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SaaS Metrics School

Ben Murray

Ben Murray brings you actionable SaaS metrics lessons that he has learned through years of being in the SaaS CFO trenches. Whether you are new to SaaS or a SaaS veteran, learn the latest SaaS and AI metrics, finance, and accounting tactics that drive financial transparency and improved decision-making.

Ben’s SaaS metrics blog consistently rates a 70+ NPS, and his templates have been downloaded over 100,000 times. There is always something to learn about SaaS and AI metrics.

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Saturday · 4 min
What Public SaaS Comps Reveal About Earning a Premium Valuation Multiple
Only 9% of public software companies trade above 10x revenue — do you know which metrics separate them from the 68% stuck below 5x? In episode #389, Ben Murray breaks down what public comps reveal about how premium SaaS valuations are actually created. You can't value a private SaaS business straight off public multiples, but the correlations between metrics and enterprise value tell you exactly what buyers reward — and if you're a founder or CFO eyeing an exit, these are the numbers that will decide whether you land the index median or the premium multiple. The real distribution of public software multiples — 68% below 5x revenue, 23% at 5–10x, and just 9% above 10x — and what it takes to reach the cream of the crop. How net revenue retention maps to valuation: NRR below 100% earns a 3.1x EV-to-revenue multiple, the index median sits at 5.7x, and companies above 120% NRR command 9.3x. Why retention sits at the top of the valuation pyramid and drives premium multiples in bear markets and bull markets alike. Why two companies hitting the same Rule of 40 get different valuations — and why 30% growth + 10% EBITDA beats 30% EBITDA + 10% growth. How to pressure-test your own exit readiness: if you can't confidently enter your metrics into a valuation calculator, your finance data foundation isn't ready for due diligence. Tune in to see where your SaaS would land on the valuation curve — before a buyer runs the numbers for you. Resources Mentioned Ben's SaaS Exit Readiness blog post (four pillars + valuation framework): Contact Ben for help on preparing for an exit: https://www.thesaascfo.com/contact/
Thursday · 5 min
Founders, Are You Exit Ready?
Founders, if a buyer asked for your metrics tomorrow, could your data prove your financial performance or would holes in it kill the deal? In episode #388, Ben Murray breaks down the metrics acquirers use to underwrite your SaaS business — and why so many exits fail before they start. Due diligence is about de-risking the acquisition, and buyers with deep bench strength will pour through your data looking for weaknesses. If you can't control the financial narrative with accurate, defensible metrics, they'll write it for you — and your valuation will pay the price. The four metric pillars buyers scrutinize in due diligence — retention quality, growth, capital efficiency, and margin architecture — and why retention is still king. The GRR threshold many private equity firms treat as a hard floor (hint: it's 90%) and why the gap between GRR and NRR matters for every recurring revenue stream. Why two companies with the same Rule of 40 score get very different valuations — and which composition earns the premium. The capital efficiency metrics on the exam — burn multiple, CAC payback, ARR per FTE — and how go-to-market efficiency changes how much capital a buyer must invest post-acquisition. Why AI-native margins are rotating back to 70–80% software expectations, and how a correctly structured SaaS P&L lets you explain your margin by revenue stream. The minimum runway Ben recommends for exit prep — 6 months of data foundation work — so you hand buyers numbers you're confident in. Tune in before your next investor conversation — because if you're eyeing an exit in the next year or two, the data prep starts now. Resources Mentioned Ben's SaaS Exit Readiness blog post: https://www.thesaascfo.com/saas-exit-readiness-score/ Contact Ben for exit-ready services: https://www.thesaascfo.com/contact/
September 16 · 3 min
Your Dev Team's AI Bills Are Hitting the Wrong Line on Your P&L
Is your dev team's AI spend buried in the wrong part of your P&L — quietly distorting your gross margin and making your AI ROI impossible to measure? In episode #387, Ben Murray tackles one of the most pressing accounting questions hitting SaaS CFOs and controllers right now: how to properly classify AI development costs across your chart of accounts. With dev teams racking up daily charges from multiple AI providers, getting these expense codes wrong doesn't just create accounting noise — it misrepresents your gross margin, obscures your true AI economics, and leaves you unable to answer board questions about AI ROI. The right GL structure makes all the difference. The single most important question to answer first: is the AI cost production-facing (customer-serving inference) or internal engineering use — because the answer determines whether it hits COGS or OpEx, and your gross margin depends on getting this right. A full breakdown of the five AI cost categories SaaS companies are facing today — production inference, dev/testing inference, model training and fine-tuning, GPU compute for production serving, and vector database infrastructure — and exactly where each one belongs on the P&L. Why model training and fine-tuning costs may need to be capitalized rather than expensed, just like other qualifying software development costs under existing accounting guidance. Why internal AI tool spend (Copilot, ChatGPT, Claude, etc.) needs its own dedicated GL account — separate from internal-use software — so you can actually measure efficiency gains and answer the AI ROI question when the board asks. Why this isn't optional housekeeping: as AI costs grow, the difference between properly coded and miscoded AI expenses will show up directly in your gross margin and in how investors read your unit economics. Tune in to get the GL coding framework SaaS CFOs are building now — before AI expenses get too big to untangle. Resources Mentioned Ben's AI COGS blog post (detailed GL coding guidance): https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/
August 27 · 5 min
Why AI-Powered SaaS Dashboards Are Making ERP Reporting Obsolete
Is your ERP dashboard actually built on data that matters — or is it just a chart of accounts dressed up to look useful? In episode #386, Ben Murray breaks down why traditional ERP dashboards are losing ground to AI-generated, prompt-built SaaS reporting and what that means for CFOs and finance leaders right now. If your team is still relying on static dashboards anchored to your general ledger, you're missing three out of four key SaaS data sources before you even start the analysis. The gap between what ERP dashboards can show and what modern AI-native metrics engines can produce is widening fast and the CFOs who close that gap first will be the ones driving the board conversations. Why ERP dashboards are fundamentally limited to chart-of-accounts data — and the three additional SaaS data sources (HRIS, bookings, and customer/revenue data) that actually drive metrics like CAC payback, LTV to CAC, NRR, and Rule of 40. How Ben vibe-coded a full SaaS metrics dashboard in minutes using Claude — covering ARR trajectory, EBITDA margin, gross margin, revenue per FTE, and cash balance — and why a prompt-built report on a deterministic engine beats any canned dashboard. Why controlling the period of measurement matters: the example of setting CAC payback on a six-month sales cycle basis — something a standard ERP dashboard simply can't do. Where AI actually belongs in the FP&A process: not at the beginning, but at the end — writing board narratives, flagging dormant customers ripe for expansion via a RevIntel engine, and generating insights that traditional FP&A could never surface. Why agent-friendly APIs matter: how Saster's API grading tool surfaces whether your SaaS stack is actually exposing the data AI needs to take action — not just technically having an API. Tune in to understand exactly where your ERP dashboard ends and where a closed-loop, AI-powered metrics engine takes over — before your next board meeting. Resources Mentioned Ben's LinkedIn post (vibe-coded SaaS metrics report): https://www.linkedin.com/posts/benrmurray_saas-activity-7498039803582689280-7Ckt?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAOOEO8Bf5aRLyU0jjrGXvPD2odJNDer6KU Ben's deterministic SaaS metrics engine: https://softwaremetrics.ai Ben's Five Pillar SaaS Metrics Framework: https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026
August 22 · 5 min
How AI Is Writing 84-Data-Point Board Reports — And Why CFOs Can Trust Them
What if AI could produce a better board memo than any CFO — and you could trust every data point in it? In episode #385, Ben Murray breaks down how he is using AI to generate complete 5-page financial board reports, and more importantly, why the outputs are reliable. This isn't AI hype — it's a working FP&A process Ben is running today inside his fractional CFO practice. If you're still manually assembling dashboards, PDFs, and narrative summaries before every board meeting, this episode reframes what's actually possible right now. Why a deterministic metrics engine — not AI — is the non-negotiable foundation that makes AI-written board reports trustworthy (and how Ben built one backed by 35 pages of documentation) The exact structure of a SaaS financial board report: executive overview, metrics vs. benchmarks, ARR growth, customer retention, GTM efficiency, financial capacity, data anomalies, and top priorities — all generated by AI How Ben identified 84 unique data points in a single AI-written board memo using ChatGPT — and why that number changes how you think about QA The MCP connection to SoftwareMetrics.ai that makes this a repeatable monthly process — and how Replit is now building it as a native app feature What's coming in October: Ben's SaaS Metric Sprint, a live walkthrough of building the data foundation, calculating metrics, and connecting MCP so you can run this for your own company Tune in to see how Ben is turning month-end close into a board-ready narrative in minutes — and how you can build the same process for your SaaS company. Resources Mentioned Ben's app: https://softwaremetrics.ai/ SaaS Metric Sprint (October 6th): https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026
August 19 · 6 min
Should AI Run Your Board Meetings? A CFO's Framework for AI-Prepped Board Packages
Is your board meeting time being wasted on reporting instead of judgment and quality discussion? In episode #384, Ben Murray addresses how AI can transform board meeting prep for SaaS finance leaders. If your board decks are packed with data but the important issues still get buried, and board members show up with wildly different levels of prep, you already know the problem. Every hour spent reviewing numbers that should have been read beforehand is an hour not spent on the judgment calls that actually move the business forward. Understand the core frustration behind Jason Lemkin's "AI board member" post and why it resonates with CFOs building out their FP&A process See how AI can do a first analytical pass through your financials, metrics, and benchmarks before the meeting ever starts Learn how to use AI to set a focused board agenda: the 3 issues that matter, the 5 metrics that don't need discussion, and the questions still unanswered Get Ben's real-world example of using an AI agent to identify top 3 board priorities and pull follow-up ownership from meeting transcripts Discover why a deterministic data engine, not just a chatbot, is the real key to accurate AI-written financial narratives and agendas Tune in to see exactly how Ben is putting Jason Lemkin's AI board member idea into practice, before your next board meeting rolls around. Resources Mentioned Jason Lemkin's SaaStr post on AI and board meetings: https://www.saastr.com/the-ai-board-member-why-yours-should-chair-the-next-meeting-for-real/? What I'm using: https://softwaremetrics.ai/ CFO courses: https://www.thesaasacademy.com/
August 8 · 5 min
How to Vibe Code Finance Dashboards for Your SaaS Metrics
Can you actually trust the numbers when AI writes your board report? In episode #383, Ben Murray breaks down how to vibe code finance dashboards that hold up to CFO standards. Every finance leader is being sold the same promise: that AI will do your analysis for you, but the hype skips the part that decides whether the output is usable. If you are putting AI-written numbers in front of your board or investors, the difference between a trusted report and an embarrassing one comes down to work most CFOs never do. Why the data foundation, not a magic prompt, decides whether your AI dashboards can be trusted How a deterministic metrics engine keeps AI away from your calculations while it writes the narrative on top The Cisco playbook for letting AI draft 80 to 90 percent of your board commentary before you finish it off How to turn an LLM-generated HTML dashboard into a live, refreshable report in about 5 minutes Which model, ChatGPT, Claude, or Gemini, actually produces the best-looking finance dashboards Tune in to get the exact process CFOs are using to put AI-written reports in front of their boards with the numbers they can defend. Resources Mentioned Webinar: How I Vibe Code Finance Dashboards, plus templates: https://www.thesaasacademy.com/pl/2148817040 SaaS Metrics Sprint, October cohort: https://www.thesaasacademy.com/saas-metrics-implementation-sprint-sept-2026 Tech CFO community: https://docs.google.com/forms/d/e/1FAIpQLSfP4uwvwEoc92Qc_rS8eu-9EzV6shivPbBhaNcPqsy5sNVNNg/viewform?usp=dialog
August 7 · 4 min
The 2026 ARR per Employee Benchmarks: Where Top-Quartile SaaS Actually Lands
Seeing the millions-per-employee AI headlines and wondering where your SaaS company actually stands? In episode #382, Ben Murray covers the latest ARR per FTE benchmarks from Ray Rike's Benchmarkit data. Social media is full of ARR per employee hype, but almost none of it tells you how the number was defined, whether contractors are counted, or how your company compares once you cut the data the way it actually matters. If you are benchmarking efficiency for a board deck, a raise, or a headcount plan, the aggregate number can quietly send you the wrong signal. This episode grounds the metric in real survey data so you know what good looks like for a company your size, in your region, with your pricing model. Know the headline numbers: bottom quartile at 127K, median at 193K, and top quartile at 279K of ARR per employee across the full SaaS population. See how pricing model changes everything, from usage-based leading at 291K down to subscription plus usage hybrids at 136K. Understand why efficiency can drop in the 50 to 100 million ARR band instead of climbing, and what that says about your next phase of growth. Compare the cuts that actually move the number: North America versus EMEA, and horizontal B2B versus vertical SaaS. Learn why aggregate benchmarks can be dangerous to your SaaS health, and why size and pricing bands beat the total-population average every time. Tune in to see where your ARR per employee really stands before you use it in your next board deck or fundraise. Resources Mentioned Benchmarkit (Ray Rike) - benchmarkit.ai Ben Murray's blog post with the full data cuts: https://www.thesaascfo.com/arr-per-employee-benchmarks/
July 22 · 3 min
How Much Are Tech CFOs Actually Making in 2026?
In episode #381, Ben Murray covers the latest 2026 tech CFO compensation benchmarks across base, bonus, equity, and severance. If you set finance comp or negotiate your own, guessing at the market rate is expensive in both directions. Underpay and you risk losing your best finance leader. Overpay and you burn cash you cannot spare. This episode gives you the median numbers and the revenue tier splits that decide what competitive actually looks like. Why the median CFO package of $285K base, $100K target bonus, and $1M equity is only a starting point, and why company revenue size changes the whole picture How median base pay climbs across revenue tiers, from roughly $240K under $10M up to $375K at $100M to $250M, a premium of about 50 percent The gap between target and realized bonus, with CFOs hitting about 87 percent attainment while VPs of Finance and FP&A land closer to 50 percent Why equity is where packages really split, at a $1M median for CFOs versus $200K at the VP of Finance level, a 5x difference Where severance protection shows up, at the CFO and VP of Finance level, and where it disappears at the director level Tune in, then grab the full report and interactive benchmarks from the show notes before your next comp conversation or board meeting. Resources Mentioned Blog post and report: 2026 CFO compensation summary: https://www.thesaascfo.com/cfo-and-vp-finance-compensation-base-bonus-and-equity-benchmarks/ Full 2026 CFO compensation report: https://www.benchmarkit.ai/2026-finance-executive-compensation
July 2 · 5 min
The Latest GRR Benchmarks
Is gross revenue retention under attack at your SaaS company? The latest benchmark data says the ground has shifted under everyone. In episode #380, Ben Murray breaks down the latest SaaS gross revenue retention benchmarks from Ray Rike's Benchmarkit report, the same data set Ben uses to benchmark his own client base. GRR is one of the power three metrics, and it is hard to scale without it. Pricing models are changing; seat-based pricing is under pressure, and AI is reshaping how revenue holds. If your board still treats 88% median GRR as the baseline, you are benchmarking against last year's reality. Why median GRR fell from 88% to 84% year over year, and why the top quartile slipped from 95% to 91% Whether the 95% GRR "elite" rule of thumb still holds, backed by three years of top-quartile benchmark data Which pricing model retains revenue best, comparing pure subscription against usage-based and subscription-plus-usage Why vertical SaaS is outperforming horizontal SaaS on retention, with a 90% median GRR versus 84% How to benchmark GRR the right way by ACV segment instead of relying on dangerous aggregate numbers Tune in to see where your gross revenue retention really stands, before your next board meeting or investor update. Resources Mentioned Benchmarkit SaaS Metrics Benchmark Report, Ray Rike: https://www.benchmarkit.ai/2026-saas-ai-native-metrics Ben's KPI app: https://softwaremetrics.ai/ Ben's blog post on 2026 GRR benchmarks: https://www.thesaascfo.com/saas-grr-benchmark-2026/
June 24 · 4 min
Why AI ARR Alone No Longer Lifts Your Software Valuation
AI ARR is easy to announce. Proving it is where most SaaS finance teams are about to get exposed. In episode #379, Ben Murray tackles the new bar for AI financial transparency and what it means for your next budget season. The public markets have already moved the goalposts. Launching AI was the 2024 story. Reporting AI ARR was the 2025 story. Now investors and boards want to see AI margins, customer outcomes, and proof that AI revenue is actually dropping to the bottom line. That same pressure is heading straight for private SaaS, and your board will bring it to budget season whether you are ready or not. Understand why AI ARR by itself no longer satisfies boards or investors, and what they now demand to see in the numbers. Separate pure AI revenue, AI-influenced revenue, and AI upsell so your reporting survives scrutiny, using clean SKUs, product IDs, and chart of accounts. Know which AI costs belong in COGS, including inference, infrastructure, and observability, so you can show your real AI margins. Walk into budget season ready for the board questions on AI revenue, AI cost, and margin by revenue stream. Instrument heavy, medium, and light AI users so you can defend margins and LTV to CAC as usage scales. Listen now and build the AI transparency your board will expect before budget season starts. Resources Mentioned Ben's blog posts on capturing AI costs in COGS: inference, infrastructure, and observability: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/ Ben's training on AI metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas
June 23 · 4 min
Here's What Separates the 9 Public SaaS Companies that Trade Above 10x
Is your SaaS company stuck in the valuation doghouse while a handful of names trade at a massive premium? In episode #378, Ben Murray breaks down Meritech's June 2026 public software comps report and the widening valuation gap across SaaS. The median revenue multiple has fallen 64% from its pre-ZIRP peak, and most public software now trades below 5X. If you are a SaaS founder or CFO, the multiple attached to your business depends on a short list of traits the market now rewards. This episode shows you which ones, and why the rules quietly changed. Why only 9 of roughly 100 public software companies trade above a 10X revenue multiple, while 77 sit below 5X How the Rule of 40 shifted under the surface, with revenue growth now 3.3x more correlated with the multiple than free cash flow margin Why two companies with the same Rule of 40 score can trade at 7.3x versus 3.7x, depending entirely on how they got there What the top 9 share in common: free cash flow margins above 20% and ARR growth above 20% at the same time How AI exposure now sorts the market, and why a weak AI ARR story lands horizontal SaaS in the doghouse Tune in to see exactly what separates the premium names from the rest before you benchmark your own SaaS valuation. Resources Mentioned Meritech June 2026 Public Software Comps (Pulse Report): https://meritech.substack.com/p/meritech-software-pulse-12-june-2026 Ben's academy: https://www.thesaasacademy.com/
June 12 · 6 min
12 Steps to Creating an Outcome-based Pricing Plan
Everyone says seat-based pricing is dead, but do you actually have an outcome you can charge for? In episode #377, Ben Murray breaks down the 12 steps to building an outcome-based pricing plan, drawn from analyzing real, live outcome-based pricing pages and the fine print buried in their terms and conditions. Outcome pricing is complex to design and even harder for customers to understand: when are they charged, and where is the failure point at which they aren't? For SaaS founders and CFOs weighing a move to outcome- or agentic-AI pricing, getting the unit, success criteria, and spend controls right is the difference between a model customers trust and one that creates budget anxiety and billing disputes. How to decide whether you even have a billable outcome, and why a completed customer result is not the same as an activity. How to define the outcome unit and write success criteria twice, with real examples from Intercom's Fin, Help Scout's AI Answers, and Zendesk's 72-hour resolution window. Why failure forgiveness is a conversion tool, not just billing logic, and how measurement windows protect you from outcomes that unravel later. How to choose your commercial structure, anchor price to labor savings, revenue, or risk avoidance, and plan for the training lag before charges begin. Why spend controls and auditable billing events are non-negotiable, and how to know when outcome pricing is the wrong model entirely. Tune in for the full framework, then grab the deep-dive blog post before you design your next AI pricing plan. Resources Mentioned Ben's blog post: 12 Steps to Creating an Outcome-Based Pricing Plan: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/
June 10 · 6 min
5 Takeaways for CFOs from the 2026 AI Pricing Report
Is your 2027 software budget ready for the AI spend that's about to blow past every forecast you've built? In episode #376, Ben Murray covers five takeaways for CFOs from the Pricing I/O AI Pricing Report, produced in partnership with Benchmarkit, which surveyed 296 software buyers in Q1 2026. With budget season around the corner and demand for tokens, agentic AI, and tools like ChatGPT and Claude climbing fast, the gap between what buyers want and where AI pricing is heading has never mattered more. If you own a software budget or sell AI software, these findings reshape how you should think about predictability, governance, and the guardrails buyers are actually asking for. Why buyers rank predictable total cost as a top-3 priority, far above low entry price, and why the seat-based pricing obituary may be premature for enterprise deals. What the 89% budget-overrun rate really signals: a forecasting problem on the buy side, not vendors changing the rules after signing. Why credit and token pricing is the single hardest model to evaluate, and what Salesforce's new agentic work units mean for your bill. The surprising finding that IT, not Finance, owns AI cost risk, and why department-level allocation of token spend is the fix. Why buyers want soft caps, alerts, and approval steps over hard cutoffs, and where hard caps get genuinely painful in outcome-based pricing. Tune in to get the buyer-side data shaping AI pricing before you lock in your 2027 budget. Resources Mentioned Pricing I/O AI Pricing Report: https://www.benchmarkit.ai/widget/ai-pricing/cy-26?utm_source=TheSaaSCFO&utm_medium=Podcast&utm_campaign=TheSaaSCFO Ben's blog post: 12 Steps to Creating Your Outcome-Based Pricing: https://www.thesaascfo.com/how-to-build-outcome-based-pricing/
June 2 · 5 min
Your AI Subscription Pricing Is Losing Money on the Customers You Care About Most
Do you actually know which of your AI customers are making you money and which are quietly destroying your gross margin? In episode #375, Ben Murray breaks down the shape of AI usage and why the distribution curve of your customers determines whether your AI subscription product is profitable. This is why Anthropic and GitHub changed their pricing. Heavy users on a flat subscription can quietly turn a 40% gross margin into a negative one, and most finance teams are not tracking token usage by customer in enough detail to see it coming. The three AI usage distribution scenarios every SaaS CFO needs to model: normal, right skew, and left skew, and what each does to your gross margin Why a right-skewed distribution means your light users are subsidizing your heavy users, and how to spot when that subsidy stops working How a left-skewed distribution can leave 80% of customers unprofitable and drag overall gross margin into the negatives Why median, mean, and P90 token usage by customer are now core SaaS finance metrics, not just product analytics What finance needs from product and engineering — usage by customer, model mix, input and output token pricing — to run real pricing scenario analysis Tune in before your next pricing review and find out where your AI margin is actually leaking. Resources Mentioned Ben's AI metrics course with the usage distribution template and free preview: https://www.thesaasacademy.com/ai-finance-metrics-saas AI readiness quiz: https://www.thesaasacademy.com/ai-finance-metrics-saas
May 31 · 5 min
4 SaaS P&L Metrics That Break When You Kill Per-Seat Pricing
The pricing model that built the SaaS industry is being replaced in real time. Is your finance team ready for what it does to your core metrics? In episode #374, Ben Murray breaks down the four SaaS P&L metrics that break when per-seat pricing dies. Public tech leaders are already shifting fast. ServiceNow now drives 50% of net new business from non-seat-based pricing, Workday is reporting hundreds of millions in AI ARR, and GitHub is moving Copilot to usage-based billing. If you are a SaaS CFO or finance leader still modeling on a single blended gross margin, your benchmarks are about to stop working. Why the AI product gross margin sits around 52% and how a 30% revenue mix shift can compress your blended margin by 10 to 15 points How AI COGS scale directly with product usage, breaking the near-zero incremental cost assumption traditional SaaS finance was built on Why one blended LTV no longer works once you have heavy, medium, and light AI usage cohorts, and how to rebuild LTV to CAC by cohort How CAC payback period shifts when gross margin is no longer a single number across the customer base The new frameworks finance teams need to model hybrid subscription plus usage and outcome-based pricing before the board notices the margin compression Tune in to get ahead of the pricing shift before your next forecast and board deck go out. Resources Mentioned Ben's blog post on the SaaS pricing revolution: https://www.thesaascfo.com/saas-per-seat-pricing/ Ben's AI course for SaaS finance leaders: https://www.thesaasacademy.com/ai-finance-metrics-saas
May 29 · 5 min
Per-Seat Pricing Is Dying: What the Shift to Usage-Based SaaS Means for Your Margins
Is per-seat pricing dying a slow death, and is your SaaS expense structure ready for its replacement? In episode #373, Ben Murray breaks down the shift from per-seat subscriptions to usage and outcome-based pricing, and what it means for your finance org. Bloomberg projects subscription pricing falling from 60% to 30% of SaaS models over the next decade, while outcome-based pricing climbs from 10% to 60%. This is no longer a thesis on a slide. GitHub, Salesforce, Zendesk, Intercom, Figma, HubSpot, and others are already repricing, and public companies are reporting AI ARR in the hundreds of millions. If you cannot answer what your AI margins are when the board asks, you are already behind. See exactly how legacy SaaS leaders are repricing, from Zendesk charging per automated resolution to Salesforce billing per AI conversation plus flex credits, and what GitHub's June 1 move to token-based billing signals for the rest of the market. Understand why a single bucket of cloud hosting that blends traditional infrastructure with inference spend leaves you blind, and what instrumentation to put in place before budget season. Learn the questions your board will ask about AI margins, and how to answer whether low-usage customers are quietly subsidizing your heaviest users. Get the case for reconvening your pricing committee now to align product roadmap, AI features, and the expense framework that tracks them. Know which AI unit economics to track by revenue stream and by usage bucket so you can defend margin as your pricing model changes in real time. Listen now and put the tracking framework in place before the AI margin questions land on your desk. Resources Mentioned Ben's blog post: https://www.thesaascfo.com/saas-per-seat-pricing/ New course on AI unit economics and metrics: https://www.thesaasacademy.com/ai-finance-metrics-saas
May 20 · 3 min
The Two SaaStr Annual Slides Every SaaS Operator Needs to See Today
Are you a legacy SaaS company quietly hoping for a recovery that isn't coming? In episode #372, Ben Murray breaks down two slides from Jason Lemkin's State of SaaS keynote at SaaStr Annual that every SaaS operator and CFO needs to confront. The four categories Lemkin laid out will tell you exactly where your company sits in the AI transition, and whether your ARR growth is real or borrowed time. If you're building, leading, or financing a SaaS business right now, this is the reality check that should reshape how you frame your strategy for the next board meeting. Understand the four SaaS+AI categories Jason Lemkin used to map every software company, and which one quietly signals the end of the road Learn why AI driving expansion revenue versus net new customer acquisition matters more than top-line ARR growth right now See which public SaaS companies are pulling off the AI-powered rocket ship growth and what they share Hear the "tired versus wired" narratives that separate operators stuck in 2024 talking points from those building what's next Get a clear lens for whether your AI features are real revenue drivers or just a story you're telling investors Tune in to find out where your company actually sits before the next board meeting forces the question. Resources Mentioned SaaStr Annual / Jason Lemkin: https://saastrannual.com/ Ben's new AI metrics course: https://www.thesaasacademy.com/ai-finance-metrics-saas
May 10 · 4 min
2 AI Metrics Every SaaS CFO Should Track Today
If you're shipping AI product lines, are you measuring the two metrics that actually tell you whether your AI is making money — or burning it? In episode #371, Ben Murray covers two AI unit economics metrics every SaaS CFO and founder should be tracking today: the Inference Expense Ratio and the Work-to-Inference Ratio. Traditional SaaS metrics aren't enough anymore — and a year from now, when your board, investors, and potential acquirers start asking for AI margin and efficiency data, the companies that built the chart-of-accounts structure now will have clean answers. Everyone else will be scrambling. The Inference Expense Ratio (AI revenue ÷ inference cost) — and why you can start calculating this from your GL today if your chart of accounts is set up properly The healthy benchmarks: 10:1 for AI-infused products, 5:1 for AI-native, and why 3:1 is the warning zone where inference is silently eating your gross margin Why this metric only works if your chart of accounts cleanly separates AI revenue from non-AI revenue — and the SKU tagging that makes it possible The Work-to-Inference Ratio — how Salesforce's "agentic work units" concept lets you measure whether your AI is getting more efficient over time Why every AI product needs its own definition of a "work unit" — record updated, report generated, MCP called — and how the wrong definition will distort your margin trends The chart-of-accounts evolution every SaaS company needs right now: from SaaS-only structure to SaaS + AI, with new GL accounts for inference cost in DevOps COGS How the Inference Expense Ratio connects to Ben's ROSE metric — measuring revenue produced per dollar of employee, contractor, and agentic AI spend Tune in to get the AI unit economics framework in place — before your board and investors start asking the questions you can't answer. Resources Mentioned Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas ROSE metric: https://www.thesaascfo.com/saas-rose-metric/
May 9 · 4 min
What Belongs in AI COGS? The Financial Framework SaaS Companies Are Scrambling to Build
Are AI inference costs already eating into your gross margin — and you can't even see them on your P&L? In episode #370, Ben Murray breaks down exactly what belongs in AI COGS for SaaS companies offering an AI-first or AI-infused product line. Inference bills are stacking up fast, infrastructure-layer spend is the surprise line item nobody priced in, and most finance teams haven't built the GL account structure to capture any of it cleanly. If you don't get the framework in place now, you'll be reporting AI gross margin you can't actually defend by next quarter — and your board will notice. The 5 cost categories every AI COGS framework needs — inference, model hosting/GPU infrastructure, the AI infrastructure layer, monitoring and observability, and AI-specific support Why AI inference costs deserve their own GL account — and shouldn't be buried inside your cloud hosting bill where they disappear The surprise cost line one industry report flagged as the #1 unexpected AI expense — hiding in data platform usage, networking, and egress How to structure your COGS cost centers so you can deliver clean margins by AI product line, not just lumped together at the company level Why token tracking by customer cohort (heavy / medium / light users) is now table stakes for any AI product sold as a subscription The deployed-engineer question: should AI support tickets sit with tech support or a specialized team — and how that decision rewires your margin model Tune in to get the AI COGS framework in place before your gross margin lands on a board slide you can't defend. Resources Mentioned Ben's new AI course: https://www.thesaasacademy.com/ai-finance-metrics-saas Ben's blog post: What Should Be Included in AI COGS: https://www.thesaascfo.com/what-should-be-included-in-ai-cogs/ SaaS Metrics Foundation course: https://www.thesaasacademy.com/the-saas-metrics-foundation

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