Software Autonomy: A Cost Reassessment for Engineering Leaders

Source: Dev.to

SaaS became the default not because it produced optimal systems, but because custom software was costly to build and costly to maintain. For most teams, adapting processes to generalized tools was cheaper than owning software. That assumption no longer holds. AI materially changes the economics of internal software along two dimensions: build cost and maintenance cost. Both are now low enough that the traditional SaaS trade‑off needs reassessment.

Build Cost Is No Longer the Primary Barrier

• Historically, even modest internal tools required significant upfront investment.
• Typical figures: (e.g., months of engineering time, multi‑thousand‑dollar budgets).
• With AI‑assisted development: development cycles shrink dramatically, and required engineering effort drops.
• Observed outcomes across teams: faster delivery, lower cost per feature, and higher alignment with specific business needs.
• The threshold at which “build vs buy” favors building is materially lower than it was even two years ago.

Maintenance Is No Longer an All‑or‑Nothing Commitment

• Maintenance was the stronger argument for SaaS.
• Internal tools historically required ongoing patches, upgrades, and operational monitoring.
• SaaS centralized this burden and reduced organizational risk.
• That advantage is eroding as maintenance costs decline.

Open Source as Risk Reduction

• For limited‑scope tools, open‑sourcing internal software can spread the maintenance load.
• Maintenance still exists, but failure becomes less expensive because the community can contribute fixes and improvements.

Agent‑Assisted Maintenance

• AI systems are already handling parts of routine upkeep (e.g., log analysis, alert triage, automated refactoring).
• Early results show: reduced mean‑time‑to‑repair and lower human effort for repetitive tasks.
• Maintenance shifts from continuous effort to periodic review.

Implication for Engineering Leadership

• SaaS solved two problems: high build cost and high maintenance cost. AI reduces both.
• When internal software can be built quickly, modified cheaply, and maintained with limited ongoing effort, generalized tools lose their structural advantage.
• For many teams, owning small, focused systems is now the lower‑risk option.
• This is not a tooling preference change; it’s a shift in economic reality.
• Engineering leaders should revisit build‑vs‑buy decisions with updated assumptions, especially for:
  • mission‑critical workflows,
  • rapidly evolving product features,
  • compliance‑driven processes,
  • and any domain where customization yields measurable value.
• The default answer no longer needs to be SaaS.