M&A strategy, competition, pricing, and open source at dbt Labs

Executive overview

Most founders treat M&A as a last resort and handle it reactively. The window to create leverage closes long before you need it.

Start M&A positioning before you need an exit. Build relationships with potential acquirers by competing in the specific area they care about — making yourself impossible to ignore — while staying friendly enough to keep doors open.

The best M&A outcome comes from building a strong independent business first; everything else is optionality.

Evaluating early-stage companies

• Four dimensions: people, market, product, distribution — rarely 10/10 on all four.
• People: can the founder paint a compelling vision and work at the day-to-day detail level?
• Market: is it growing, and is there space for a new entrant?
• Product signal: users can't stop talking about it; they want to share it with teammates and peers.
• Distribution matters more than product: does the team know how to reach the market — PLG, enterprise sales, or ecosystem?
• When joining, ask which weak dimension you personally de-risk.

M&A strategy for founders

• Start planning when you don't need an exit — having a viable independent path gives leverage in every conversation.
• Only two to three buyers will ever find your product truly strategic; identify them early.
• Inflict pain on the potential acquirer in the exact area where you compete — force them to notice you — but stay friendly and keep communication open.
• Don't prematurely shut down conversations with incumbents; you don't know if you can go the distance alone.
• Use CorpDev teams to your advantage: take the meeting, ask for warm introductions to product or GM sponsors.
• If you have runway: never mention M&A — talk partnerships and collaboration instead.
• If you're out of time: be transparent, cast wide, and don't disguise desperation. "We're running a process, are you interested?" is fine.
• When building a buyer list, target a dozen, then filter by who's digesting recent acquisitions, on a hiring freeze, or sitting out due to market uncertainty.
• Acquirers often buy teams — have your data room ready and lead with the people who will stay.

How dbt Labs thinks about competition

Three pillars:

1. Hold true to the vision — most competitive noise is a distraction; run your own race.
2. Grow the pie — work with ecosystem partners to expand the opportunity rather than carve it up.
3. Lean into strengths — be a platform, leave space for the ecosystem, partner broadly; hold ground only on the transformation and semantic standards.

Why dbt won

• Power through simplicity: SQL practitioners could do data engineering work without a steep learning curve; this unlocked a huge latent audience.
• Commitment to open source lowered adoption friction and created a self-reinforcing flywheel: users → shared best practices → partners building integrations → more users.
• Timing: cloud data warehouse explosion (Snowflake going from $4B to $12B in 2019) created the ideal moment for a transformation layer.
• Founders spent ~two years at Fishtown Analytics consulting — building dbt while solving real client pain firsthand; every friction point went back into the product.
• 20,000+ companies using dbt weekly; 50,000-person Slack community; 30%+ of employees have contributed to the transformation workflow.

Open source vs. proprietary — the open core model

• Open source: the transformation logic, the standard, the ecosystem layer — kept open to maintain community and distribution flywheel.
• Proprietary (dbt Cloud): stateful interactions, cross-team collaboration, production-grade scheduling — things that require scale management.
• Losing a deal to dbt open source is acceptable; the job is to make the cloud offering compelling enough for teams at scale.

Pricing and willingness to pay

• Many startups delayed pricing conversations during zero-interest-rate era; this is a mistake — have it before you build, not when sales is struggling.
• dbt's value is often cited as 20–35% as valuable as a company's cloud data warehouse spend; dbt charges a small fraction of that — by design.
• Pricing is not fixed; it evolves and gets more complex, so build the muscle early when stakes are lower.
• Willingness-to-pay research: ask customers for relative value comparisons, not direct price quotes; probe for "no-brainer", "fair", and "too expensive" thresholds.
• Track conversion and churn rates carefully around any pricing change — that's your elasticity signal.
• Pricing is cross-functional: finance (modeling), product (packaging), product marketing (communication), and customer conversations all required.

Product philosophy and team practices

• "Worse is better" and "tech debt is a champagne problem" — ship, learn, then scale; don't over-engineer before you have users.
• Do fewer things; single-thread the team on one main priority.
• When introducing a complex new algorithm or process, create shared understanding across the whole team — not just the leads.
• Spend meaningful time investing in a network of operators slightly ahead of where you are; bring back the best ideas.
• T-shaped generalism is an asset in product: broad business and financial context makes cross-functional execution faster.
• Apply investor-style thinking to product bets: accept that many initiatives go to zero, but keep making asymmetric bets that could bend the company's trajectory.

dbt Labs core values

• More concerned with value creation than value capture.
• Transparency always wins — open board decks, public Slack, writing culture, hard conversations in the open.
• Humility — success is never assumed; the mission is to serve the community.
• Work done well is its own end — focus on the journey, not just the destination.