Contents
- The build-versus-buy decision has been one of the most reliable frameworks in technology strategy for three decades.
- Gartner's build-versus-buy decision matrix weighs five factors: cost, time-to-value, competitive differentiation, control, and ongoing maintenance.
- The build-versus-buy calculus changed because of three converging factors.
- The 2026 build-versus-buy analysis needs updated inputs.
Published: February 2026 | Stealth Labz | Search Intent: Commercial Investigation Keywords: build vs buy software 2026, custom software vs SaaS decision, build or buy software analysis
The Setup
The build-versus-buy decision has been one of the most reliable frameworks in technology strategy for three decades. Gartner's decision framework, Forrester's Total Economic Impact methodology, and every CTO's mental model share the same basic logic: building custom software is expensive, slow, and risky. Buying SaaS is cheaper (per month), faster (immediate), and lower risk (someone else maintains it). Therefore, buy unless you have a very specific reason to build.
This framework was correct. For most of the last 20 years, building meant hiring a development team at $150,000-$250,000 per engineer per year, scoping a 12-18 month project, and accepting that you would ship late, over budget, and with fewer features than planned. McKinsey's digital transformation data showed that 70% of custom software projects failed to deliver their intended business value. The risk-adjusted cost of building was, for most organizations, prohibitive.
The buy side was not perfect -- vendor lock-in, data fragmentation, integration overhead, price escalation -- but these costs were predictable and manageable. A $1,500/month SaaS stack was a known quantity. A $1.5 million custom build was a gamble.
In 2026, the inputs to this equation have changed. The cost of building has dropped by orders of magnitude. The risk profile has shifted. The compounding benefits of owned infrastructure have become measurable. The old framework produces the wrong answer for an increasing number of organizations -- not because it was flawed, but because the data it was calibrated on no longer applies.
What the Data Shows
The External Framework
Gartner's build-versus-buy decision matrix weighs five factors: cost, time-to-value, competitive differentiation, control, and ongoing maintenance. Historically, "buy" won on cost and time-to-value while "build" won on differentiation and control. Maintenance was a wash -- SaaS maintenance is someone else's problem, but vendor management and integration maintenance carry their own costs.
Forrester's Total Economic Impact methodology adds risk-adjusted ROI and timeline discounting. Under Forrester's model, the faster time-to-value of SaaS typically dominates, because custom builds that take 12-18 months lose significant discounted value during the build period.
McKinsey's digital transformation data provides the failure rate context: 70% of digital transformations fail to reach their stated goals. For custom builds specifically, the rate is higher -- Standish Group's CHAOS data shows that only 31% of software projects are considered successful (on time, on budget, with required features).
These frameworks all assume a specific cost-and-risk profile for building that no longer holds when AI-assisted development under a compounding methodology is the build approach.
The Internal Evidence: Build Costs Have Collapsed
Case 1: The Flagship Platform (PRJ-01)
A multi-vertical operations platform replacing 6 SaaS vendors. 135 database tables. 20 external integrations. Multi-tenant architecture. 616,543 leads processed.
- Market replacement value (US mid-market): $780,000-$1,560,000
- Market replacement timeline: 12-18 months
- Actual build cost: $16,800
- Actual build time: 74 active days
- Cost multiple: 46x-93x less than market rate
Case 2: The Full Portfolio
10 production systems across 7 verticals and 2 geographies. 596,903 lines of custom code. 2,561 commits.
- Market replacement value: $1,445,000-$2,890,000
- Market replacement timeline: 12-24 months (with a full team)
- Actual build cost: $65,394 (QB-verified)
- Actual build time: 116 calendar days
- Cost multiple: 22x-44x less than market rate
Case 3: The SaaS Displacement
6 SaaS platforms replaced with owned infrastructure. $1,565/month in subscriptions eliminated. $62,731 in annual contractor costs eliminated. Total annual displacement: $82,640.
Monthly operating cost went from $6,312/month average to $825/month. The "buy" option was costing $75,744/year in subscriptions plus contractor overhead. The "build" option cost $16,800 once and $825/month ongoing.
Gartner's Five Factors, Revisited
| Factor | Traditional Build | AI-Assisted Build (CEM) | Buy (SaaS) |
|---|---|---|---|
| Cost | $780K-$2.9M | $16,800-$65,394 | $19,909-$75,744/year |
| Time-to-value | 12-24 months | 5-74 days | Immediate |
| Differentiation | High | High | Low |
| Control | Full | Full | None |
| Maintenance | Team required | Solo + AI ($105/mo) | Vendor-managed |
The traditional build-versus-buy analysis compared a $1M+ build against a $20K/year SaaS cost. Build lost on cost by 50x. In the updated comparison, a $16,800-$65,394 build competes against $20K-$76K/year in SaaS costs. Build wins on cost in year one -- and the gap widens every year because the build is a one-time investment while SaaS is recurring.
Time-to-value flips as well. PRJ-04 shipped in 5 active days. PRJ-03 shipped in 9 days. These timelines compete with SaaS onboarding and configuration periods, not with 12-18 month custom build cycles.
How It Works
The build-versus-buy calculus changed because of three converging factors.
First: AI reduced the build cost by orders of magnitude. A solo operator with $105/month in AI tools (Cursor, Claude, OpenAI) produced output that would have required a 4-6 person team at 12x-43x the cost. This is not a marginal improvement. It moves custom software from a capital expenditure into an operating expense smaller than most SaaS subscriptions.
Second: Compounding execution reduced the cost of each successive build. Under CEM, the first project cost $7,995 in external support. The ninth cost $0. Template reuse reached 95%+ by the late portfolio. New projects required only the 5-20% of functionality that was unique to them. In the traditional model, every build starts from zero. In the compounding model, every build starts from the accumulated foundation of every build that came before it.
This means the build-versus-buy comparison is wrong when it evaluates a single product in isolation. The correct comparison is: what does it cost to build a foundation that produces 10 products versus what does it cost to buy 10 platforms? Building one product for $7,995 that makes products 2-10 nearly free changes the math entirely.
Third: The total cost of ownership for SaaS is higher than the subscription price. The CEM case data shows $1,565/month in SaaS subscriptions but $9,046/month at peak for contractors to maintain integrations between those platforms. The real cost of "buy" was $6,312/month -- four times the subscription cost. Data fragmentation, integration maintenance, vendor management, and cross-platform debugging are not line items on the SaaS invoice, but they are real costs that appear in the contractor budget and the operator's time.
The Forrester TEI methodology accounts for "hidden costs" but typically underweights them. The actual hidden cost multiplier in this case was 4x the subscription price. Organizations running standard SaaS rationalization analyses that focus only on subscription costs are seeing 25% of the real number.
What This Means for Decision-Makers Running Build-vs-Buy Analysis
The 2026 build-versus-buy analysis needs updated inputs. The old inputs -- $150K+ per developer, 12-18 month timelines, 70% failure rates -- produced correct outputs for the traditional model. If you are evaluating a traditional team build, those numbers still apply.
If you are evaluating an AI-assisted build under a compounding methodology, the inputs are different: $105/month in tooling, 5-74 day timelines, measurable quality at 12.1% defect rates. The outputs are different: 10 production systems for $65,394 versus $1.4M-$2.9M at market rates. And the compounding is different: every product built makes the next one cheaper, meaning the economic advantage widens with each additional build.
The framework itself is not broken. Gartner's five factors still apply. But the numbers you plug into that framework determine whether "build" or "buy" wins -- and the numbers have changed by one to two orders of magnitude on the build side. Any organization making this decision in 2026 using cost assumptions from 2020 is using a map from a different territory.
Run the analysis with current data. The answer may have changed.
Related: How to Displace $82,000 in SaaS and Contractor Costs with Owned Infrastructure | How the Marginal Cost of New Software Approaches Zero
References
- Gartner (2024). "Build vs Buy Decision Framework." Enterprise technology acquisition methodology.
- Forrester Research (2024). "Total Economic Impact Methodology." Risk-adjusted ROI and timeline discounting framework.
- McKinsey & Company (2024). "Digital Transformation ROI Data." Custom software project success rates and value delivery.
- Standish Group (2024). "CHAOS Report." Software project success and failure rate benchmarks.
- Keating, M.G. (2026). "Case Study: The Platform Displacement." Stealth Labz. Read case study
- Keating, M.G. (2026). "Case Study: The Flagship Build." Stealth Labz. Read case study