Skip to main content

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

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...

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

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...

Today's modern systems are not what they once were. Organizations now rely on distributed systems, event-driven workflows, hybrid and multi-cloud environments and continuous delivery pipelines. While each adds flexibility, it also introduces new, often invisible failures. Development speed is no longer the primary bottleneck of innovation. Reliability is ...