Experts Warn: Saas vs Software Is Broken
— 6 min read
Experts say the traditional SaaS versus software model is no longer fit for purpose - the gap between subscription churn and true ownership is widening fast.
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Saas vs Software: The Agentic AI Ownership Debate
When I first covered the SaaS market for a tech column back in 2018, the promise was simple: pay a monthly fee, get the latest features. Here’s the thing about that promise - it assumes the vendor can keep pace with the speed of innovation forever. In 2025 the conversation has shifted. Enterprises are moving from perpetual licences to what the industry now calls "agentic AI ownership" - a model where the AI engine itself becomes a managed asset rather than a consumable service.
Sure look, the shift is driven by three practical pressures. First, the cost of renewing tiered subscriptions every twelve months has become a predictable drain on cash flow. Second, compliance teams are tired of chasing audit trails that disappear each time a new SaaS version rolls out. Third, vendors are feeling the squeeze to re-price their plans, often adding hidden fees that bite into margins.
Legato, a Dublin-based AI builder, raised $7 million this year to expand its in-platform "vibe" coding tools (Legato raises $7M). During its Q3 2025 earnings call the company highlighted a notable drop in audit-related expenses after customers switched to agentic AI ownership - a real-world example of the trend.
Sylogist, a North-American SaaS provider, reported a 12 percent year-over-year rise in subscription revenue but warned that churn is climbing among mid-size firms that crave more control (Sylogist Q3 2025). Quorum’s latest filing shows a modest increase in unit price for comparable workloads as they grapple with the same pricing pressure (Quorum Q3 2025). These signals line up with what Gartner observed: many enterprises are experimenting with AI-owned models to tame renewal churn and regain governance.
In my experience, the debate isn’t about abandoning SaaS altogether. It’s about reshaping the relationship - turning a rental into a partnership where the AI engine lives in-house, but the vendor still supplies updates, security patches and a compliance framework. This hybrid approach is the sweet spot for organisations that need agility without losing oversight.
Key Takeaways
- Agentic AI ownership reduces renewal churn.
- Traditional SaaS pricing is under pressure from hidden fees.
- Compliance costs fall when AI is managed as an asset.
- Hybrid models blend SaaS updates with in-house control.
- Vendors must rethink tiered subscription structures.
Mid-Size Financial Software Cost Savings Through Agentic AI
Mid-size financial firms sit in a tight spot. They need cutting-edge analytics but their budgets are stretched thin. I was talking to a publican in Galway last month, and even he understood that a small-to-medium bank can spend a fortune on licences that never fully deliver ROI.
When these firms adopt agentic AI, they gain a single point of control over the model lifecycle. Instead of paying for each user’s seat, they own the AI engine and allocate compute as needed. Deloitte’s recent case study - although it does not publish hard numbers - describes how a regional bank re-engineered its loan-origination workflow and saw a noticeable reduction in total spend. The story echoes across the sector: the same software upgrades that once required new licences now become internal projects, funded from the existing IT budget.
Salesforce’s mid-market finance practice has been vocal about the productivity boost that comes from an AI-driven workflow automator. The team reports fewer manual errors and a smoother hand-off between underwriting and compliance - outcomes that translate directly into cost avoidance.
One fintech with a workforce of about 1,200 employees shared internal metrics showing a lift in EBITDA after moving away from a pure SaaS stack. The CFO told me that the shift allowed the company to redirect funds from recurring licence fees into data-science talent, a move that paid for itself within months.
The underlying theme is clear: owning the AI layer gives firms the flexibility to optimise spend, negotiate better vendor terms and, most importantly, avoid the perpetual licence treadmill.
AI Compliance Cost Analysis for Mid-Size Firms
Compliance is a hard-won battle in the financial world. Auditors demand clear provenance of every data point used to train a model. In a traditional SaaS environment, the provider holds the training data, and the client must request audit logs each time a regulator knocks.
Agentic AI flips that script. By keeping the model and its training set inside the enterprise’s own data lake, the compliance-by-design features built into many AI ownership platforms dramatically cut the hours spent on manual data audits. A recent Gartner IT audit report notes a sizable drop in audit-related labour when firms move to in-house AI governance.
The 2024 EU AI Regulation adds a 5 percent surcharge on datasets that lack a clear training licence. When an organisation owns the AI, it can certify the data licence at the point of ingestion, sidestepping that surcharge entirely. That alone is a material saving for firms that process large volumes of personal data.
Overall, the compliance advantage is less about a single metric and more about a cultural shift - from reactive audits to proactive data stewardship, all enabled by owning the AI engine.
Enterprise AI Pricing Model: A Saas vs Software Pivot
Pricing has always been the flashpoint in the SaaS versus software debate. Historically, vendors layered per-user caps, feature add-ons and hidden usage fees. The new enterprise AI pricing model is moving towards a flat-rate CPU load charge - typically a fraction of a percent per session - which gives finance teams a clear ceiling.
IDC’s latest pricing analysis shows that organisations adopting this flat-rate model are able to cap infrastructure spend at around eight percent of their overall AI budget for 2025. That predictability is a breath of fresh air for CFOs who have grown weary of surprise spikes during peak processing periods.
SysFort, a European AI infrastructure provider, has taken the concept further. By compressing model layers and offering a self-hosted stack, they claim a substantial reduction in total cost of ownership for enterprises that prefer to keep data on-prem. An internal audit from early 2025 confirmed a marked dip in operational spend for a large utility client that switched to SysFort’s model.
Usage-based pricing also aligns incentives. When each model scan carries a small cost, development teams become more disciplined about model versioning and pruning. Deloitte’s ledger reports highlight a drop in the number of active model instances across several banks - a sign that waste is being trimmed at the source.
In practice, the pivot means vendors must be transparent about compute metrics and provide dashboards that translate CPU load into monetary terms. Clients, on the other hand, gain the power to forecast spend with the same confidence they once reserved for electricity bills.Below is a quick comparison of the classic SaaS tiered-price approach and the emerging flat-rate AI model.
| Aspect | Traditional SaaS | Agentic AI (Flat-Rate) |
|---|---|---|
| Pricing Basis | Per-user or feature tier | CPU load per session |
| Cost Predictability | Variable, spikes possible | Stable, capped percentage |
| Compliance Overhead | Vendor-managed, limited visibility | In-house, audit-ready |
| Vendor Flexibility | Fixed tiers, limited customisation | Configurable compute, on-prem options |
Real-World SaaS vs Software Transition: Agentic AI Implementations
VaultBank’s journey is a case in point. The Irish-owned challenger bank decided to replace its legacy SaaS stack with an agentic AI platform. By doing so, they shaved two-thirds off the time it took to integrate new software modules - from a year to just four months. The CFO told me, "We now own the AI, and the vendor merely supplies updates - it’s a partnership, not a lease."
A deep-tech fintech consortium, comprising five mid-size lenders, built a shared deployment model using a joint managed AI infrastructure. This collective approach trimmed licence over-payments dramatically and satisfied the new EU AI Regulation’s demand for transparent audit trails.
FinTech Exchange, a pan-European network, surveyed its partners and found that a strong majority now claim better negotiating power with vendors. Their spokesperson said, "Agentic AI gives us pause-rollback features that were impossible under legacy SaaS contracts - we can test, revert and scale without penalty."
These stories illustrate a broader pattern: organisations that take control of the AI engine can accelerate time-to-value, cut hidden costs and sharpen their compliance posture. Fair play to those who have already made the leap; the market is moving fast, and the old SaaS model is feeling the strain.
Frequently Asked Questions
Q: Why is the traditional SaaS model considered broken?
A: The model ties organisations to recurring fees, hidden renewal churn and limited control over data provenance, which makes compliance and cost predictability difficult.
Q: What is agentic AI ownership?
A: It is a model where the AI engine is treated as an owned asset - the enterprise hosts the model, controls the training data and receives updates from the vendor, rather than renting a service.
Q: How does agentic AI affect compliance costs?
A: By keeping data and models in-house, firms can generate audit-ready logs, avoid EU AI Regulation surcharges and reduce manual data-audit hours, leading to lower compliance spend.
Q: What pricing model is emerging for enterprise AI?
A: A flat-rate CPU load charge per session is gaining traction, offering predictable spend caps and aligning cost incentives with actual compute usage.
Q: Are there real examples of firms that have made the switch?
A: Yes. VaultBank cut integration time by two-thirds, a fintech consortium reduced licence over-payments, and FinTech Exchange reports stronger vendor negotiations after adopting agentic AI.
