Based on a16z interviews with the CFO's of Databricks, ElevenLabs, Together AI, Concourse, and Ambient.ai

Pricing: From Seats to Usage- or outcome-based billing

AI makes it possible to bill for the outcomes your product delivers, not just the inputs customers consume.

“The dramatic difference at Databricks is that our pricing and revenue recognition are based entirely on output, unlike input-based consumption models. If customers don't derive value, they don't consume, and revenue doesn't appear on our P&L.”
— Dave Conte, CFO, Databricks

Outcome alignment can also de-risk revenue and encourage larger commitments:

“Our pricing is built as a function of increasing profit in absolute terms, but decreasing margin in percent terms — we decrease unit prices as customer commitments increase. We automatically discount via our pricing calculator to encourage larger customer commitments. Locking customers into higher spend helps us de-risk revenue.”
— Maciej Mylik, Finance, ElevenLabs

For early-stage teams, relentless iteration is the rule:

“I changed my pricing more than seven times in the 40 days post-launch. It was really helpful to understand the market and customer's appetite to pay. Right now, pricing is just a slide in my deck — something I'll keep iterating on and improving.”
— Matthieu Hafemeister, Cofounder, Concourse

FlyCode takeaway: Usage- or outcome-based billing raises the stakes of every failed payment. Your retry strategy must be just as dynamic as your pricing.

ARR Needs a Makeover

Traditional “new ARR” counters break down when usage swings month-to-month.

“We started annualizing the usage-based revenue and adding it to a new metric — ARR plus annualized usage — because enterprise customers exceed their quotas far more frequently. Without this, we'd be underselling or undercounting what we actually earn.”
— Maciej Mylik, ElevenLabs

“With consumption-based models, how do you think about ARR? You might have a committed relationship, but actual usage varies month-to-month, so traditional ARR definitions become challenging.”
— Noah Barr, CFO, Ambient.ai

Databricks attacks the volatility head-on with granular AI forecasting:

“Unlike SaaS models, where revenue tends to be linear, consumption models are inherently nonlinear — customers surge, then optimize. We manage this volatility by focusing heavily on diversification to avoid customer concentration. ... We track contract values closely, and use AI to help understand and forecast our true consumption-based ARR.”
— Dave Conte, Databricks

FlyCode takeaway: However you modernize ARR, the recovery of every single usage-driven invoice matters. Treat involuntary churn as a forecasting variable—not a rounding error.

The New Gross-Margin Math

Every token, every call, every GPU minute now hits COGS.

“We closely monitor infrastructure spend, and if costs grow faster than usage, we quickly send engineers to optimize it — there's a continuous cycle of managing cost efficiency.”
— Maciej Mylik, ElevenLabs

“We triangulate pricing decisions through customer value proposition, competitive benchmarking, and cost and return analysis. Given how quickly AI infrastructure and software is evolving, we continually re-evaluate. ... Creative pricing and packaging happens quite often based on customer need, term, and deal size — so long as we're carefully considering the unit economics.”
— Hanson Hermsmeier, VP Corporate Finance, Together AI

GPU idle time is the silent margin killer:

“You have to watch costs closely — these GPU costs are significant. We track regrettable idle GPU time as utilization loss, which directly impacts margin and efficiency. Every hour we have GPUs not being used by customers impacts our margins.”
— Hanson Hermsmeier, Together AI

And for vision-based AI, humans-in-the-loop become part of COGS:

“We have a human-in-the-loop (HILT) team that's part of COGS. As algorithms improve, effective adjudications per human go up and unit costs come down, but we still have to bias toward false positives to manage risk.”
— Noah Barr, Ambient.ai

FlyCode takeaway: Margin pressure amplifies the need to collect every dollar owed. Automated card-on-file intelligence can be the cheapest margin lever you have.

R&D ROI: Betting Beyond the Next Quarter

Strategic research can’t always be tied to near-term ARR—but ignoring it courts disruption.

“Not every R&D project can be directly tied to immediate top-line revenue, but through predictive analytics, we measure how certain capabilities, like Unity Catalog, drive higher customer adoption and growth.”
— Dave Conte, Databricks

“Research projects might not directly translate to immediate revenue, but they create significant long-term differentiation, product development, and stickiness, becoming essential in competitive markets. For example, the investment we made in research around kernels enables us to now provide a unique differentiation to our customers to reduce infrastructure costs and increase performance.”
— Hanson Hermsmeier, Together AI

“Pure text-to-speech will inevitably become commoditized. To maintain long-term defensibility, we need sophisticated product layers — workflows, data-rich features, and APIs — so customers become deeply embedded and find switching difficult.”
— Maciej Mylik, ElevenLabs

FlyCode takeaway: When your team invests in new models or value-added layers, make sure payments infrastructure scales too. Nothing erodes ROI faster than churned users you already won.

AI-Powered Forecasting (and Its Limits)

Planning windows are shrinking, but AI can extend your line of sight.

“It can be a challenge to plan even 12 months out in AI right now. There's constant innovation, and new use cases appear quickly. You have to remain agile and factor in change as part of your risk management strategy. ... The sure constant in AI is change. Models evolve rapidly, and inference use cases we haven't even considered today will be critical in a year.”
— Hanson Hermsmeier, Together AI

Databricks forecasts at the workload level—inside its own lakehouse:

“We use Databricks itself — AI, machine learning and advanced analytics — to forecast consumption patterns at the customer, workload, and product levels. This is critical not just for financial forecasting, but also for accurately setting quotas for our large sales team. ... You can't achieve the precision required in consumption forecasting using Microsoft Excel — you have to use advanced analytics, machine learning, and AI to build those predictions.”
— Dave Conte, Databricks

“We’ve got a product, Genie, which is basically a natural language query. So you can type in natural language into your data lake. It extracts answers. Genie will understand your data. And then it learns and understands your data more and more and more, the more you use it.”
— Dave Conte, Databricks

The frontier is still foggy:

“I don't think anyone has fully cracked forecasting revenue for AI. The market is booming and changing so fast that reliable forecasting feels like more of a sanity check than precise predictions.”
— Maciej Mylik, ElevenLabs

FlyCode takeaway: Forecasting may be imperfect, but payment recovery doesn’t have to be. Put adaptive, AI-driven retries on autopilot and let your finance team focus on the bigger puzzle.

Bottom Line for AI-SaaS CFOs

Align price with delivered value—then automate the back-end collection workflows that safeguard that value.

1. Modernize ARR metrics to capture real usage; supplement with tooling that slashes involuntary churn.

2. Track cloud and GPU costs like COGS and respond in near-real-time.

3. Fund strategic R&D that deepens customer lock-in.

4. Use AI-native forecasting while acknowledging its uncertainty—and build flexible cash-flow defenses.

FlyCode exists to eliminate the involuntary churn that muddies every one of these KPIs. Ready to see how AI-driven recovery can lift ARR and margins? Book a demo →