Key Takeaways

  • Dan Bender of Code & Co. points out that middle-market software targets often rely on Delphi fat clients where presentation and business logic layers intertwine, making standard continuous deployment impossible without a complete rewrite.
  • In heavily regulated sectors such as pharmaceutical software, code branches frequently sit seven to 10 years behind main trunk because minor releases trigger weeks of customer validation and regulatory compliance audits.
  • Kirby Montgomery argues that high deployment frequency is meaningless without tracking user adoption, requiring sponsors to pair engineering speed with concrete business outcomes.
  • Technical diligence is shifting left before the letter of intent, as sponsors look to identify architectural debt and compliance release bottlenecks before finalizing enterprise valuations.

Desktop Architecture Breaks Modern Engineering Benchmarks

Dan Bender points out that modern engineering metrics assume cloud-native architectures that rarely match what private equity sponsors buy. When deal teams evaluate middle-market B2B software, they infrequently encounter clean microservices. Instead, they find monolithic desktop applications written decades ago.

As Bender explains: “Realistically in private equity we still see desktop apps, right? We still see Delphi to still see fat clients where business layer so logic and and presentation layer UI are intertwined and ripping that apart would be pretty much a rewrite.”

Applying standard DORA metrics like deployment frequency or lead time to these environments misleads the deal team. In a fat client where the user interface and business logic are locked together, engineers cannot ship daily micro-updates. Untangling them requires a capital-intensive rewrite that sponsor underwriting rarely accounts for. If an operating partner attempts to force modern deployment frequencies onto a Delphi monolith, engineering velocity slows down as regressions multiply across the codebase.

In Regulated Software, Fast Deployment Is a Bug

Velocity metrics also collapse when applied to regulated enterprise verticals. Bender highlights pharmaceutical software, where customers cannot accept continuous deployment even if the vendor maintains automated delivery pipelines.

“When we look at pharmaceutical software that serves pharmaceutical companies for example, you see protected branches because there's so much customization and sometimes like seven to 10 years behind and that's terrible, right?” Bender notes. “On the other hand, it's the reality of an industry that's so massively regulated that even a minor update means weeks and weeks of compliance work.”

In these accounts, stability supersedes deployment frequency. Customers require long audit cycles before pushing any update into production. Pushing unvalidated code disrupts operations and breaches compliance mandates. A low DORA deployment score in these companies is not evidence of poor engineering; it reflects the friction built into enterprise procurement and compliance.

Speed Means Nothing Without Feature Adoption

For targets that can deploy quickly, speed alone remains a vanity metric. Kirby Montgomery stresses that engineering velocity must tie directly to customer outcomes rather than git commits.

“Stability and speed are what DORA is based on,” Montgomery observes. “Maybe speed is less important now and stability is the things that are out there that people care about.” He argues that measuring the output of the software factory without assessing market uptake creates false confidence during value creation.

“The thing that we're seeing is the best metric to combine now with DORA is the outcomes. Are you building features that matter and people use?” Montgomery asks. A portfolio company shipping twelve updates a week delivers zero enterprise value if end users ignore the new features. For private equity sponsors, measuring engineering efficiency requires tracking feature adoption and revenue impact alongside raw development cycle times.

Why It Matters

This reality signals a widening disconnect between tech-native operating playbooks and the realities of middle-market software buyouts. Valuations that assume rapid SaaS margin expansion fall apart when an asset requires an unbudgeted architecture rewrite or faces multi-week customer validation cycles. Sophisticated sponsors are moving technical due diligence before the LOI to price code entanglement and deployment friction directly into their opening bids.