80+ SaaS statistics every founder should know in 2026

SaaS is still growing, but the market looks different from the one founders knew three years ago.

Companies spent much of 2024 and 2025 consolidating their software stacks and questioning subscriptions that failed to deliver clear value. Now AI is pushing those stacks outward again. New AI-native tools are appearing, established platforms are adding intelligent features and vendors are experimenting with pricing beyond the traditional per-seat model.

The following SaaS statistics show where the market stands in 2026, from growth and retention to AI adoption, spending, security and cloud infrastructure.

SaaS market statistics

Software remains one of the largest areas of global technology spending. Demand is no longer driven only by companies moving processes to the cloud. AI infrastructure, automation and the need to connect data across products are creating another wave of investment.

1. The global SaaS market was valued at approximately $315.7 billion in 2025. (Fortune Business Insights)
2. The market is projected to reach roughly $375.6 billion in 2026. (Fortune Business Insights)
3. The global SaaS market could grow to around $1.48 trillion by 2034. (Fortune Business Insights)
4. That forecast represents a compound annual growth rate of approximately 18.7% between 2026 and 2034. (Fortune Business Insights)
5. Worldwide IT spending is expected to reach $6.31 trillion in 2026, up 13.5% from 2025. (Gartner)
6. IT services spending alone is forecast to exceed $1.87 trillion in 2026. (Gartner)
7. Public cloud spending continues to rise faster than many other areas of enterprise IT, helped by growing investment in AI infrastructure and services. (Gartner)
8. The United States remains the largest national market for SaaS companies and enterprise software spending. (Fortune Business Insights)
9. North America continues to account for the largest share of the global SaaS market. (Fortune Business Insights)
10. Large organizations remain the biggest SaaS buyers, but small and mid-sized companies are expected to record some of the fastest adoption growth. (Fortune Business Insights)

The SaaS opportunity is still large. The challenge is that market growth no longer guarantees growth for every vendor. Buyers have more options, higher expectations and more pressure to justify every recurring expense.

Product teams also have more infrastructure choices. They can build every product capability themselves, purchase a specialist platform or combine both options. The same decision applies to analytics. Our guide to the embedded analytics stack decision tree explains how product type, data complexity and customer expectations influence the right setup.

SaaS growth statistics

The growth-at-all-costs era has been replaced with closer scrutiny of retention, margins and capital efficiency.

Founders are still expected to grow, but investors and leadership teams increasingly want that growth to come from a sustainable operating model.

11. The median annual growth rate for private B2B SaaS companies was 25% in the 2025 SaaS Capital benchmark. (SaaS Capital)
12. That median fell from 30% in the previous benchmark, showing that growth conditions remain more difficult than they were during the SaaS boom. (SaaS Capital)
13. Bootstrapped SaaS companies reported median annual growth of 23%. (SaaS Capital)
14. Equity-backed SaaS companies reported median annual growth of 25%. (SaaS Capital)
15. Approximately 6.9% of companies in the benchmark reported flat or negative growth. (SaaS Capital)
16. The growth rate that counts as strong depends heavily on company size. Growth of 25% may put a $20 million ARR company above its peer median while leaving a $2 million ARR company below its peer median. (SaaS Capital)
17. Nearly half of software startups eventually reach $1 million in ARR within ten years. (ChartMogul)
18. Only around one in ten software startups reaches $10 million ARR within the same period. (ChartMogul)
19. Approximately one in 50 reaches $25 million ARR. (ChartMogul)
20. Among companies that reach $1 million ARR, the typical journey takes between two and five years. (ChartMogul)
21. Only 3.3% of software startups reach $1 million ARR in their first 12 months. (ChartMogul)
22. Top-quartile bootstrapped companies reach $1 million ARR in around two years, only four months later than comparable VC-backed companies. (ChartMogul)

These numbers should not discourage early-stage founders. They show how unusual breakout growth is and why comparing a company with heavily funded outliers can create unrealistic targets.

Growth also depends on what happens after someone lands on the website. Product positioning, pricing communication and conversion rate optimization services can help companies turn more of the demand they already generate into trials, demos and paying customers.

SaaS retention statistics

Acquisition still matters, but more SaaS growth now comes from customers who are already using the product.

As acquisition costs rise and software categories become crowded, retention and expansion have become central growth levers.

23. Moving from NRR in the 90–100% range to the 100–110% range is associated with approximately five percentage points of additional annual growth. (SaaS Capital)
24. Companies with the highest NRR recorded median growth rates 83% higher than the overall benchmark median. (SaaS Capital)
25. Larger SaaS companies with between $15 million and $30 million or more in ARR now generate approximately 40% of their growth from expansion revenue. (ChartMogul)
26. In early 2021, expansion accounted for closer to 30% of growth among those companies. (ChartMogul)
27. Annual plans generally produce stronger retention than monthly plans across ARR and average revenue per account ranges. (ChartMogul)
28. Companies with stronger gross revenue retention and net revenue retention are more likely to maintain growth as they scale. (ChartMogul)
29. Expansion revenue becomes more important as SaaS companies mature and the relative contribution of new customer acquisition falls. (ChartMogul)

A product cannot rely forever on bringing in new customers faster than old ones leave. The more mature the company becomes, the more its growth depends on helping current users get enough value to stay, upgrade and expand usage.

Customer-facing reporting can make that value easier to see. Customer-facing analytics gives users relevant reports and interactive data views inside the product rather than forcing them to export data or request updates from a support team.

SaaS spending and budget statistics

SaaS companies are operating with tighter budgets than they did during the 2020–2021 funding boom.

Bootstrapped businesses tend to stay closer to profitability, while equity-backed companies are more likely to spend ahead of revenue.

30. Bootstrapped private B2B SaaS companies spend a median of 96% of ARR across all departments. (SaaS Capital)
31. Equity-backed SaaS companies spend a median of 101% of ARR. (SaaS Capital)
32. Approximately 83% of bootstrapped SaaS companies are profitable, at breakeven or within two percentage points of breakeven. (SaaS Capital)
33. Only around 52% of equity-backed companies meet the same profitability or near-breakeven threshold. (SaaS Capital)
34. The median SaaS company spends approximately 15% of ARR on sales. (SaaS Capital)
35. Marketing accounts for a median of around 8% of ARR. (SaaS Capital)
36. Customer support and customer success account for approximately 9% of ARR. (SaaS Capital)
37. Research and development is one of the largest expenses, with median spending equal to 22% of ARR. (SaaS Capital)
38. Hosting costs account for a median of approximately 5% of ARR. (SaaS Capital)
39. DevOps accounts for another 4% of ARR. (SaaS Capital)
40. Professional services cost of goods sold represents approximately 5% of ARR. (SaaS Capital)

These percentages are useful benchmarks, not fixed targets. A developer platform may naturally spend more on infrastructure and R&D, while a sales-led enterprise product may put a larger share of ARR into sales.

The more useful question is not whether a team matches the median exactly. It is whether each part of the budget creates measurable customer or business value.

Teams considering embedded reporting should also compare the ongoing cost of an internal build with a specialist platform. An analytics-as-a-service model can reduce the infrastructure and maintenance work product teams need to own.

SaaS pricing statistics

Per-seat subscriptions are still common, but they fit some AI products poorly.

When software performs tasks rather than supporting a fixed number of human users, vendors need pricing metrics that reflect usage, work completed or value delivered.

41. Around 22% of the average technology stack is now AI-powered. (BetterCloud)
42. More than one-third of those AI products use only usage-based or token-consumption pricing rather than traditional per-seat or hybrid plans. (BetterCloud)
43. Three out of five SaaS companies were already using some form of usage-based pricing in OpenView’s benchmark. (OpenView)
44. Approximately 46% of SaaS companies using usage-based pricing had adopted a hybrid model that combined subscriptions with consumption. (OpenView)
45. Only around 15% used a primarily pay-as-you-go or usage-based model. (OpenView)
46. In Luzmo’s research into SaaS analytics, 59% of SaaS companies used add-on features such as reporting and analytics to drive expansion revenue. (Luzmo)
47. The same research found that 21% of cloud software products received negative reviews about valuable features such as analytics being locked behind higher pricing tiers. (Luzmo)

Usage-based pricing can make it easier for customers to start small and expand. It can also make bills less predictable.

SaaS companies need to select a value metric that customers understand and can influence. Charging for a metric that feels disconnected from the value received can quickly create pricing frustration.

Analytics creates a particular pricing challenge because basic reporting may feel like a standard product requirement, while advanced exploration can deliver enough value to support a premium tier. The guide to finding what to charge for in a data-heavy product explains how teams can identify paid features from customer jobs instead of arbitrarily locking charts behind a higher plan.

Companies further along can use the analytics monetization maturity framework to assess whether their product has enough trust, adoption and perceived value to support a paid analytics offer.

SaaS adoption statistics

SaaS portfolios contracted as companies removed unused or duplicate software. That consolidation now appears to be reversing as businesses add AI tools and intelligent features.

48. The average company managed approximately 106 SaaS applications in 2025. (BetterCloud)
49. In 2026, the average SaaS portfolio increased by 11% year over year after two years of consolidation. (BetterCloud)
50. Mid-market organizations recorded the largest increase, with the average number of applications rising from 116 to 164 in one year. (BetterCloud)
51. That represents a 41% increase in app volume among the mid-market organizations studied. (BetterCloud)
52. AI is the main force behind the renewed expansion of SaaS portfolios. (BetterCloud)

The software stack is not returning to its old shape. Traditional tools are increasingly joined by AI-native applications, intelligent add-ons and agents capable of taking actions across multiple platforms.

That creates opportunities for SaaS vendors, but it also raises the standard for integration, governance and measurable value.

Product teams adding reporting to that stack need to decide when the feature deserves dedicated infrastructure. The embedded analytics readiness guide covers the product, data and engineering signals that indicate a team is ready to move beyond basic reports.

AI in SaaS statistics

AI is no longer a separate software category. It is becoming a product layer across customer service, analytics, development, security, marketing and operations.

53. Organizations now deploy an average of 27 AI-powered SaaS applications. (BetterCloud)
54. Those tools account for approximately 22% of the average SaaS portfolio. (BetterCloud)
55. In 2025, organizations used an average of only 7.3 SaaS applications with AI functionality, although the methodology and definition used in that earlier dataset were narrower. (BetterCloud)
56. Approximately 95% of companies had invested in at least one AI-driven use case. (BetterCloud)
57. More than 60% of enterprise SaaS products had embedded AI features by 2025. (BetterCloud)
58. Around 40% of organizations used AI in customer service or support automation applications. (BetterCloud)
59. Approximately 45% used AI in IT service management applications. (BetterCloud)

AI-enabled features are quickly becoming expected. Adding a generic assistant to a product is no longer enough to create a lasting point of difference.

The stronger opportunity lies in applying AI to workflows where the product already has context, trusted data and a clear job to complete. Embedded AI places those capabilities directly inside the software experience instead of sending users to a separate general-purpose tool.

Analytics is moving one step further. Agentic analytics does not stop at explaining a chart. It can select tools, investigate a question and complete parts of an analytical workflow under defined product and data guardrails.

Developers do not have to implement the entire experience as one large assistant. Luzmo’s Agent APIs expose narrower capabilities for tasks such as describing data, finding datasets, creating formulas and generating visualizations.

SaaS analytics statistics

Customers do not only expect SaaS products to store data or automate tasks. They expect the product to explain what is happening and help them decide what to do next.

That is pushing analytics from an internal reporting function into the customer-facing product experience.

60. In Luzmo’s analysis of productivity software reviews, 20% of reviews mentioned analytics dashboards or reporting. (Luzmo)
61. In an analysis of 140 popular software products, 94% displayed analytics dashboards on their websites. (Luzmo)
62. Approximately 74% of the tools studied received negative reviews related to their user interface. (Luzmo)
63. Reporting and analytics were among the product capabilities SaaS companies most commonly used to support expansion revenue. (Luzmo)
64. Product analytics is moving beyond static dashboards toward natural-language questions, generated summaries and proactive insights. (Luzmo)
65. Embedded analytics allows SaaS companies to place those insights inside the workflows customers already use instead of sending them to a separate BI tool. (Luzmo)

The next stage is not simply adding more dashboards. It is making analytics easier to reach and act on.

A dashboard can show a user that a metric has changed. AI-powered analytics can help explain why it changed, which segment caused the movement and what the user may want to investigate next.

The foundation still matters. An interactive dashboard lets users filter data, drill into individual categories and change the view without requesting another static report.

Teams evaluating vendors can compare the leading embedded analytics tools based on embedding options, multi-tenant security, customization, AI support and pricing.

Interface quality matters as much as technical functionality. Broader website design statistics also show how quickly users form impressions from visual presentation and usability. A customer-facing dashboard may contain the right data and still fail when users cannot understand its hierarchy or find the controls they need.

This is one reason embedded analytics often misses the mark. Teams can build too many dashboards before validating the user’s problem, assign no clear owner to the experience or try to monetize analytics before customers use it regularly.

SaaS security statistics

As software stacks grow, so does the number of identities, integrations and data flows a security team has to govern.

AI adds another layer. Employees can adopt new tools quickly, while agents may operate through service accounts and access several systems at once.

66. Around 15% of employees use unsanctioned AI tools on corporate devices. (BetterCloud)
67. Two-thirds of IT professionals are more concerned about data loss or rogue AI agents than about their organization adopting AI too slowly. (BetterCloud)
68. The global average cost of a data breach was $4.44 million in 2025. (IBM)
69. That figure was approximately 9% lower than the previous year, partly because organizations identified and contained breaches faster. (IBM)
70. The average cost of a data breach in the United States reached a record $10.22 million. (IBM)
71. Around 13% of organizations reported a breach involving an AI model or AI application. (IBM)
72. Of the organizations that experienced an AI-related security incident, 97% lacked proper AI access controls. (IBM)
73. Organizations using security AI and automation extensively saved an average of $1.9 million per breach compared with organizations that did not use them. (IBM)
74. Extensive use of AI and automation shortened the breach lifecycle by approximately 80 days. (IBM)

Security cannot remain a final review added after an AI feature has been built. Access controls, data policies and monitoring need to cover human users, service accounts and AI agents from the start.

The same applies to analytics. Customer-facing data experiences must respect tenant boundaries, user roles and account-level permissions while still returning results quickly enough to feel native to the product.

Cloud infrastructure statistics

SaaS growth continues to support the cloud infrastructure market. AI workloads have accelerated that growth as companies buy more compute, storage and managed services.

75. Global enterprise spending on cloud infrastructure reached approximately $119 billion in the first quarter of 2026. (Synergy Research Group)
76. The market’s annual revenue run rate passed $500 billion during the same quarter. (Synergy Research Group)
77. Cloud infrastructure spending increased by approximately 35% year over year in Q1 2026. (Synergy Research Group)
78. AWS held approximately 28% of the worldwide cloud infrastructure market. (Synergy Research Group)
79. Microsoft held approximately 21%. (Synergy Research Group)
80. Google held approximately 14%. (Synergy Research Group)
81. Together, Amazon, Microsoft and Google controlled roughly 63% of the global cloud infrastructure market. (Synergy Research Group)
82. Google and Microsoft continued to grow faster than AWS in percentage terms, although AWS retained the largest individual market share. (Synergy Research Group)
83. Generative AI has become one of the main factors accelerating cloud infrastructure demand. (Synergy Research Group)

The market share numbers have shifted since the early 2020s, but the larger trend remains familiar. A small group of hyperscale providers supports much of the infrastructure behind modern SaaS products.

At the same time, AI is creating space for specialized infrastructure providers, model-hosting platforms and services designed for compute-intensive workloads.

As stacks become more distributed, teams also need infrastructure monitoring tools that can help them track availability, resource consumption and failures across services. Monitoring becomes particularly important when a customer-facing feature depends on several databases, APIs, models and cloud providers working together.

What SaaS statistics tell us about 2026

The SaaS market is not disappearing. It is becoming less forgiving.

Buyers are still adding software, but they are more willing to remove products that fail to deliver visible value. Growth is still possible, but it depends more heavily on retention, expansion and efficient spending. AI creates new product opportunities while also raising infrastructure costs, security risks and pricing questions.

For SaaS founders, five conclusions stand out.

Growth depends on existing customers

As companies mature, expansion revenue makes up a larger part of total growth. Strong NRR can separate a growing SaaS company from one that has to replace lost revenue every quarter.

That makes customer value a financial metric, not only a product goal.

Analytics can become part of that expansion strategy when it solves a sufficiently valuable customer problem. The guide to embedded analytics as a revenue feature shows how SaaS companies package customer insights as premium modules, add-ons or higher product tiers.

AI needs a real job inside the product

Users no longer need another generic chat window. They need AI that understands their data, works inside existing workflows and reduces the effort required to complete a task.

A focused AI feature connected to trusted product data may create more value than a broad assistant with limited context.

Per-seat pricing will not fit every AI product

AI agents can complete work without adding another human seat. That makes consumption, credits, actions and outcome-based models more relevant.

The right model will depend on how customers experience value and how predictable the underlying costs are.

Software sprawl is returning in a new form

The average SaaS stack is growing again, but AI is driving the increase.

That creates room for new products. It also increases pressure on IT teams to control access, understand usage and prevent sensitive data from moving into unapproved systems.

Analytics is becoming part of the product experience

Customers want more than raw data. They want answers, explanations and useful next steps.

Embedded analytics puts dashboards, reports and self-service data exploration inside the software product where users already work.

Customer-facing analytics can help users understand the value they receive from a SaaS product. AI can make those insights easier to explore, but only when the underlying data is accurate, governed and available in context.

Turn product data into a feature customers use

SaaS products collect valuable data every day, but users do not always have an easy way to explore it.

With embedded analytics, you can add interactive dashboards and customer-facing insights directly to your application. Your users get the answers they need without leaving your product, while your team avoids building and maintaining an analytics layer from scratch.

Luzmo helps SaaS teams launch embedded analytics in days rather than months. Build dashboards with Luzmo Studio, create custom experiences with the Flex SDK or help users explore their data through AI-powered analytics.

Explore what customer-facing analytics with Luzmo can look like or start building your first dashboard today.