In 2026, the M&A tech due diligence process extends beyond the codebase to include product architecture, cloud infrastructure, security controls, data handling practices, IP and licensing, and how engineering teams develop, deploy, and support the product over time.
Increasingly, it also includes evaluating AI and machine learning layers, from model architecture and training data provenance to third-party model dependencies and performance reliability. Furthermore, this means the operational and regulatory risks tied to AI-enabled products will also be contemplated.
What is technology due diligence in SaaS M&A
Technology due diligence in SaaS M&A is a big part of the general business diligence, where the goal is to test whether the technology platform can support the deal’s investment thesis, and at what cost and risk.
In practice, buyers use technology diligence to answer a small number of commercially critical questions:
- Can the platform scale without re-architecting core systems?
- Will infrastructure and cloud costs behave predictably as usage grows?
- Are there security, data, or IP issues that could delay closing or limit future customers?
- How dependent is execution on a handful of individuals rather than repeatable systems and processes?
The findings may ultimately influence the valuation, the deal terms, and of course, the post-close priorities.
What buyers may review in a SaaS tech due diligence
Experienced acquirers evaluate a SaaS business across several areas that explain how the company may operate at scale. For founders, this means proving that the system, as a whole, can support the next phase of the business.
Here are the core areas that buyers typically examine:
#1 AI and machine learning: Is the intelligence layer defensible and reliable?
As AI becomes embedded across SaaS products, buyers now start diligence by evaluating the intelligence layer itself, when applicable, not just how it works technically, but whether it is differentiated, scalable, and safe to own.
This is especially relevant where AI drives core product value, automation, decision-making, or customer experience. In these cases, understanding the quality, ownership, and risk profile of AI capabilities becomes as important as reviewing the software codebase.
They typically look at:
- Model architecture and design. What types of models are deployed, how they are trained or fine-tuned, and whether the company builds proprietary models or primarily orchestrates third-party ones.
- Training data provenance and ownership. Where training data comes from, whether it is licensed, consented, or proprietary, and whether it creates defensibility or legal exposure.
- Third-party model dependencies. Reliance on external LLMs or AI APIs, exposure to pricing changes, performance shifts, or vendor restrictions, and how portable those capabilities are if providers change.
- Performance and reliability. Accuracy, hallucination risk, bias mitigation, explainability, and how outputs are monitored and improved over time, particularly in customer-facing or decision-critical workflows.
- Operational scalability. Compute requirements, inference costs, latency, and whether infrastructure and MLOps practices can scale economically as usage grows.
- Regulatory and legal exposure. Alignment with emerging AI frameworks (such as the EU AI Act), copyright considerations, data consent, and risk tied to automated decision-making.
Recommendation for founders: Prepare a clear overview of your AI stack. Therefore, map what is proprietary versus third-party, how models are trained, what data is used, and how performance, cost, and risk are managed in practice.
#2 Product architecture: Can the product scale?
Product architecture diligence is about one thing: whether the product can grow with the business, or whether growth will force a major rebuild.
Buyers want to understand how the product is put together and whether it can handle more customers, more usage, and a broader roadmap without becoming fragile or slow to change.
They typically look at:
- How the product is structured. If different parts of the product can change independently, or if updating one feature tends to break others.
- APIs and integrations. How easy it is to integrate the product with customers, partners, or an acquirer’s existing systems.
- Technical debt and dependencies. Whether some older components or dependencies make the product harder to maintain, secure, or extend.
- Fit with where the business is going. Whether the current setup supports moves into enterprise customers, new regions, or additional products.
Recommendation for founders: Keep a simple architecture diagram and be able to explain, in plain terms, how the product supports growth without needing a major rebuild.
Also read: How AI has changed due diligence?
#3 Infrastructure and cloud: Will it hold up at scale?
Infrastructure diligence is about whether the system can stay reliable as the business grows and more importantly, whether the cost of running it stays under control.
Buyers want to understand how the platform behaves under pressure: more users, higher usage, new regions, and occasional failures.
They typically look at:
- Where the product runs. Which cloud providers and regions the business relies on, and whether those choices make sense given the customer base and growth plans.
- How the system scales. Whether the platform can handle spikes in usage and continued growth without outages or performance issues.
- How environments are managed. Whether infrastructure is set up in a consistent, repeatable way, or whether it relies on manual work and individual knowledge.
- Backup and recovery. How quickly the company could recover from a serious incident, and whether recovery plans have actually been tested.
- Cloud costs. How infrastructure costs change as usage grows, and whether leadership understands what drives those costs.
Recommendation for founders: Be ready to explain how your system scales and how cloud costs behave as usage grows, using simple business terms versus technical details.
Read: How M&A Advisors Prepare a Founder's Exit Strategy
#4 Security and risk: Are there deal-blocking gaps?
In terms of technical security, buyers are not looking for a flawless program, but they need to trust that founders are clear on the risks and that there is a plan to address them in case they arise.
For a SaaS M&A, security diligence may also focus on whether the company’s controls meet the expectations of external partners, including enterprise customers and regulators.
Buyers typically look at:
- Basic security setup. How access, applications, and data are protected in practice.
- Who can access what. How permissions are granted, reviewed, and removed as roles change.
- How issues are handled. Whether vulnerabilities are identified and fixed consistently, not only after incidents.
- Incident readiness. Whether the team knows how to respond if something goes wrong, and whether that process has been tested.
- Compliance expectations. Whether the company broadly meets customer or regulatory requirements and understands any gaps.
Recommendation for founders: Prepare a short security summary explaining key risks, past incidents (if any), and how issues are addressed.
#5 Data and compliance: Can customer data be handled safely?
Data diligence looks at how customer data is handled day to day, where it lives, who can access it, and how well privacy obligations are handled across customers and regions.
They typically review:
- How data moves through the system. Where customer data is stored, processed, and shared.
- Customer data separation. Especially in multi-tenant systems, whether one customer’s data is clearly isolated from another’s.
- Privacy and consent. How consent is collected and how data requests are handled in practice.
- Regulatory exposure. Which rules apply, and if the current practices align with them. This is particularly important in cross-border M&A deals, where the buyer and seller might be in different regulatory landscapes.
- Retention and deletion. Whether data can actually be deleted or anonymized when required.
Recommendation for founders: Have a simple data overview and be ready to explain how customer data is stored, isolated, retained, and deleted.
#6 Software delivery: Can the team ship reliably?
Software delivery is about whether the team can keep building and improving the product after the deal, without constant fire drills. In other words, what does execution consistency look like?
They typically assess:
- How changes are shipped. Whether deployments are frequent and controlled, or infrequent and risky.
- How issues are caught. How the team detects and responds to production problems.
- Environment consistency. Whether development, testing, and production environments are predictable and aligned.
- Change discipline. How changes are reviewed and rolled out, especially for larger customers.
Recommendation for founders: Be able to explain how often you ship, how issues are caught, and how quickly the team can recover from problems.
#7 Engineering team: Is execution people-dependent?
This part of diligence tries to rule out that success depends on a few individuals, and if knowledge and responsibility are spread across the team.
They focus on:
- Team structure and coverage. Whether skills are distributed or concentrated in a few people.
- Key person risk. Where critical knowledge lives and whether it’s documented.
- Use of contractors or offshore teams. How dependent the business is on external contributors.
- Leadership depth. Whether there is more than one technical decision-maker.
Recommendation for founders: Be ready to explain who owns what, how knowledge is shared, and how the team operates without single points of failure.
#8 Intellectual property: Is ownership clean?
IP diligence confirms that the buyer is actually acquiring the technology it thinks it is. This is one of the few areas where small gaps can quickly become big problems.
Buyers typically review:
- Who owns the code. Whether the company (not individuals) owns the core software.
- Employee and contractor agreements. Whether IP has been properly assigned.
- Open-source usage. Whether licenses are understood and compliant.
- Third-party dependencies. Whether any components limit future use or transfer.
Recommendation for founders: Make sure IP assignments, contractor agreements, and open-source usage are clearly documented and easy to explain.
Read: The M&A NDA: What Matters For SaaS Companies
#9 Vendor dependencies: Where does the business rely on others?
Every SaaS business depends on third parties, but buyers want to understand where that reliance creates risk.
They typically look at:
- Critical vendors. Cloud providers, data services, security tools, and embedded APIs.
- Concentration risk. Where one vendor is hard to replace.
- Contract terms. Pricing changes, renewal timing, and termination rights.
- Switching difficulty. How hard it would be to move away if needed.
Recommendation for founders: Keep a clear list of critical vendors, contracts, and renewal timelines, with a basic view on alternatives.
#10 Integration readiness: How hard will post-close integration be?
Finally, buyers assess how the technology will fit into their broader environment. Even when the product remains standalone, some level of integration is almost always required.
They evaluate:
- System compatibility. How APIs, data access, and authentication work.
- Infrastructure alignment. Whether the platform can adapt to the buyer’s preferred setup.
- Data migration complexity. Especially for analytics, billing, or reporting.
- Constraints tied to the deal thesis. Any technical limits that affect growth or integration plans.
Recommended: Be prepared to explain likely integration paths and where complexity exists, before buyers assume the worst.Common red flags in a technology due diligence process
Common red flags in a technology due diligence process
Technology rarely derails a SaaS transaction in isolation, but it can become decisive where integration feasibility, scalability constraints, or remediation costs materially impact the buyer’s ability to realize value.
Below are several technology-related red flags that may surface during SaaS technical due diligence and influence buyer risk assessment.
Unclear ownership of core technology
This usually traces back to the early days, when founders were building fast, contractors helped informally, and documentation lagged. Even if no one disputes ownership, gaps or inconsistencies can create enough uncertainty to slow a deal.
Limited ownership or defensibility of AI capabilities
As AI becomes more important for product differentiation, buyers assess whether the intelligence layer is truly proprietary or largely reliant on third-party models. Risk emerges where training data rights are not well established, fine-tuned models cannot be transferred, or AI functionality depends heavily on external providers, creating exposure around portability, pricing, and long-term defensibility.
Missing or incomplete PIIA agreements with employees
Buyers expect every employee to have a Proprietary Information and Invention Assignment (PIIA) agreement. When these are missing or incomplete, it often triggers legal clean-up in the middle of deal negotiations.
Architecture that doesn’t scale with the growth story
The product may work well today, but has its limits as usage grows. Buyers get cautious if scaling requires major rework and not just incremental improvements.
Heavy customer-specific customization
Extensive customization makes products harder to maintain, secure, and scale, and raises questions about long-term efficiency.
Cloud costs growing faster than revenue
Rising infrastructure costs are expected, but what actually concerns buyers is when spending becomes unpredictable or outpaces growth, putting margins and unit economics under pressure.
Security controls that exist mostly on paper
Buyers look for evidence that security best practices are implemented and maintained daily.
Key-person dependency
When critical knowledge for the business lives with one or two people, the continuity risk increases.
Pre-sale preparation: How to find the right M&A advisor
How technology diligence impacts valuation and deal structure
As mentioned before, although technology diligence does not typically determine deal viability in isolation, it can influence how buyers assess risk as the full due diligence progresses.
When diligence reveals areas that may require more time, capital, or focus after closing, buyers naturally start to reconsider how confident they feel about the business.
As a founder, what matters is being able to clearly explain how the technology you’ve built supports growth, where it needs investment, and why the gaps or risks are manageable. And when that story is well articulated, the tech diligence becomes a confirmation of the deal you want rather than a lever for renegotiation from the acquirer.
How business owners should prepare for technology due diligence
Technology due diligence tends to move quickly once a deal is underway. Founders who prepare early are better positioned to protect the valuation.
Here's a checklist of what you need to take into account to be ready:
Managing the technology narrative in a sale
In 2026, technology diligence is ultimately an assessment of execution confidence. Buyers can accept a certain level of risk, but they discount opacity, unpriced AI exposure, infrastructure inefficiencies, and integration complexity. Well-prepared founders shape how technology risk is interpreted, valued, and integrated into the transaction.
At L40°, we work with founders to ensure technology diligence supports the deal by translating technical realities (from architecture and data foundations to AI capabilities and operating maturity) into a clear, credible narrative aligned with the investment thesis. Contact us.
