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The Great SaaS Hangover (and the Cure Nobody Is Talking About)

Chris Webber
Formstack

We've all been there.

The morning-after fog. The pounding headache. The light sensitivity. The creeping existential dread.

You had a few too many last night.

It's not entirely your fault. The playlist was amazing. The dance floor was hopping. The drinks were flowing. It happens to the best of us.

What follows varies from person to person — and culture to culture. Reddit threads offer thousands of post-party remedies: a scalding hot shower, a punishing gym session, a greasy breakfast, or — for the bold — the infamous "hair of the dog." Some of these might help. Most don't. At the end of the day, everyone comes to the same conclusion: the only surefire way to avoid a hangover is to drink less in the first place.

And that brings us to the SaaS industry.

The SaaS Party That Went Too Hard

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality.

Gartner estimated global SaaS spending hit $157 billion in 2020, and it hasn't slowed much since. Companies layered tools upon tools, often with overlapping functionalities, all in the name of agility and speed.

But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill.

Welcome to the Great SaaS Hangover.

What Is a SaaS Hangover?

A SaaS hangover is the result of years of unchecked software adoption. It's marked by:

  • Redundant tools doing the same job in slightly different ways.
  • Ballooning software costs where every employee is another $$$ per month.
  • Disjointed user experiences that frustrate employees and reduce productivity.
  • Security and compliance risks from managing too many vendors and endpoints.

In fact, a 2023 Productiv report found that companies use an average of 371 SaaS apps, yet only 47% are actively used in any given 30-day period. That's like stocking your fridge with five brands of orange juice and drinking just one.

The Cure: SaaS Consolidation Through Horizontal Platforms

Here's the good news: unlike a gin-fueled hangover, the SaaS hangover does have a cure — and it's surprisingly simple: Shrink your stack. Consolidate your spend. Invest in platforms, not point solutions.

The smartest companies today are shifting toward horizontal platforms — tools that solve broad business problems across departments, rather than hyper-specialized point solutions. Think Notion over five separate productivity apps. Think HubSpot over a scattered mix of CRM, email, and marketing tools. Think Microsoft 365, not a patchwork of document editors, cloud drives, and meeting apps.

Why It Works

  • Lower cost: Bundled pricing often beats à la carte tools.
  • Simpler onboarding: Fewer tools means faster adoption and less training.
  • Better integration: Native connections across features reduce data silos.
  • Improved visibility: Centralized platforms offer unified reporting and analytics.
  • Stronger security: One platform means fewer vendors to vet and monitor.

And here's the kicker: consolidation doesn't mean compromise. Modern horizontal platforms are more robust than ever, often outperforming niche competitors while offering broader utility.

You Wouldn't Build a Sandwich This Way

Let's end with a metaphor as simple as it is relatable: you wouldn't go to three different sandwich shops to assemble your lunch. One for the bread, one for the meat, one for the cheese? Ridiculous. You go to one deli. You get the combo. It's faster, cheaper, and it just makes sense.

So why do we treat our software stack any differently?

It's time to sober up.

The SaaS party was fun while it lasted — but now, it's time to clean house and consolidate. Your budget, your team, and your sanity will thank you.

Chris Webber is Director of Engineering at Formstack

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...

The Great SaaS Hangover (and the Cure Nobody Is Talking About)

Chris Webber
Formstack

We've all been there.

The morning-after fog. The pounding headache. The light sensitivity. The creeping existential dread.

You had a few too many last night.

It's not entirely your fault. The playlist was amazing. The dance floor was hopping. The drinks were flowing. It happens to the best of us.

What follows varies from person to person — and culture to culture. Reddit threads offer thousands of post-party remedies: a scalding hot shower, a punishing gym session, a greasy breakfast, or — for the bold — the infamous "hair of the dog." Some of these might help. Most don't. At the end of the day, everyone comes to the same conclusion: the only surefire way to avoid a hangover is to drink less in the first place.

And that brings us to the SaaS industry.

The SaaS Party That Went Too Hard

2020 was the equivalent of a wedding with a top-shelf open bar. As businesses scrambled to adjust to remote work, digital transformation accelerated at breakneck speed. New software categories emerged overnight. Tech stacks ballooned with all sorts of SaaS apps solving ALL the problems — often with little oversight or long-term integration planning, and yes frequently a lot of duplicated functionality.

Gartner estimated global SaaS spending hit $157 billion in 2020, and it hasn't slowed much since. Companies layered tools upon tools, often with overlapping functionalities, all in the name of agility and speed.

But now the music's faded. The lights are on. Everyone from the CIO to the CFO is checking the bill.

Welcome to the Great SaaS Hangover.

What Is a SaaS Hangover?

A SaaS hangover is the result of years of unchecked software adoption. It's marked by:

  • Redundant tools doing the same job in slightly different ways.
  • Ballooning software costs where every employee is another $$$ per month.
  • Disjointed user experiences that frustrate employees and reduce productivity.
  • Security and compliance risks from managing too many vendors and endpoints.

In fact, a 2023 Productiv report found that companies use an average of 371 SaaS apps, yet only 47% are actively used in any given 30-day period. That's like stocking your fridge with five brands of orange juice and drinking just one.

The Cure: SaaS Consolidation Through Horizontal Platforms

Here's the good news: unlike a gin-fueled hangover, the SaaS hangover does have a cure — and it's surprisingly simple: Shrink your stack. Consolidate your spend. Invest in platforms, not point solutions.

The smartest companies today are shifting toward horizontal platforms — tools that solve broad business problems across departments, rather than hyper-specialized point solutions. Think Notion over five separate productivity apps. Think HubSpot over a scattered mix of CRM, email, and marketing tools. Think Microsoft 365, not a patchwork of document editors, cloud drives, and meeting apps.

Why It Works

  • Lower cost: Bundled pricing often beats à la carte tools.
  • Simpler onboarding: Fewer tools means faster adoption and less training.
  • Better integration: Native connections across features reduce data silos.
  • Improved visibility: Centralized platforms offer unified reporting and analytics.
  • Stronger security: One platform means fewer vendors to vet and monitor.

And here's the kicker: consolidation doesn't mean compromise. Modern horizontal platforms are more robust than ever, often outperforming niche competitors while offering broader utility.

You Wouldn't Build a Sandwich This Way

Let's end with a metaphor as simple as it is relatable: you wouldn't go to three different sandwich shops to assemble your lunch. One for the bread, one for the meat, one for the cheese? Ridiculous. You go to one deli. You get the combo. It's faster, cheaper, and it just makes sense.

So why do we treat our software stack any differently?

It's time to sober up.

The SaaS party was fun while it lasted — but now, it's time to clean house and consolidate. Your budget, your team, and your sanity will thank you.

Chris Webber is Director of Engineering at Formstack

The Latest

Performance bottlenecks aren't uncommon when it comes to rolling out new technology, regardless of how capable or game-changing that technology might be. Every generation of new tech has encountered roadblocks that had to be overcome before it was truly able to shine. Virtualization forced organizations to rethink resource allocation, cloud transformation had us shift our focus toward scalability and elasticity, and microservices introduced entirely new challenges around observability and distributed systems. There's something different about AI, however ...

Consider a single order represented across order-management, execution, and settlement systems. Each database, message broker, and application may be online and processing its own records correctly. Yet the workflow has failed if related events arrive on different clocks, rely on inconsistent state, or cannot be reconciled before an operational decision must be made ...

AI now exists in almost every IT workflow. In a recent survey of more than 800 IT service professionals, all respondents indicated the use of AI in some form within their organization. But there's a growing paradox: if dashboards are clearing faster and alerts are resolved at unprecedented speed, why aren't IT service desks reporting lighter workloads? The research found that 71% of IT teams said their actual workload has remained flat or increased since adopting AI. This reality appears to contradict what we’ve been told about AI ...

Two years ago, almost every customer conversation about AI started with the same questions: Which model should we use? What can it do? Is it ready for the enterprise? Today, those discussions have moved on. CIOs are far more interested in how to govern AI, integrate it with existing systems, prepare their workforce and make it part of everyday operations. The challenge is no longer to prove that AI can deliver value. It's instead about how to embed AI into the business in a way that's secure, scalable and delivers measurable outcomes ...

Two things happened to production incidents between 2023 and now, and they did not happen at the same speed. The first is that a class of dependency that barely existed three years ago now accounts for one incident in ten. Incidents disclosed by AI model and AI application providers rose from 1.7% of all disclosed unplanned incidents in 2023 to 10.7% in 2026 year to date, roughly a sixfold rise; that counts only incidents at AI companies themselves, so the true share is higher. The second is that the time to close an incident has not come down ...

When an AI assistant gives an incomplete or incorrect answer, teams often blame the model. They adjust prompts, switch models, increase context windows or test a new retrieval strategy. However the model may not be a problem. In many enterprise AI workflows, the problem begins inside the document-ingestion pipeline ...

If you talk to any security or observability teams right now, they're all fighting the same fire: their tooling was built to ingest X, but their sources are pumping Y and soon to be doing Z. The knee-jerk reaction is always the same: we need more platform. However, this reaction is wrong. Let me explain why, because the solution to this problem is foundational, not financial. Instead of hurling yet more money at the problem, make sure you've done what's needed upstream ...

Rapid AI adoption and the unique ways AI workloads operate is redefining the scope and structure of what these teams must deliver. This shift is forcing organizations to rethink how they manage scale, automation, and control, according to The State of SRE and Platform Engineering 2026, a new report from Dynatrace ...

AI is usually talked about as a software tool, but it also depends heavily on the network behind it. Whether a company is using AI for chatbots, automation, monitoring, analytics, or employee support, all of that information has to move across the network in a reliable and secure way. That means AI is not just an application decision. It is also an infrastructure decision. Before organizations rush into AI, they should ask a simple question: Is our network ready to support it? ...

Enterprise AI often lacks governed access to where business processes actually execute. Without that access, AI agents may be able to reason, but they cannot operate reliably across enterprise workflows. For AI agents to effectively carry out workflows, they will require integration-layer context and controls. Organizations can implement these prerequisites by providing AI with managed access to the middleware layer ...