AI startups are scaling at a record pace, reaching $1M in annualized revenue in a median of just 11.5 months. But this hypergrowth puts immense pressure on the revenue engine.

Without the right infrastructure, this speed creates critical RevOps challenges:

  • Revenue leakage from rigid pricing models that can't adapt to new GTM strategies or customer value metrics.
  • Manual quote-to-cash cycles that consume engineering resources, slowing down both product velocity and time-to-revenue.
  • Brittle, manual revenue processes that become devastating bottlenecks overnight as usage spikes.
  • An inability to support the complex hybrid and usage-based models that AI customers demand, putting you at a competitive disadvantage.

This guide, brought to you in partnership with Stripe, provides a blueprint for building a resilient and automated revenue architecture.

Learn how AI leaders like ElevenLabs, Runway, and Hex built their GTM stack to scale from day one.

Grab your copy:

What you'll learn

Inside, find practical frameworks and case studies from AI leaders who built revenue engines for massive scale:

  • Optimize pricing as a GTM lever: Learn how companies like Runway monetized complex hybrid models from the start, and how Hex transitioned to usage-based billing to align revenue with costs and customer value, improving unit economics.
  • Automate the quote-to-cash lifecycle: See how ElevenLabs scaled to a unicorn valuation with just one engineer managing the entire billing function, freeing up valuable R&D resources to focus on their core product.
  • De-risk global expansion: Discover the strategies Leonardo AI used to launch in 189 countries, automating tax collection, and recovering over 40% of failed payments to protect ARR.
  • Eliminate revenue bottlenecks: Understand how Decagon built a fully agentic billing workflow in just one week with a single engineer, allowing them to respond to customer needs without derailing the product roadmap.

Inside the playbook…

These are the core principles for building a modern AI revenue engine:

  • Your tech stack defines your GTM speed. Monetization models shouldn't be limited by your tools.
  • Automate revenue workflows, don't just hire billing teams. Smart automation scales your team's impact and preserves capital.
  • Build for global scale before it becomes a problem. Today's AI companies sell into twice as many countries in their first year as SaaS companies did.
  • Treat "operational drag" as a critical risk to revenue. Manual invoicing and chasing failed payments can stall growth during your most critical phase.

Brought to you in partnership with Stripe.