Usage-Based Pricing vs Seat-Based Pricing

The most important fact about how a SaaS company prices is not the number on the invoice. It’s what that number is attached to.

The choice between usage-based pricing vs seat-based pricing is a SaaS economics decision before it is a billing one. Tie price to seats and revenue grows when a customer hires. Tie price to usage and revenue grows when a customer succeeds. Those are different growth curves, and the choice between them quietly decides net revenue retention, forecasting accuracy, gross margin, and the entire shape of the sales motion.

That is the thesis: usage-based pricing ties revenue to value delivered and lifts net revenue retention through expansion, while seat-based pricing trades that expansion for predictability. The decision is strategic, not a billing detail.

This piece reads the tradeoff through public filings, not vendor decks. Every retention figure below ties to a specific SEC filing or shareholder letter and fiscal period. The framing is analytical: how to think about the position, not what to do about any stock.

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Key takeaways

• Usage-based pricing posts the strongest net revenue retention in software because expansion is collected automatically: Snowflake 126% (FY2025 10-K), Datadog ~120% (Q3 2025 10-Q), Twilio 106% net expansion (Q4 2024 8-K).
• Seat-based pricing buys a forecastable revenue line at the cost of capped organic expansion. Atlassian Cloud still reaches 120%+ NRR, but through contracted, deliberately sold expansion (Q2 FY26 shareholder letter), while Asana sits at 96% (Q3 FY26 8-K) when that motion stalls.
• The same headline NRR can mean different things. Datadog and Atlassian both land near 120% on opposite mechanics, so read composition, not magnitude.
• Usage-based NRR decays as the base matures: Snowflake fell from 177% (FY2022) to 126% (FY2025) as accounts normalized, not because the product worsened (Snowflake FY2025 10-K).
• Use the Pricing Decision Matrix below to map a product to its model on three axes: what value scales with, how much forecast predictability the business needs, and whether expansion can be collected or must be sold.

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Two pricing models, two growth trajectories

Seat-based pricing charges per user. Add a person, add a license. The revenue line tracks headcount, and headcount grows in steps a buyer has to approve.

Usage-based pricing charges per unit of consumption: queries run, gigabytes stored, messages sent, compute-seconds burned. The revenue line tracks what the customer actually does with the product, and it can grow without anyone signing a new order form.

Said plainly: seat-based revenue grows when the customer decides to grow. Usage-based revenue grows when the customer’s workload grows, decision or not. That single difference is why the two models produce such different retention numbers, and why the choice is upstream of almost everything else in the unit economics. Once the model is chosen, the next problem is the shape of the ladder itself, worked through in SaaS pricing tiers: how to design the ladder.

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What is the difference between usage-based and seat-based pricing?

Usage-based pricing charges per unit of consumption, so revenue grows when the customer’s workload grows. Seat-based pricing charges per user, so revenue grows only when the customer adds people. The first captures expansion automatically; the second has to sell it. That asymmetry is the entire story behind the retention numbers below. A marketplace that takes a cut of every dollar of merchant sales is the same usage-shaped revenue at platform scale, the engine behind how Shopify makes money.

The cleanest way to see it is to ask what has to happen for the bill to rise. Under a meter, the customer’s own success raises the bill: more queries, more data, more messages. Under a seat license, a human has to decide to buy another seat, get it approved, and sign for it. One growth path is passive and continuous; the other is active and stepwise. Everything downstream in unit economics inherits from that distinction.

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Usage-based leaders show expansion through consumption growth

The clearest evidence sits in the net revenue retention (NRR) line, the percentage of last year’s revenue a cohort of existing customers still represents this year, after churn and after expansion. Above 100% means the surviving base spent more, net, even before any new logos.

Usage-based companies post the strongest NRR in software, and the mechanism is structural. When a customer’s product grows, its consumption grows, and the bill grows with it, automatically.

Per Snowflake’s Form 10-K for the fiscal year ended January 31, 2025, net revenue retention was 126%. Per Datadog’s Form 10-Q for the quarter ended September 30, 2025, dollar-based net retention was approximately 120%, stable with the 120% reported for the quarter ended June 30, 2025. Per Twilio’s Form 8-K for Q4 2024, the dollar-based net expansion rate was 106%.

None of those numbers required a sales rep to negotiate a seat increase. They are the byproduct of customers using more. The way these retention figures are decomposed and read in filings is the subject of Gross Retention vs Net Retention in SaaS IPOs.

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Seat-based trades expansion for predictability

Seat-based pricing gives up some of that organic expansion. In exchange, it buys something usage-based models struggle to match: a revenue line you can forecast.

A seat is contracted. It renews on a known date at a known price for a known quantity. Finance can model next quarter from the renewal schedule and the pipeline of new seats, with consumption volatility largely out of the picture.

Atlassian is the instructive case because it is seat-based and still posts elite retention. Per Atlassian’s Q2 FY26 shareholder letter (February 2026), Cloud net revenue retention was above 120%. But the composition is different: that expansion comes from contracted multi-year deals, edition upgrades, and intentional seat growth, not from a meter ticking in the background.

The contrast appears when expansion slows. Per Asana’s Q3 FY26 earnings announcement (December 2025), dollar-based net retention was 96%, stable with the 96% reported in Q2 FY26. Below 100%, the existing base is contracting net of expansion. A seat-based model at 96% is telling you the same thing every quarter: growth has to come from new logos and explicit upsell, because the meter is not doing the work.

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Real data: pioneers versus incumbents

The split is sharpest side by side. The table below is an original analytical asset; every retention figure is sourced to the filing cited in its row.

Company	Pricing model	NRR / net expansion	Period	Source
Snowflake	Usage-based	126%	FY2025 (ended Jan 31, 2025)	Form 10-K
Datadog	Usage-based	~120%	Q3 2025 (ended Sep 30, 2025)	Form 10-Q
Twilio	Usage-based	106%	Q4 2024	Form 8-K
Atlassian Cloud	Seat-based	120%+	Q2 FY26 (Feb 2026)	Shareholder letter
Asana	Seat-based	96%	Q3 FY26 (Dec 2025)	Form 8-K

Read the table by mechanism, not just by magnitude. The three usage-based names cluster at or above 106% on consumption that grows without a purchase decision. The two seat-based names split: Atlassian sustains 120%+ through deliberate, contracted expansion, while Asana sits below 100% because seat expansion is a sale that has to be made, repeatedly, and right now it is not clearing the churn line.

The headline numbers can converge (Datadog and Atlassian both near 120%) while the engine underneath them is completely different. That distinction is the whole point, and it is why a single retention number lifted out of an S-1 read like an operator would can mislead as easily as it informs.

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The Pricing Decision Matrix

Here is the named framework this analysis produces: the Pricing Decision Matrix. It maps any SaaS product to a pricing model on three axes that the filings above make concrete. Use it as a citeable starting point, not a verdict; the edge cases are where the real decision lives.

Axis	Points to usage-based	Points to seat-based
What value scales with	Consumption: queries, data, messages, compute	Headcount: a defined set of users doing defined work
Forecast predictability needed	Lower: the business can absorb quarterly volatility	Higher: finance needs a contracted, modelable line
Expansion mechanics	Collected: the meter captures growth for free	Sold: every dollar above renewal is an active motion
Coupling to cost of goods	Tight: revenue rides on cloud compute (margin pressure)	Loose: incremental delivery cost is near zero
Live example	Snowflake 126%, Datadog ~120%, Twilio 106%	Atlassian 120%+ (sold well), Asana 96% (motion stalled)

The matrix is deliberately not a scorecard that sums to a single answer. A product can sit on the usage side of “what value scales with” and the seat side of “forecast predictability needed,” and that tension is exactly what pushes so many durable businesses toward a hybrid. The point of naming it is to force the three questions before the billing system gets built, because the billing system is the last place you want to discover you priced the wrong dimension.

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The NRR arbitrage: why expansion economics diverge

Call it the NRR arbitrage. The same retention percentage can be earned or it can be collected, and those are not equally durable.

In a usage-based model, expansion is collected. The customer scales its own product, consumption rises, the invoice rises. The vendor’s marginal cost to capture that dollar is close to zero, which is why usage-based NRR tends to run high without a proportional jump in sales spend.

In a seat-based model, expansion is earned. Every incremental dollar above flat renewal is a deliberate motion: a new team onboarded, an edition upgraded, a multi-year deal negotiated. That can absolutely work (Atlassian’s 120%+ proves it) but it consumes sales and customer-success effort that the usage meter would otherwise have done for free.

This is why the models diverge on gross margin pressure too. Usage-based revenue is often coupled to a real cost of goods, the cloud compute under the meter, so a chunk of each expansion dollar funds the infrastructure that produced it. The relationship between pricing, cost of goods, and durable margin is its own subject, treated in Why Gross Margin Is Destiny in SaaS. Seat-based revenue, by contrast, is decoupled from incremental delivery cost, which is part of why its gross margin can look cleaner even when its NRR looks weaker.

Methodology: how to read these retention figures

• Inputs: NRR / net expansion rates from each company’s cited filing (Snowflake FY2025 10-K, Datadog Q2 and Q3 2025 10-Q, Twilio Q4 2024 8-K, Atlassian Q2 FY26 shareholder letter, Asana Q2 and Q3 FY26 8-K).
• Assumption: each company computes the metric on a comparable cohort basis (trailing-period existing customers, net of churn and expansion). Definitions vary slightly by issuer, so cross-company comparison is directional, not exact.
• Sensitivity: usage-based NRR is the more volatile input. A consumption-led base can swing several points quarter to quarter on customer workload alone, whereas a contracted seat base moves slowly.
• What this misses: NRR says nothing about new-logo growth, gross retention, or absolute dollar size. A 96% NRR company adding logos fast can out-grow a 126% NRR company that has stopped landing accounts. Retention is one input, not the scoreboard.

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Which pricing model captures more land-and-expand revenue?

Usage-based models capture more land-and-expand revenue passively, because the customer lands at low consumption and grows the bill simply by using the product more. Seat-based models can match the headline rate but only by selling each expansion: Twilio’s 106% net expansion (Q4 2024 8-K) is collected, while Asana’s 96% (Q3 FY26) shows seat expansion that has to be won and currently is not clearing churn.

The deeper distinction is who does the work. In a consumption model the customer’s own growth does the selling, which is why a usage business can post elite expansion with a leaner sales motion. In a seat model, expansion is a recurring operating expense: customer success, upsell reps, renewal negotiations. Both can reach 120%; only one reaches it for free.

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Where usage-based is vulnerable

A credible analysis names the holes. Usage-based pricing has three.

Revenue is volatile by construction. If the bill rises when customers do more, it falls when they do less. A demand shock, a customer cost-cutting cycle, or a single large account optimizing its consumption can move the revenue line in ways no renewal schedule predicts. The same meter that delivers 126% NRR in a good year delivers the disappointment in a bad one.

Forecasting is opaque. Finance cannot model a meter the way it models a contract. Quarterly guidance becomes a probabilistic read on aggregate customer behavior, which is why usage-based companies often carry wider guidance ranges and more guidance risk than their seat-based peers.

NRR decays as the base matures. This is not hypothetical. Per Snowflake’s FY2025 10-K and investor materials, net revenue retention fell from 177% in FY2022 to 126% in FY2025. The product did not get worse. The base got bigger and normalized: once a platform is broadly adopted inside its accounts, the rate of net-new usage expansion per account naturally slows. A still-excellent 126% is the mature shape of what used to be a 177% land grab.

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Where seat-based is vulnerable

Seat-based pricing has the opposite failure mode: it leaves expansion on the table.

Price is decoupled from value. A customer can extract enormous value from a seat-based product, automating work, displacing headcount, running mission-critical workflows, and still pay exactly the contracted per-seat rate. The vendor captures none of that upside automatically. Worse, a product that makes each user more productive can reduce the number of seats a customer needs, putting the pricing model in direct tension with the product’s own promise.

Expansion is a recurring sales cost. Because growth above flat renewal must be sold, seat-based NRR depends on a customer-success and upsell motion that has to be funded and executed every quarter. When that motion stalls, retention drifts toward 100% and below, which is the story Asana’s 96% (Q3 FY26 8-K) is telling. The expansion that usage-based peers collect for free, seat-based vendors have to go win.

The AI era sharpens this. Agentic and AI-assisted software increasingly delivers outcomes rather than occupying a seat at a desk, which is precisely why so many AI products are reaching for consumption or outcome metering instead of per-user licenses, the full re-rating traced in AI is breaking per-seat SaaS pricing. The cost dynamics that push usage-priced infrastructure businesses around are visible in AWS Margin Pressure and the Cloud Reset.

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The bear case: usage-based is not the obvious winner

The strongest counter-argument is that the retention numbers flatter usage-based pricing while hiding its fragility, and that seat-based predictability is worth more than the comparison admits.

Start with the volatility. A 126% NRR earned through a meter is a number that can fall as fast as it rose. In a downturn, consumption-led revenue contracts in real time, with no contracted floor to catch it, while a seat base keeps paying its renewals through the same shock. The market often pays a premium for that predictability, and a CFO who can guide the quarter to the dollar has an advantage a usage business cannot replicate. Predictability is not a consolation prize; for many buyers it is the product.

Then there is the survivorship problem in the data. Snowflake, Datadog, and Twilio are the usage-based companies that worked. The model also produces a long tail of businesses whose customers optimized consumption away, or whose forecasts blew up because a few large accounts changed behavior at once. Reading only the winners makes the meter look like free expansion when it is really a bet that customer workloads keep growing.

Finally, decoupling cuts both ways. Seat-based revenue’s loose coupling to cost of goods, framed above as a weakness on capture, is a strength on margin: a seat business does not hand a slice of every expansion dollar back to a cloud provider. A clean, decoupled gross margin can fund more growth per revenue dollar than a higher-NRR business that is renting its expansion from infrastructure, which is the entire argument in Why Gross Margin Is Destiny in SaaS.

The bear case does not overturn the thesis. It bounds it. Usage-based pricing wins on expansion capture; it does not automatically win on durability, predictability, or margin. The right model is the one that matches the business, not the one with the highest retention slide.

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What operators should take from this

The decision is not ideological. It follows from what your product actually scales with, and from how much forecast risk the business can carry. Here is the concrete playbook.

1. Run the Pricing Decision Matrix before you build billing. Answer the three axes honestly: what value scales with, how much predictability you need, and whether expansion can be collected or must be sold. Price the dimension the customer’s success actually moves.
2. If value tracks consumption, meter it and budget for volatility. Accept harder forecasting in exchange for expansion you don’t sell. Hold extra cash and guide in ranges, because a consumption base swings on customer behavior you don’t control.
3. If value is per-person, seat-price it and fund the expansion motion. Seats only reach elite retention when a deliberate upsell and customer-success engine is staffed and measured. Atlassian’s 120%+ is sold, not collected; budget for the sellers.
4. Hybridize to fix the mispricing the matrix exposes. A committed seat or platform floor for the forecast, a meter on top for the upside. Prepaid credits drawn down by consumption smooth volatility into something finance can model while preserving consumption upside.
5. Watch the margin coupling, not just the NRR. If your meter rides on cloud compute, every expansion dollar funds the infrastructure under it. Track gross margin and NRR together; a high-NRR business renting its expansion can be worth less than a lower-NRR one that keeps its margin.
6. Read your own retention by composition. Separate collected expansion from sold expansion in your reporting. The two are not equally durable, and a board deck that blends them hides exactly the risk you most need to see.

Here is the mechanism at founder scale, as an illustrative, hypothetical example (numbers invented to show the logic, not drawn from any company). Suppose a product charges 25 dollars per seat and a customer buys 40 seats: 1,000 dollars a month, flat until they hire. The same product metered at, say, a fraction of a cent per processed item, against a customer running 3 million items a month, grows its bill every time that customer’s own volume grows, with no new order form. Same product, same value delivered, two completely different revenue curves. The only variable that changed was what the price was attached to.

That is the decision. Not how to bill. What to bill for.

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How the pieces fit together

Usage-based and seat-based pricing are not better and worse. They are bets on different sources of growth.

1. Usage-based bets that expansion you collect automatically beats the volatility it imports. The payoff shows up as elite NRR (Snowflake 126%, Datadog 120%, Twilio 106%) and the cost shows up as forecasting risk and NRR decay at maturity.
2. Seat-based bets that a forecastable, contracted revenue line is worth capping organic expansion. The payoff is predictability; the cost is that every dollar above flat renewal has to be sold, and when that motion slows, NRR drifts below 100% (Asana 96%).
3. The headline retention number can match across models while the engine differs entirely (Datadog and Atlassian both near 120% on opposite mechanics), so read composition, not just magnitude.
4. The strongest position is usually a hybrid: a committed base for the forecast, a meter on top for the upside.

The framing carries across the portfolio. The same surface-versus-payload logic that governs platform strategy in Google’s AI Strategy Is a Distribution War shows up here as model-versus-meter: the pricing surface, not the feature, decides who captures the value the product creates.

That’s the whole tradeoff. The rest is instrumentation and nerve.

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Analysis, not investment advice. Retention figures are drawn from the cited public filings and shareholder letters (Snowflake, Datadog, Twilio, Atlassian, Asana) and tied to their fiscal periods inline. Frameworks here, including the Pricing Decision Matrix, are for understanding pricing strategy and tradeoffs, not for making buy or sell decisions.

Want the full toolkit for reading filings like this, the NRR-decomposition worksheet, the Pricing Decision Matrix, and the unit-economics scorecard used above? It’s in the Tech Business Analysis Playbook.