Should you build or buy AI for RFPs?
Compare the true 3-year cost of building an in-house AI response platform versus buying AutoRFP.ai — including engineering time, maintenance, time to value, and win-rate impact.
36-month comparison horizon
Your RFP workload
How many responses you run and what expert time costs.
Build scenario
In-house platform engineering, rebuilds, and upkeep.
Buy scenario
AutoRFP.ai subscription, implementation, and admin time.
Performance assumptions
Editable benchmarks — tune time reduction and win-rate uplift for each path.
Editable benchmark
Editable benchmark
How this calculation works
- Build 3-year cost = initial build labour + rebuild labour + maintenance FTE + cloud/model spend after v1.
- Buy 3-year cost = AutoRFP.ai subscription × 3 years + implementation labour (weeks × 40 hrs × blended rate) + admin labour over 36 months.
- Build time to value = initial build months + (rebuild cycles × months per rebuild). Buy time to value = implementation weeks ÷ 4.
- Capacity gained = responses/year ÷ 12 × months Buy is live earlier. Hours gained applies AutoRFP.ai’s time-reduction rate. Annual hours saved once live = responses × hours/response × each path’s time-reduction rate.
- Projected win rates = current win rate + each path’s uplift (capped at 100%). Wins influenced use each path’s active months within the 36-month horizon.
FAQ
Frequently asked questions
What costs does the Build vs Buy calculator include for the in-house 'Build' scenario?
The calculator includes initial build labor, expected rebuild cycles and their duration, ongoing maintenance FTE costs, and cloud/model spending after the initial version is completed.
How does the calculator account for the 'time to value' difference between building and buying?
It calculates the time to value by adding the initial build months and the time required for future rebuilds for the 'Build' scenario. For the 'Buy' scenario, it calculates the implementation weeks divided by 4, showing how much earlier AutoRFP.ai can start delivering results.
What are the benchmark assumptions used for AutoRFP.ai's performance in the calculator?
The calculator uses editable benchmarks, defaulting to a 64% reduction in response time and a 10-point uplift in win rates for AutoRFP.ai, compared to a 40% time reduction and 5-point win-rate uplift for an in-house build.
Does the calculator consider the impact on win rates for both scenarios?
Yes, it factors in a projected win rate for both paths by adding the respective win-rate uplifts (5 points for build, 10 points for buy) to your current win rate. It then calculates the number of wins influenced during the active months within the 36-month horizon.
Are the 3-year cost estimates provided by the calculator considered a final quote?
No, these are illustrative estimates based on editable benchmarks. Actual costs and outcomes depend on your specific scope, tech stack, team, and product mix. You should confirm AutoRFP.ai pricing with a live demo.
See it on your workflow