Software pricing has shifted over the past decade. Subscription tiers built around fixed seat counts fail to match the actual patterns of modern software use. Usage based pricing has emerged as a model that connects revenue directly to consumption levels. This may involve API calls, data processed, or active users. The approach appears simple at first, yet it changes how sales and product teams handle operations, internal discussions, and performance tracking.
Why the Old Model Created Silos
Traditional SaaS environments gave sales and product teams largely independent roles. Sales teams concentrated on closing deals and locking in annual contracts. Product teams directed efforts toward feature development and churn reduction. Joint work usually stayed limited to initial kickoff calls and scheduled quarterly reviews.
The pricing approach strengthened this divide. Customers paid a set fee regardless of how much they used the product. Sales teams could finalize agreements with limited insight into usage patterns. Product teams could release updates without concern for pricing alignment.
Such arrangements proved adequate while buyers tolerated unclear pricing structures. They lose effectiveness when buyers insist on paying only for actual consumption.
What Changes When Pricing Follows Usage
Sales Becomes a Long-Term Revenue Partner
Usage based pricing removes the idea that signing the first contract marks completion. Revenue now expands or contracts according to post-signing product use. This reality alters sales team priorities in fundamental ways.
Instead of seeking maximum upfront commitments, sales teams now focus on identifying which customer segments show strong potential for usage growth, setting clear expectations around costs at various consumption levels, staying engaged after the sale to track account health, and partnering with customer success to catch usage declines before they become churn.
Sales representatives shift toward roles as account partners rather than pure deal closers. This requires meaningful updates to how they are compensated, which is covered in more detail below.
Product Teams Start Thinking in Revenue Terms
Product managers once operated mainly with terms such as features, user stories, and retention numbers. Usage-based models require these teams to examine how individual features influence billable events.
After a team releases a new capability, key considerations now cover several points:
- Does this feature increase usage events that contribute to revenue?
- Does it remove barriers that once restricted consumption?
- Do usage patterns indicate points where customers lose momentum before reaching full value?
Product managers do not need to obsess over billing numbers. They do need clear visibility into how features connect to revenue, and that requires regular coordination with sales and finance.
Shared Data Becomes Non-Negotiable
Usage-based models create a practical need for both teams to work from identical usage information. Sales requires current consumption data for effective account conversations. Product depends on usage patterns to make roadmap decisions.
Separate reports and disconnected systems create misalignment quickly. Sales representatives without visibility into usage trends cannot hold productive expansion conversations. Product managers without insight into which features drive consumption base their priorities on incomplete information.
Choosing the right usage based billing software is an operational requirement for keeping sales and product working from the same picture.
Where the Friction Usually Appears
In practice, the shift creates predictable points of tension.
Forecasting differences arise frequently. Sales teams are asked to project quarterly revenue, yet usage-based figures are harder to estimate than fixed renewals. Product teams face pressure to build features that support usage stability or growth, but without agreed-upon indicators, those requests feel vague.
Attribution questions also surface. When usage grows after a product change, sales and product may both claim credit. Without defined attribution methods, that ambiguity weakens cooperation over time.
The deepest friction point, however, is compensation. Sales incentive structures built for subscription SaaS break down under usage-based models in specific and painful ways.
The Real Problem: How to Restructure Sales Compensation
This is where most companies struggle the most, and where the least guidance exists.
Under traditional SaaS, quota credit is straightforward: close a contract, book the ACV, move on. Usage-based pricing complicates every part of that equation. If a customer commits to $200,000 in contracted ARR but only consumes $80,000 in year one, should the account executive receive quota credit on the commitment or the consumption? Most companies default to crediting contracted ARR at signing, which preserves the incentive to close but does nothing to encourage the post-sale behavior that actually drives revenue.
A more functional approach splits the comp structure. Account executives receive partial quota credit at signing based on a realistic consumption estimate, with the remainder credited as the customer reaches consumption milestones, typically at 60 and 90 days. This creates a direct incentive for AEs to set customers up for success rather than overselling and handing off. Some companies, including early Snowflake and several Twilio teams, introduced ramp periods of 90 to 120 days during which clawback provisions apply if consumption falls significantly below the signed estimate. That mechanism discourages sandbagging on usage projections during the sales cycle.
AI companies face a sharper version of this problem: because per-call costs vary with model choice and input length, consumption estimates made during the sales cycle can diverge significantly from actual spend, making accurate quota attribution between sales and product considerably harder to manage.
Expansion revenue requires a separate conversation about how credit is split between the account executive and the customer success manager. One common approach assigns the AE full credit for expansion during the first 12 months, then transitions ongoing expansion credit to CS. This reflects the practical reality that customer success owns the relationship once onboarding completes. Datadog, which built one of the more studied usage-based go-to-market motions in enterprise software, ties a meaningful portion of CS compensation to net revenue retention rather than renewal rates, which aligns the CS team’s incentives directly to consumption growth rather than contract continuation.
For companies earlier in the transition, a simpler pilot approach works reasonably well. Select a segment of 15 to 20 mid-market accounts, introduce an expansion bonus of 5 to 8 percent of net new consumption revenue over a six-month period, and track whether AE behavior around post-sale engagement changes. That test generates real data before a broader compensation redesign, which minimizes political resistance.
Practical Steps for Better Alignment
Align on five concrete shared metrics
Before restructuring anything else, sales and product should align on a short list of metrics both teams track and review together. Useful candidates include: consumption rate (actual usage as a percentage of contracted volume), time-to-first-billable-event (how quickly new customers reach meaningful usage after onboarding), expansion velocity (rate of consumption growth in accounts beyond 90 days), and gross usage retention (whether existing customers maintain or reduce consumption quarter over quarter). These metrics create a shared language and reduce the space for later disagreements about what success looks like.
Pilot compensation changes on a defined segment over a defined period
Broad compensation changes rolled out simultaneously generate resistance and make it hard to isolate what is working. Choose a specific account segment and a specific time window, test an expansion incentive structure, and review the results before scaling. Six months is usually enough to see behavioral shifts.
Establish a monthly cross-functional review with structured inputs
Monthly sessions where sales present field insights from recent customer conversations and product walks through roadmap direction provide steady value. The meeting works best when it has a fixed format: sales shares two or three patterns observed in usage conversations that month; product shares one or two roadmap decisions and explains the usage data behind them. That structure produces better exchanges than an open agenda and ensures the meeting does not drift into status reporting.
A Real-World Reference Point
MongoDB’s shift to consumption-based pricing for Atlas offers one of the cleaner public case studies. As the company moved away from subscription commitments toward a model where revenue tracked database usage, the sales motion changed substantially. MongoDB invested in what it called a “land and expand” approach, where initial contracts were deliberately sized to reduce friction to getting started, and account team resources shifted toward driving consumption growth post-signature. The company restructured sales coverage ratios and CS responsibilities to reflect that consumption, not contract size, was the primary growth lever. Revenue from existing customers became a more significant driver of total growth, which required the company to align its product roadmap explicitly around reducing time-to-value and removing consumption barriers. For AI companies, the same dynamic applies with greater intensity: when the billable unit is a model inference rather than a database query, usage variance between customers is wider, onboarding ramp is less predictable, and the case for post-sale alignment between sales and product becomes even harder to ignore.
Conclusion
Usage-based pricing does not just change how customers are billed. It redefines who is responsible for revenue growth and how that responsibility is shared across teams. Sales and product cannot operate in separate lanes when every feature release and every customer interaction connects directly to consumption outcomes.
The structural work required is real: compensation models need to reflect post-sale behavior, shared metrics need to be defined before friction builds, and cross-functional reviews need enough structure to produce useful output rather than general updates.
Flexprice is the usage-based billing software for AI and SaaS companies. It gives revenue and product teams access to the same consumption data in real time, with native integrations across Stripe, NetSuite, Salesforce, and Snowflake, SOC 2 Type II compliance for enterprise procurement, and infrastructure built to handle over 20 billion events per month. Shared data does not fix sales-product misalignment on its own, but it creates the visibility that makes alignment possible.
