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Custom Software vs SaaS Subscriptions: A 5-Year Total Cost Comparison

SaaS feels cheap until year 3. Custom feels expensive until year 5. The 5-year TCO comparison across three scenarios, plus the decision framework we use with clients.

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Partha Sarathi Ghosh

Partha Sarathi Ghosh

Founder & Engineering Lead · 3 min read · May 23, 2026

Should I build custom software or buy SaaS?

You should buy SaaS if you're under 30 users, your workflow is standard, the SaaS does 80% of what you need out of the box, and you don't need deep customization. You should build custom if you have over 50 users on a per-seat SaaS, your workflow is differentiated, the SaaS forces you to adapt your business to its model, or you're paying for 4+ overlapping SaaS tools and stitching them together is eating into productivity. The crossover is rarely about technology — it's about the math of compounding subscription costs against the one-time cost of building. Per-seat SaaS at $20/user/month is $4,800/year for 20 users, $24,000/year for 100, $120,000/year for 500. A custom replacement of that same tool runs $40k–$80k once. The crossover point at modest growth is typically year 2–3.

How SaaS subscription costs compound

SaaS pricing is engineered to scale linearly with your business. That's good for vendors and bad for customers — every hire you make is a tax to your SaaS stack. At 10 seats, a $40/seat SaaS costs $4,800/year. Manageable. At 50 seats, the same SaaS costs $24,000/year. Noticeable. At 200 seats, it's $96,000/year — more than a senior engineer's salary, just for a tool that doesn't quite fit your workflow. The compounding effect isn't just price-per-seat. It's also the price-per-seat going up over time (industry standard: 5–10%/year), the tier breakpoints that force upgrades, and the per-feature add-ons that creep into the bill (SSO, audit logs, API access, advanced analytics — each $10–$30/seat/month). By year 5, your SaaS bill is rarely under 2× your year-1 spend.

When SaaS always wins (and when it doesn't)

SaaS always wins for commodity utilities — payments (Stripe), email (Postmark/SendGrid), auth (Clerk/Auth0), error tracking (Sentry), analytics (Mixpanel/PostHog), search (Algolia). These are deeply solved problems where the vendor's product is engineered better than anything you'd build in-house in less than $500k. We default to SaaS for all of these on every project we ship at Custom Software Development. SaaS loses when the product is your business workflow — your CRM (because how you sell is the business), your operations dashboard (because how you operate is the business), your customer-facing product (because the customer experience is the business). The pattern: SaaS for the commodity layer, custom for the differentiator. If you're confused about which is which, that's exactly the kind of question we work through in CTO-as-a-Service engagements — it's usually the highest-leverage decision a founder makes before product-market fit.

How do I calculate my own crossover?

Five-minute formula: Custom wins when (Annual SaaS cost × Years × 0.85) > (Custom build cost + Annual custom maintenance × Years). Plug in your numbers. Annual SaaS cost is current bill × your growth factor over the period. The 0.85 accounts for SaaS price increases (5–10%/year) and tier upgrades that you'll hit. Custom maintenance is typically 15–20% of build cost per year. Worked example: 50 users on a $40/seat tool. Year-1 SaaS: $24k. 5-year SaaS with growth + increases: ~$165k. Custom build: $60k. 5-year maintenance: $50k. Total custom: $110k. Custom wins by $55k over 5 years. The math gets more dramatic at scale. The math flips the other way under 20 users or in the first 18 months. Numbers don't lie — but they do require honest growth assumptions. We're happy to run this calculation for free in a discovery call.

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Partha Sarathi Ghosh

Written by

Partha Sarathi Ghosh

Founder & Engineering Lead, DevOrbital

Partha leads DevOrbital, where his team has elevated 50+ businesses across MVP development, AI agents, custom software, and growth. He writes about the hidden mechanics of getting AI-generated code into production, MVP scope discipline, and the architecture decisions founders make too late.

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