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In early February 2026, an estimated $285 billion in software market was wiped out within days, in a sell-off the financial press dubbed the “SaaSpocalypse.” Headlines declaring that “SaaS is dead” followed. The trigger was a wave of AI agent launches, and the question behind it has not gone away: if software can be built faster and cheaper than ever, why keep paying a subscription for it?
It’s a fair question, and the honest answer sits between the two camps. One says SaaS is dead: if AI can build working software in an afternoon, why keep paying for a subscription? The other says SaaS is safe: vendors will add AI to their products, and customers will keep buying what they already trust. Both are partly right. So will AI kill SaaS? Not across the board, and not evenly. This article looks closely at both camps, and what buyers should weigh before they build, buy, or combine both.
According to GitHub’s research, developers can complete coding tasks up to 55% faster thanks to AI-powered tools. But faster coding doesn’t automatically mean better delivery. Google’s DORA research shows a mixed picture at the team level. Its 2024 report linked rising AI adoption to an estimated 1.5% drop in delivery throughput and a 7.2% drop in delivery stability. The 2025 edition found that AI tools no longer slowed delivery, but stability remained an issue. The speed gains are real, and with AI, companies can:
It’s important to understand that even though software development has seemingly become cheaper thanks to AI, it did not make software development free, it simply changed where the cost sits.
While coding takes less time and fewer people, ensuring that code is secure, reliable, and maintainable is just as important as ever. It’s just that software development itself is becoming less of a differentiator. Here is why:
Software engineering, contrary to what many believe right now, is not actually dying. The values are shifting, as well as the approach to how software is developed. AI-generated code still needs sound architecture, human judgement, security controls, quality assurance, and long-term maintenance. As more software is produced, poor engineering decisions can spread faster, too. It is important to distinguish between:
The strongest companies will combine AI-enabled development with these differentiating capabilities rather than simply trying to produce more code.
As AI makes software faster and potentially cheaper to develop, businesses are reassessing whether to replace SaaS with custom software built by an in-house team or an agency, or keep paying for standardized products. The decision is no longer just about the cost of acquiring software. It is about customization, control, integration, and the long-term value technology creates.
Off-the-shelf SaaS products are designed to serve a broad market. While they offer ready-made functionality, businesses with specialized workflows may need to adapt their processes to the tool or rely on workarounds. Custom software takes the opposite approach, allowing companies to build for their specific operations, prioritize the features they need, and respond to changing requirements without depending entirely on a vendor’s roadmap.
SaaS customers depend on providers for pricing, feature availability, release schedules, and service terms. Custom software gives businesses greater influence over how their technology evolves and how their data is managed, subject to the chosen architecture and applicable regulations.
These concerns are reflected in a 2025 BCG study of 500 IT decision-makers: 62% expressed concern about digital platform lock-in, while 70% were exploring alternatives.
Businesses often rely on multiple platforms for customer relationship management, content, analytics, and operations. When these systems do not work seamlessly together, employees may spend time transferring data, reconciling inconsistencies, and switching between applications.
Custom software can connect existing tools, unify key workflows, and reduce operational friction. However, integrations require ongoing maintenance, and their complexity can create costs regardless of whether a company chooses SaaS or custom development.
SaaS typically requires less upfront investment, but subscriptions, per-user fees, add-ons, and integration costs can accumulate as a business grows. Custom software requires investment in discovery, development, testing, infrastructure, and ongoing maintenance, but may offer better long-term economics when specialized requirements or operational inefficiencies justify the expense. A hybrid approach sits between the two: a lower upfront cost than a full build, with custom spend limited to the processes that need it.
There is no universal break-even point. Standard functions such as payroll and accounting are often better served by established SaaS products, while specialized processes may justify a tailored solution.
As AI lowers some development barriers, businesses must consider where technology creates competitive advantage. The value may lie less in the application itself and more in the proprietary data, industry expertise, workflows, and integrations that make it useful. For many organizations, a hybrid approach, like buying standard capabilities while building around strategically important processes, may offer the best balance of cost, flexibility, and business value.
The software is the part you see when you sign a SaaS contract. The rest is easy to overlook. Someone is hosting it, patching it, keeping it compliant, and doing numerous other tasks to keep things running smoothly. Here is what goes beyond just the software:
Infrastructure, maintenance, security, scalability, compliance, reliability, updates
Hosting, patching, security monitoring, cloud computing, and capacity planning and capacity planning all need dedicated staff. Most business cases for building in-house leave that cost out. A SaaS vendor spreads that cost across every customer. Scaling for a launch week becomes a setting, not a project, and compliance (SOC 2, ISO 27001, GDPR) is maintained as regulations change and differ from market to market. Reliability comes with uptime commitments, disaster recovery, support, and regular updates, with no upgrade projects or downtime windows. A quick build can copy a feature, but it can’t copy years of earned trust.
Specialized workflows, domain knowledge, integrations
A vendor that has watched hundreds of teams do the same job bakes best practices into its workflows. Generic tools can be configured to do almost anything but understand nothing about your field. A specialized platform already knows your terminology, approval steps, and pain points, and the data from millions of real interactions feeds benchmarks and AI models that a new entrant can’t replicate. Integrations matter just as much. Connecting to your CRM, digital asset management system, or approval systems is where custom builds most often stall, while a mature platform arrives with those connections tested and keeps them working as other tools change. Each partner and integration also makes the ecosystem more valuable.
Faster implementation, time-to-value
A custom build means scoping, development, testing, and rollout, then ongoing ownership and maintenance, with real risk of overruns. SaaS onboarding typically takes weeks, and a product shaped by thousands of users removes friction that a one-off build never will. Teams can pilot with one market or department, prove the value, then expand.
As AI makes code cheap to build, software itself is no longer the moat. SaaS differentiates through what surrounds the code: proprietary data from millions of real interactions, workflows and domain expertise built over years, tested integrations and a growing ecosystem, and compliance and security credentials that take time to earn.
Projects that survive any technological advancements are the ones that can actually integrate those innovations into their capabilities and continue providing their users with value. The table below compares the SaaS products most exposed to AI disruption with those more likely to last.
Life sciences makes the case clearly. Content must pass MLR review, carry an audit trail, and work across channels and markets, so confidence in the result matters more than speed of build. That is why platforms like eWizard and eVa AI are built around compliance from the start.
Your content has to clear MLR review, keep an audit trail, and work across every channel and market. eWizard and eVa AI take care of that, so your team can spend its effort on the parts worth building.
A simple test shows where your own product or platform stands: could a capable team rebuild it in a few weeks, and would anyone trust the result? If so, it is exposed. If the real value lies in data, compliance, integrations, or trust, it isn’t.
Most companies will do both. The useful question is which parts of your content operation a platform should handle, and which parts are worth building yourself.
AI-native products have AI capabilities built into their core. Instead of sitting beside the workflow, the model runs it, helping the team with tasks like drafting, deciding, routing, and numerous others. It learns from every interaction, and every next output it produces is better than the last one. This has changed the buyer’s expectations. Development has shifted in step, with shorter release cycles, and the speed at which a product improves is now something customers judge.
Building a system around an AI model sounds ideal, and customers like it. But what about a product that already exists without AI? Adding an AI assistant to an existing product is the fastest response, and the easiest to copy. Bolt-on AI also inherits the old product’s limits. Lasting advantage comes from what the model can’t supply on its own:
Most vendors now claim to be “AI-powered”, but it is reported that only 36% of SaaS companies said AI is core to their product. For the rest, it’s a supporting feature. Use these questions as a buyer:
Most organizations land on a mix of approaches: a proven platform for the core, with custom layers where proprietary data or processes create a real edge.
SaaS isn’t dying, but it is being redefined. As AI makes code easier to produce, the evidence points in both directions. Research found that 76% of enterprise AI use cases are now bought rather than built, while other research reports that 32% of organizations skipped a software purchase to build with coding agents.
The likely outcome is a hybrid. Proven platforms will carry the core, and custom layers will appear where a company’s own data or processes create a real edge. Advantage will come from what code alone can’t supply:
Life sciences makes the case clearly. Content must pass MLR review, carry an audit trail, and work across channels and markets, so confidence in the result matters more than speed of build. That is the thinking behind Viseven’s eWizard and eVa AI, which combine modular content architecture with AI, built around pharma’s compliance requirements from the start.
Every content operation is different. Tell us where yours stands, and we’ll help you work out which parts a platform should handle and where a custom layer would give you a real edge.
SaaS, or software as a service, is software that a vendor hosts and maintains in the cloud, and customers access over the internet through a subscription instead of installing and running it themselves. The vendor handles hosting, security, updates, and support, and customers typically pay per user, per month or year, or by usage. Common examples include CRM platforms, project management tools, accounting software, and content management systems.
No. SaaS is changing, not disappearing. IDC’s European practice described the shift as a transformation, not an end. IDC’s European practice framed it as SaaS metamorphosing rather than dying. Thin tools that do one narrow job are under pressure, while platforms built on proprietary data, domain workflows, compliance, and integrations remain hard to replace.
In part. Agents can take over simple tasks such as drafting, summarizing, and tagging, and they put thin tools with little proprietary data at real risk. Simple workflow apps and thin UI layers are exposed, while platforms in complex domains with compliance requirements and deep integrations are much harder to replace. In most cases, agents work through software, using the platform’s data, permissions, and audit trail, rather than replacing it.
Buying is usually cheaper upfront, and building can be cheaper over time in specific cases. SaaS typically costs less to start, but subscriptions, per-user fees, add-ons, and integrations add up as a business grows. Custom software needs investment in discovery, development, testing, infrastructure, and maintenance, so it pays off mainly when specialized requirements justify it.
A hybrid approach means buying a SaaS platform for standard capabilities and building a custom layer for the processes that set your business apart. The common pattern is to buy commodity systems, then build a thin custom layer, such as integrations, dashboards, or portals, on top of their APIs. The trade-off is that the custom layer still needs maintenance, especially when the vendor changes its product or API.
