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
Want a quote built on these numbers?
Send us the brief and a delivery lead validates these assumptions against your data, then replies with indicative scope, timeline and commercial options.
CalculatorCompare three-year TCO for building an AI team versus buying platform and partner delivery. — routed to this team
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.
Should we build an in-house AI team or buy a platform and partner?
Compare three-year TCO for an in-house AI build — hiring, salary, attrition, ramp, infrastructure and opportunity cost of delayed value — against platform licence plus partner delivery. Time to value is the decisive variable: every quarter of delay carries the full benefit the deployment would have produced.
Ungated — results appear instantly, no email required.
What you enter
- Target in-house team size and blended salary
- Hiring lead time, ramp time and annual attrition
- Infrastructure and tooling cost
- Platform licence and partner delivery cost
- Monthly benefit the deployment produces once live
How it is calculated
- 1.Model in-house cost across three years including hiring, ramp and attrition replacement.
- 2.Model platform plus partner cost across the same period.
- 3.Estimate time to first production value under each path.
- 4.Charge the slower path the benefit forgone during the delay.
- 5.Compare total three-year cost including that opportunity cost.
What you get back
- Three-year TCO for each path
- Time to first production value
- Cost of delayed value
- Recommended path with the crossover assumptions made explicit
Built for: CIOs, CTOs and finance leaders deciding how to resource an AI programme.
When does building an in-house AI team beat buying?
When the capability is a durable differentiator, volume is high enough to amortize the team, and you can hire and retain scarce skills at the assumed salary. For a first production deployment on a schedule, the delay cost usually decides against it.
Why include opportunity cost in a build-versus-buy model?
Because the two paths reach production at different times. A cost-only comparison silently gives the slower path free months, which is exactly the error that makes in-house builds look cheaper than they are.