Compare three-year TCO for building an AI team versus buying platform and partner delivery.
Built for CIOs, CTOs and CFOs deciding which layer of the AI stack to own. Includes hiring, attrition and the cost of arriving in production nine months later.
Your inputs
Benchmarks show typical enterprise ranges — override every field with your own numbers.
AI/ML engineers, data engineers, MLOps and product
Benchmark: 6–12 for a first production AI platform team
Benchmark: $160k–$230k in US metros
Recruiting, agency fees and ramp, as a share of salary
Benchmark: 18–28% of first-year salary
Drives re-hiring cost and lost productivity while roles are open
Benchmark: 15–25% for AI engineering roles
Compute, inference, observability, vector storage, security tooling
Benchmark: $150k–$400k at production scale
Bought path: CCaaS/AI platform subscription and consumption
Benchmark: $300k–$800k enterprise-wide
Implementation and enablement; assumed to fall to 35% in years 2–3
Benchmark: $500k–$1M for a scoped program
Benchmark: 12–18 months including hiring
Benchmark: 4–7 months with an experienced partner
Use the output of your Contact Center AI or Agentic AI model
Benchmark: Pull from your ROI calculator result / 12
Three-year advantage of the bought path over the in-house build, expressed per year. Negative results favour building.
Directional estimate over a three-year horizon. Assumes partner delivery falls to 35% of year-1 cost in years 2–3, and that attrition costs 25% of a loaded salary in lost productivity per departure.
Three-scenario view
Finance reviewers expect a range. These scenarios flex adoption and implementation cost around the model you entered.
Slower adoption, higher integration effort
Your inputs as entered
Strong sponsorship, clean data, phased scale-up
How enterprise leaders use this model
- What does this model compare?
- Three-year total cost of ownership for an in-house build — engineers, hiring, attrition, infrastructure and run — against a bought platform plus partner delivery, adjusted for the value of getting to production sooner.
- Why include time to value as a cost?
- Every month before production is a month of unrealised benefit. The model charges the delay difference between the two paths against the slower option, which is usually the in-house build.
- Is hybrid an option?
- Almost always. Most enterprises buy the platform and orchestration layer and build the differentiating logic and data products in-house. Model both extremes first, then decide which layer you actually want to own.
- How do we account for attrition?
- AI engineering attrition runs high in competitive markets. The model reloads a share of hiring cost each year and prices the productivity gap while roles are backfilled.