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The most reliable comparison uses the same fiscal period, the same revenue scope, and each company’s own metric definitions. Read revenue growth to measure expansion, gross margin to test the economics of delivering the software, and customer or net revenue retention to see whether the existing customer base stays and grows. No single percentage is decisive.
Start with a like-for-like data sheet, then explain what caused each result. A fast-growing company can have weakening delivery economics if inference, hosting, or cloud costs rise. A company with retention above 100% can still be risky if one or two large contracts dominate the cohort. The framework below shows how to make those distinctions.
1. Establish a genuinely comparable starting point
Before calculating anything, define exactly what is being compared. AI software companies may report consolidated results, an AI product line, a cloud segment, annual recurring revenue, usage revenue, or a mixture. Putting unlike figures in one table creates false precision.
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Use the same reporting interval wherever possible: for example, fiscal year 2025 against fiscal year 2024 for every company, or the same quarter against the prior-year quarter. Record the fiscal year-end date because companies do not all close their books on the same day. Do not compare one company’s latest quarter with another company’s full-year result.
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Match the revenue scope
Label every number as company-wide, segment-level, product-level, or subscription-only. Microsoft’s results illustrate the issue: its consolidated business includes many technology products, while Microsoft Cloud is a more focused but still broad segment. A cloud-segment growth rate is not a pure-play AI-software growth rate.
Separate recognized revenue from ARR
Revenue in the income statement is recognized under accounting rules. ARR is a run-rate measure based on contracts or subscriptions. They answer different questions and should not share a comparison column. If a company reports usage-based revenue, ARR may be especially unstable because future usage is not guaranteed.
Record the definition beside the percentage
For each retention figure, capture the cohort, measurement window, included revenue, and treatment of price changes, usage, migrations, added licenses, downgrades, and departed customers. A percentage without its formula is not comparable evidence.
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Revenue growth measures how quickly the reported top line is expanding:
Year-over-year growth = (current-period revenue − prior-period revenue) ÷ prior-period revenue × 100
Use reported revenue from matching periods and state whether the result is organic, acquisition-inclusive, currency-adjusted, or unadjusted when the company provides that detail. The percentage is only the beginning; the important question is why revenue changed.
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Break growth into operating drivers
- Usage: Customers may run more workloads, generate more transactions, or consume more inference capacity.
- Pricing: List-price increases, consumption-rate changes, or new packaging can raise revenue without adding customers.
- New customers: New logos expand the starting base.
- Expansion: Existing customers may buy more seats, products, capacity, or higher service tiers.
- Acquisitions: Acquired revenue can accelerate the reported figure but is not the same as organic demand.
Ask management or the filing to distinguish these drivers. A company growing through broad-based customer additions has a different risk profile from one growing because a few customers increased usage.
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| Company and scope | Period | Reported revenue | Reported growth | How to use the figure |
|---|---|---|---|---|
| Microsoft, consolidated | FY2025 | $281.724 billion | 15% year over year | Diversified technology-company result, not an AI-only benchmark. |
| Microsoft Cloud | FY2025 | $168.9 billion | 23% year over year | More focused cloud scope, but still includes multiple cloud businesses. |
| Freshworks, consolidated | 2025 | $838.8 million | 16% versus 2024 | Software-company example rather than an AI-only result. |
| Freshworks, consolidated | 2024 | $720.4 million | 21% versus 2023 | Shows deceleration from the prior reported year. |
| Freshworks, consolidated | 2023 | $596.4 million | Not stated for this comparison | Use as the prior-year base for the 2024 growth rate. |
Microsoft’s 15% consolidated growth and 23% Microsoft Cloud growth cannot be ranked directly against Freshworks’s 16% growth without acknowledging the differences in scale, scope, and business mix. The figures are useful examples of why the comparison unit must be visible.
3. Use gross margin to test delivery economics
Gross margin shows how much revenue remains after the direct cost of delivering the product:
Gross margin = (revenue − cost of revenue) ÷ revenue × 100
Gross profit is the numerator before operating expenses such as sales, research, and administration. For software, cost of revenue can include cloud hosting, inference, data-center capacity, third-party infrastructure, support, implementation, personnel, and amortization of acquired technology, depending on the company’s accounting presentation.
Read the level and the direction
A high margin is not automatically durable. Compare the current percentage with prior periods and identify what changed in cost of revenue. A falling margin during rapid growth may indicate that each additional dollar of revenue requires disproportionately more compute or service capacity. A stable or rising margin can indicate better utilization, pricing, model efficiency, or a shift toward products with lower delivery costs.
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Microsoft example: growth alongside infrastructure pressure
Microsoft reported FY2025 revenue of $281.724 billion and gross margin of $193.893 billion. Those figures imply an approximate consolidated gross margin of 68.8% when gross margin dollars are divided by revenue. Microsoft Cloud’s gross margin percentage was 69%. The annual report attributed pressure on the cloud margin percentage to scaling AI infrastructure, partly offset by Azure efficiency gains. This is a useful pattern to watch: strong demand can coexist with temporarily heavier delivery costs.
Microsoft is diversified, so its consolidated margin should not be treated as the expected margin for an AI-first software vendor. Use the example to connect growth, infrastructure investment, and unit economics rather than to set a universal target.
Questions to ask about cost of revenue
- Are cloud, inference, hosting, or data-transfer costs rising faster than revenue?
- Does management identify one-time capacity purchases separately from recurring delivery costs?
- Are customer support and implementation costs included in cost of revenue or operating expenses?
- Did a change in product mix, pricing, or accounting presentation affect the percentage?
- Is an apparent improvement coming from efficiency, or simply from a lower-cost legacy product growing faster?
4. Distinguish customer retention from net revenue retention
Customer or logo retention
Customer retention counts how many customers remain from a starting cohort after a specified period. It answers a logo question: did the account stay? It does not show whether the retained account bought more or less.
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Always identify the cohort and window. A twelve-month logo-retention figure for customers above a spending threshold is not comparable with a quarterly figure covering every paying account.
Net revenue retention
Net revenue retention, often called NRR or net dollar-based retention, follows the revenue from an opening customer cohort:
NRR = (opening cohort revenue − churn − contraction + expansion) ÷ opening cohort revenue × 100
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Expansion can include additional licenses, migrations, higher usage, contractual price changes, or usage-based price changes. NRR can exceed 100% when expansion more than offsets lost and contracted revenue. It can also fall below 100% even when the company adds many new customers, because new logos are outside the opening cohort.
Vertex: a company-specific definition
Vertex defines net revenue retention as the twelve-month ARR expansion for customers present at the beginning of the period. Its definition includes revenue lost from departed customers, downgrades, and reduced usage, as well as expansion from migrations, additional licenses, and contractual or usage-based price changes. Vertex reported 105% for 2025, compared with 109% in 2024.
The movement from 109% to 105% indicates weaker expansion within the measured cohort, but it does not by itself identify whether churn, downgrades, lower usage, or slower expansion caused the change. Because the formula includes several components, look for the company’s accompanying explanation.
Why percentages cannot be compared blindly
Two companies can both report 105% and measure different economic events. One may include price increases and usage; another may exclude them. One may use beginning-of-year ARR; another may use recognized revenue. One may cover all customers; another may exclude small accounts. Put the formula and cohort beside every percentage before ranking companies.
5. Test concentration and contract quality
Retention metrics become fragile when a small number of contracts represent a large share of the cohort. C3.ai warns that ARR and net dollar-based retention may not accurately indicate future results for its business because dependence on a small number of high-value contracts means gaining or losing one contract can cause substantial volatility.
Check customer concentration, contract duration, renewal dates, termination rights, minimum commitments, and the share of revenue that is usage-based. A high NRR supported by one expanding customer is less diversified than the same NRR spread across hundreds of accounts. Conversely, a lower percentage may reflect a healthy broad base if no single account can move the result materially.
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ARR caveat for monthly subscribers
Freshworks notes that ARR assumptions for monthly subscribers may not accurately reflect the revenue ultimately received over the following twelve months. This matters when comparing a contract-based vendor with a monthly or usage-oriented vendor: an ARR snapshot may overstate future revenue if customers reduce usage or leave before the next twelve months are complete.
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Use one row per company and keep the evidence and qualification in the same row. The following fields prevent the most common category errors.
| Axis | Record | Diagnostic questions |
|---|---|---|
| Revenue growth | Reported revenue and year-over-year change for the same fiscal period | Is the scope company-wide, a segment, or a product? How much came from usage, pricing, acquisitions, new customers, or expansion? |
| Gross margin | Gross profit divided by revenue, plus the multi-period trend | Are cloud, inference, hosting, support, or other delivery costs rising? Does management cite AI infrastructure or efficiency? |
| Customer retention | Logo retention, if disclosed | Does it count customers or dollars? What cohort, threshold, and time window are used? |
| Net revenue retention | Revenue from the opening cohort after churn, contraction, and expansion | Are usage, price changes, migrations, and added licenses included? |
| Quality and limits | Concentration, contract mix, definitions, and reporting period | Could one large contract move the percentage? Are ARR assumptions sensible for monthly or usage-based revenue? |
Do not reduce the scorecard to a single composite number unless you can justify the weights. A transparent narrative that explains trade-offs is usually more informative than an arbitrary ranking.
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7. A repeatable step-by-step comparison process
- Choose the comparison date. Select one fiscal year or quarter and write the exact period next to every value.
- Define the scope. Decide whether the comparison covers the whole company, an AI segment, or a specific product. Do not mix scopes in the same ranking.
- Collect reported revenue. Record current and prior-period revenue, currency, acquisitions, and any organic-growth disclosure.
- Calculate growth. Apply the year-over-year formula and preserve the company’s stated rounding.
- Collect gross profit and cost-of-revenue detail. Calculate gross margin and annotate infrastructure, hosting, support, or mix effects.
- Capture retention definitions. Copy the cohort, time window, denominator, and included expansion or contraction events into your notes.
- Separate logos from dollars. Place customer retention and NRR in different columns; never substitute one for the other.
- Check concentration. Review major-customer disclosures and contract structure before treating NRR as a stable forward indicator.
- Explain the combination. Describe whether growth is broad or concentrated and whether margin direction supports the delivery model.
- Write the limits. State when the company is diversified, the metric is company-defined, or the period and scope prevent a direct comparison.
8. Interpret common combinations of results
| Pattern | What it may indicate | What to verify |
|---|---|---|
| High growth, falling gross margin | Demand is strong but delivery costs, especially infrastructure, are scaling faster than revenue. | Compute commitments, inference efficiency, pricing, product mix, and whether management expects the pressure to persist. |
| High growth, rising gross margin | Expansion is occurring with improving delivery economics. | Whether the improvement is broad-based or caused by a temporary mix or accounting effect. |
| Low growth, NRR above 100% | Existing customers are expanding, but new-logo acquisition may be weak. | Customer additions, sales capacity, market saturation, and concentration of expansion. |
| High logo retention, NRR below 100% | Customers remain but spend less through downgrades, reduced usage, or lower prices. | Contraction by segment, usage trends, discounting, and product migrations. |
| High NRR, volatile reported results | Expansion may be concentrated in a few large contracts or usage can fluctuate sharply. | Customer concentration, contract minimums, renewal timing, and the company’s own warning about metric reliability. |
| Strong consolidated growth, unclear AI economics | A diversified company is benefiting from several businesses, so AI-specific performance is not isolated. | Segment disclosure, cloud margin, AI-related infrastructure costs, and product-level demand. |
9. Use industry statistics as context, not as a target
Software Equity Group’s 2025 Annual SaaS Report found that 63% of companies in its selected index that reported retention had net retention above 100% in 4Q24. In the same selected-index data, companies with less than 100% net retention had a 4.1x median enterprise-value-to-trailing-twelve-month-revenue multiple, compared with a 5.6x index median. The report also said that 8.2% of the index had net retention above 120%, with a median 11.7x multiple.
These are sample-specific associations based on selected 4Q24 data. They do not prove that retention alone causes a valuation multiple, and they are not guaranteed benchmarks for an AI company. Differences in growth, margins, size, market conditions, customer mix, and reporting definitions can influence the observed relationship.
High Alpha’s 2025 SaaS Benchmarks Report compares gross and net revenue retention for companies where AI is core and companies where AI is a supporting feature. Numeric values and methodology for that comparison are not established here, so do not use an uncited AI-specific benchmark number as a standard.
10. Common comparison mistakes and their fixes
- Mixing fiscal periods: Rebuild the table around one period, even if that means leaving a company out.
- Mixing scopes: Label consolidated, segment, and product results; do not rank them together.
- Calling NRR customer retention: Keep logo counts and revenue dollars separate.
- Comparing percentages without formulas: Put the definition, cohort, and window beside the metric.
- Treating ARR as recognized revenue: Show ARR in a separate section and test whether the business is contractual, monthly, or usage-based.
- Ignoring delivery costs: Read gross-margin direction and cost-of-revenue commentary alongside growth.
- Overlooking concentration: Test how much one large customer could move ARR or NRR.
- Using a benchmark as a verdict: Treat industry statistics as context from a defined sample, not as a pass-or-fail rule.
- Assuming diversified results represent AI software: Identify which businesses are included before drawing an AI-specific conclusion.
11. What a defensible conclusion looks like
A useful conclusion names the trade-off rather than declaring a universal winner. For example: a company may lead on reported growth, while another has steadier gross margins and less concentrated retention. The first may be investing heavily in AI infrastructure that depresses near-term margins; the second may have slower expansion but more predictable economics. The evidence supports a comparison of risk and operating quality, not a one-number ranking.
Before making a judgment, confirm that every headline figure has a matching period, scope, formula, cohort, and concentration note. Then explain the drivers behind the numbers. That process turns revenue growth, margins, and retention from isolated statistics into a coherent view of how an AI software business acquires customers, monetizes usage, and delivers the product.
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