessed intent signals to power their $11.4B success. Your next revenue breakthrough might already be in your data, Don’t let it slip away!

Unlock it with Reo.Dev - Book a Demo Now.

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