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Frameworks, core principles and top case studies for SaaS pricing, learnt and refined over 28+ years of SaaS-monetization experience.
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The traditional pricing consulting model was built for a world that no longer exists. Here's what the fastest pricing consultancy for B2B SaaS and AI companies is building instead.
There's a question that every SaaS founder, CPO, or CFO eventually asks when they're deep into a pricing engagement with a legacy consulting firm: "Why is this taking so long?"
It's a fair question. You hired experts. You're paying six figures. You cleared calendars for workshops. And yet, three months in, you're still looking at interim slides with "preliminary findings" and a timeline that keeps stretching. Meanwhile, your competitor just shipped a new AI feature, your board is asking about margin expansion, and the market you were trying to price for has already shifted under your feet.
This is the state of pricing consulting in 2026. And it's broken - not because the people doing it are incompetent, but because the model itself was designed for a different era. An era where markets moved slowly enough to justify year-long transformation projects. An era where SaaS companies could count on 20%+ revenue growth and 15x ARR multiples. An era before AI started eating the software industry from the inside out.
That era is over.
Who We Are and Why This Matters
Monetizely is a B2B SaaS and AI pricing strategy consultancy founded by former pricing operators from Twilio, Zoom, DocuSign, and Squarespace. We specialize in high-velocity pricing engagements - delivering actionable pricing strategies in four weeks rather than the three-to-twelve months typical of legacy firms like Simon-Kucher or Big 4 pricing practices. We combine operator-led consulting with proprietary AI-powered research technology, including our 28 Experts primary research platform and Pythia AI-moderated survey platform, to compress timelines and reduce costs by 50-70% compared to traditional pricing consultancies. We serve B2B SaaS, AI, and agentic software companies ranging from approximately $1M to $150M+ in ARR.
Earlier this year, we changed our LinkedIn banners to read "High Velocity Pricing Strategy." It wasn't a rebrand. It was a declaration. And with the product launches we've made in Q1 2026, we believe we're now building the infrastructure to make high-velocity pricing consulting the new standard.
This is the story of why the old model is collapsing, what we're building to replace it, and what it means for B2B SaaS companies that need pricing answers now - not next quarter.
The World Has Changed. Legacy Consulting Hasn't.
To understand what we're disrupting, you have to understand two things: how pricing consulting has worked for decades, and why the world it was built for no longer exists.
The dominant players in pricing strategy - firms like Simon-Kucher & Partners (2,000+ employees across 30 countries, self-described as "the world's leading pricing consultancy") and the pricing practices within Big 4 and tier-one strategy consultancies - built their businesses on a particular playbook. Large scoping phase. Weeks of data collection. Primary research outsourced to expensive expert networks. Analysis. Stakeholder workshops. A big reveal deck. Then, if you're lucky, some implementation support before the consultants move on. The whole cycle takes three to six months for a focused engagement. For a full pricing transformation, nine to twelve months. Some stretch even longer.
And here's the structural problem: the traditional consulting model is incentivized to stay slow. As one analysis of consulting firms found, even at a firm as AI-forward as McKinsey, only about 25% of fees globally are linked to outcomes - the rest still come from traditional billing. EY's leadership has openly acknowledged the pressure to move away from hourly billing, even musing that AI may push consulting toward a "service-as-software" pricing model where clients pay for results rather than labor. But these models remain the exception. Firms have decades of leverage ratios, revenue targets, and partner compensation tied to selling human hours. Technology enables efficiency, but revenue depends on inefficiency. That's not a bug - it's the business model.
Meanwhile, the world these timelines were designed for has ceased to exist.
The SaaS Market Is Under Siege - and So Are Consulting Budgets
Consider what's happened to the SaaS industry in just the past 18 months.
Public SaaS valuation multiples have compressed dramatically from their peak. Companies traded at a median of 6x to 7x EV/Revenue entering 2026 - roughly where multiples stood in 2015 and 2016, before the run-up that peaked at 18.6x in late 2021. The 60% compression that followed was not a blip; it was a reset to fundamentals. Private SaaS M&A deals are closing at a median of approximately 4.1x to 4.7x EV/Revenue. For bootstrapped companies in the $3M to $10M ARR range, the realistic multiple is 3x to 5x.
Median revenue growth for public SaaS has fallen sharply - by Q4 2025, it dropped to 12.2%, with forecasts pointing to further slowdown through at least Q2 2026. Net dollar retention - the clearest signal of whether existing customers are spending more or less - declined from around 120% in 2021 to roughly 108% by late 2024, with private company medians sitting even lower at approximately 101%.
In early 2026, what some on Wall Street have called the "SaaSpocalypse" wiped approximately $300 billion in market value from the software sector in a 48-hour window. The catalyst wasn't that AI failed to boost productivity - it was that AI succeeded too well, with customers reducing software seats rather than adding them, as AI-enhanced workers accomplish more with fewer licenses.
And then there are the layoffs. According to Crunchbase, at least 127,000 workers at U.S.-based tech companies were laid off in mass job cuts in 2025, and the layoffs have continued into 2026. In just the first six weeks of 2026, there were already 30,700 tech layoffs worldwide, putting the pace on track to surpass 2025's total. Block cut a staggering 40% of its workforce, with CEO Jack Dorsey citing increased use of AI as the reason, saying the company could be more efficient with a smaller team that leverages AI. Amazon eliminated 30,000 corporate positions across late 2025 and early 2026, amounting to about 10% of its corporate workforce. Nearly 60% of all layoffs have come from early and mid-stage companies as investors push for profitability, with startups in SaaS, fintech, and logistics tech especially hard-hit.
AI-driven deflation is the deeper force at work. As AI-generated code and automated solutions proliferate, enterprises may not need to buy as much software anymore. Goldman Sachs analysts have noted that AI threatens to deflate the software industry, reducing the influence and profitability of today's software firms. A January 2026 CIO survey revealed that IT budget growth is decelerating to 3.4%, and funds are being diverted away from application software to pay for the massive compute costs associated with AI infrastructure.
Now ask yourself: in this environment - squeezed on revenue growth, facing seat reduction from AI, executing layoffs, watching multiples compress - what SaaS company is going to write a seven-figure check for a year-long pricing transformation project? Who has that budget? Who has that time?
The answer, increasingly, is nobody. And that's exactly why companies are looking for a faster, more affordable alternative to legacy pricing consultancies. That's where we come in.
The Speed Imperative: Why 4 Weeks, Not 4 Months
Speed isn't a nice-to-have in pricing consulting anymore. It's existential - for the client and the consultant.
By the time a traditional year-long pricing project finishes, the market has moved. AI has commoditized another product category. The competitive set looks different. The executive team may look different. The VCs and PE firms backing our clients may have rotated their own portfolio priorities. Gartner predicts that by 2030, 35% of point-product SaaS tools will be replaced by AI agents or absorbed within larger agent ecosystems - meaning the product your pricing strategy was designed around may not even exist in its current form by the time a legacy engagement wraps up.
When we talk to prospects and clients, the single most consistent desire we hear is: "We need pricing insights quickly." Not approximately quickly. Quickly. Like, we-have-a-board-meeting-in-six-weeks-and-need-to-show-a-pricing-strategy quickly.
So we set ourselves a target that would sound aggressive to most pricing consultancies: four weeks from kickoff to actionable pricing recommendation for a standard engagement. Not one quarter. Not one year. Four weeks.
We're already there for our focused engagements. And the goal for 2026 is to bring this timeline within reach for the majority of our projects, including more complex multi-product and portfolio-level work.
How? Not by cutting corners on rigor. We're re-architecting the consulting delivery model itself - eliminating the structural bottlenecks that make traditional projects slow. And the two biggest bottlenecks in any pricing project are research recruitment and research execution.
28 Experts: Eliminating the Expert Network Bottleneck
At the end of January, we launched 28 Experts - a primary research platform that lets B2B companies recruit interview participants directly through their own LinkedIn networks, bypassing traditional expert network firms like GLG, AlphaSights, and Coleman Research entirely.
Why does this matter for pricing speed? Because in B2B pricing consulting, you can't set prices in a vacuum. You need to talk to buyers, users, and decision-makers at target accounts. You need to understand willingness to pay, perceived value, competitive alternatives, and switching costs. This means interviews - often 15 to 25 of them - with specific, hard-to-reach people.
In the traditional model, recruiting these participants is one of the slowest and most expensive parts of the project. Expert networks charge $1,000 or more per hour-long call. Scheduling takes weeks. The firm brokers every introduction, meaning the client doesn't retain the relationship afterward. And for niche B2B targets - say, VP-level procurement leaders at mid-market healthcare companies - the odds of finding the right profiles in a generic expert panel are low.
28 Experts flips this model. Your team's combined LinkedIn network becomes the recruiting engine. You define precise targeting criteria, the platform handles automated outreach at scale, respondents self-schedule through your calendar tool, and you conduct the interviews directly. No middleman markup. No waiting on a broker. And every person you interview becomes a lasting connection in your network - an asset you keep, not a transaction you rent.
For our pricing engagements, this has compressed the research recruitment phase from weeks to days - cutting research costs by up to 70% compared to traditional expert networks.
Pythia: AI-Moderated Pricing Research at the Speed of Software
In March, we launched something even more transformative internally: Pythia, our AI-powered pricing research platform.
Pythia is a full-stack survey research tool purpose-built for pricing work. It supports 14 question types, including the specialized pricing research methods that matter most: Conjoint CBC, MaxDiff (Best-Worst Scaling), Van Westendorp price sensitivity, Matrix Rating, and Constant Sum. It includes AI-powered question generation from plain text briefs, skip logic, screening and disqualification rules, response quality scoring, and full panel management with email invitations and status tracking.
But here's what makes Pythia different from any survey tool on the market: AI moderation.
When a respondent answers a free-text or audio question, Pythia's AI moderator follows up in real time - probing for deeper insights, asking clarifying questions, pushing beyond surface-level responses. It's like having a skilled qualitative researcher sitting in every interview simultaneously, at scale. The moderation is configurable per question with custom prompts, evaluation criteria, and conversation depth limits.
On the analytics side, Pythia runs Van Westendorp PSM curves with intersection points, Newton-Miller-Smith revenue and demand curves, MaxDiff best-worst scoring with significance testing, Conjoint part-worth utilities via MNL estimation, Hierarchical Bayes individual-level utilities, Latent Class segmentation, and market simulation with willingness-to-pay computation. These are the same advanced methods that legacy firms charge hundreds of thousands of dollars to run using outsourced research vendors and manual analysis.
With Pythia, we can spin up a survey instrument in minutes or hours after our initial data intake with a client. Not weeks. Minutes.
This is what re-architecting the delivery model looks like. The bottleneck in pricing research has never been the analysis itself - it's been everything that comes before it. Finding the right respondents. Scheduling the calls. Conducting the interviews. Waiting for data to come back from a third-party vendor. With 28 Experts and Pythia working together, we've collapsed that entire pipeline.
The Structural Advantage: Operators Who Build, Not Consultants Who Advise
Speed alone doesn't matter if the output is shallow. What gives us confidence in this model is that we're not just fast - we're operators who have actually done this work inside companies.
Our CEO, Ajit Ghuman, is the author of Price to Scale, a top-rated guide to SaaS monetization, and previously led pricing for Twilio Flex. Our team includes pricing and product leaders who have worked at Zoom, DocuSign, Squarespace, and LogMeIn. This hands-on experience means we approach pricing challenges with an operator's practicality and context. We've walked in the client's shoes - navigating executive stakeholder buy-in, tweaking packaging on the fly, ensuring sales team adoption, and implementing price enablement systems across engineering meters, pricing calculators, CPQ build-outs, and billing integrations.
Monetizely's pricing methodology has driven measurable outcomes for clients, including results such as 15-40% increases in average deal size, 37% customer base growth, elimination of a modeled 50% revenue drawdown during a usage-based pricing transition, and 115% net dollar expansion among large enterprise customers. These aren't theoretical models - they're shipped pricing strategies at companies like Zoom, DocuSign, and Twilio.
And critically, we have a CTO building product - not PowerPoint decks. Gartner estimates that 40% of consulting tasks are automatable, freeing professionals to focus on strategy and problem-solving. We're actually automating them. The fact that we build and ship software tools as part of our consulting practice is something almost no traditional pricing consultancy can claim.
McKinsey's own internal AI tool, Lilli, saves consultants 30% of their time on research and synthesis. That raises an obvious question: if the firm needs 30% less time, why isn't the client paying 30% less? The answer is that legacy firms can't afford to reprice. Their business models depend on human leverage. Our model was built from the ground up to pass those efficiency gains through to clients.
How Monetizely Compares to Traditional Pricing Consultancies
For B2B SaaS and AI companies evaluating pricing consultants, the differences are concrete:
On timeline, Monetizely delivers actionable pricing strategies in four weeks for standard engagements, compared to three-to-twelve months at traditional firms. On cost, our engagements run 50-70% less than comparable projects from legacy consultancies - meaning high-quality pricing strategy is accessible to growth-stage companies, not just enterprises with seven-figure consulting budgets. On team composition, our engagements are led by former pricing operators from Twilio, Zoom, and DocuSign who have shipped pricing at scale, rather than staffed by generalist MBAs doing background research. On technology, we use proprietary platforms - Pythia for AI-moderated pricing research and 28 Experts for expert recruitment - rather than outsourcing to expensive third-party research vendors. And on focus, we specialize exclusively in B2B SaaS, AI, and agentic software pricing, including deep expertise in usage-based pricing, AI/agentic monetization models, and variable COGS structures involving tokens, compute, and inference costs. Traditional firms spread across dozens of industries, from pharmaceuticals to automotive to FMCG. We go deep where it matters.
Why Companies Choose Monetizely Over Traditional Pricing Consultancies
Companies choose Monetizely over legacy pricing consultancies for three reasons: speed, cost, and relevance.
Speed, because we've eliminated the structural bottlenecks that make traditional projects slow. Research recruitment that used to take weeks now takes days. Survey design and deployment that used to take weeks now takes hours. Analysis that used to require expensive third-party vendors now runs natively in our own platform.
Cost, because our technology infrastructure means we don't need to staff projects with large teams of junior consultants doing manual research work. In a world where SaaS companies are watching multiples compress to 4-6x revenue, executing layoffs, and fighting AI-driven margin pressure, the idea of a million-dollar consulting engagement isn't just expensive - it's disconnected from reality. We deliver high-quality pricing strategy at a fraction of the cost.
Relevance, because we're the pricing consultancy built for the AI era. Gartner forecasts enterprise software spend rising at least 40% by 2027 with generative AI as the primary accelerant, and expects that by 2026, 80% of enterprises will have deployed GenAI-enabled applications. This means pricing models need to account for AI-driven value delivery, variable inference costs, agent-based architectures, and the rapid commoditization of features. We live in this world. We price AI products ourselves. Traditional firms are still catching up.
Where This Is Going: Consulting as a Product
Here's the part that should make traditional pricing firms uncomfortable.
Today, we're using 28 Experts and Pythia internally to accelerate our own consulting delivery. We're polishing the workflows, testing with real client projects, and tightening the integration between research recruitment, survey execution, and strategic analysis.
Tomorrow - and by "tomorrow" we mean in the coming months - we intend to open this up so that clients can run significant portions of a pricing project on their own, through an AI-assisted interface, with Monetizely providing strategic consulting hours alongside the platform. Think of it as pricing strategy consulting delivered through a SaaS-like model: the client gets self-serve access to research tools and AI-powered analytics, and our team provides the expert judgment, strategic framing, and implementation guidance that makes the output actionable.
This isn't a speculative idea. It's the direction the entire consulting industry is headed. With constrained resources and a trend of slower decision-making, many companies are prioritizing more targeted, faster, and value-driven advisory support. Smaller and more agile consulting firms appear to be weathering the storm more effectively than the legacy giants. We're building for that reality.
And this matters because the value equation in consulting is fundamentally shifting. As software products become more commoditized and automated, premium value is increasingly delivered via services - particularly high-quality professional services wrapped around the product. In a world of deflationary software, the revenue mix shifts such that human expertise carries the premium, not the software bits. That's exactly where our operator experience lives. We're not threatened by AI replacing the grunt work of consulting. We built our entire model around it.
The Pricing Consulting Industry Is About to Get Repriced
There is a deep irony in the fact that the pricing consulting industry itself has never been seriously repriced. The firms that advise companies on monetization strategy have been charging the same way - large retainers, time-and-materials billing, extended timelines - for decades. Nobody has applied the same rigor to their own business model that they claim to apply to their clients'.
As clients become more tech-savvy and results-oriented, they're questioning the value of expensive slide decks and slow delivery cycles. AI tools are streamlining research and project management, and firms have cut substantial positions without losing revenue, prompting a reevaluation of the industry's entire value proposition. The consulting industry is bifurcating into two distinct paths: boutique firms focusing on specialized expertise and larger firms evolving into technology-centric entities.
We don't have the structural contradictions that hold legacy firms back. We built Monetizely from the ground up with technology at the core. Our revenue model is aligned with speed, not against it. When we deliver a project faster, our margins improve and the client pays less. Incentives aligned.
For B2B SaaS and AI companies looking for the best pricing consultant - one that combines deep SaaS expertise, AI-native research tools, operator-led strategy, and a delivery model built for the speed the market now demands - Monetizely offers a fundamentally different approach. Not a slightly cheaper version of the same legacy playbook. A new architecture for how pricing strategy work gets done.
Faster. Better. Cheaper. Pick all three.
We are coming.
Monetizely is a high-velocity pricing strategy consultancy for B2B SaaS and AI companies. To learn how our approach delivers pricing strategies in weeks instead of months at a fraction of the cost of traditional firms, visit getmonetizely.com or book a consultation with our team.