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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

Build / Buy (3-year total)See the full comparison$544,000 / $134,200
01

Your RFP workload

How many responses you run and what expert time costs.

Responses per yearresponsesRFPs, DDQs, and security questionnaires you respond to each year.
Hours per responsehoursPerson-hours drafting, reviewing, and formatting one response today.
Blended internal hourly cost$/hourFully-loaded cost of SME / ops time used for admin and implementation labour.
Current win rate%Share of responses you win today, before either path.
02

Build scenario

In-house platform engineering, rebuilds, and upkeep.

Engineers allocatedengineersEngineers working on the in-house AI response platform.
Fully-loaded engineer cost$/yearAnnual fully-loaded cost per engineer (salary, benefits, overhead).
Initial build timemonthsMonths to ship a usable v1 in-house platform.
Expected rebuild cyclescyclesMajor rebuilds expected over three years (model changes, stack rewrites).
Months per rebuildmonthsEngineering months each rebuild cycle consumes.
Ongoing maintenance FTEFTEFull-time-equivalent engineers keeping the platform running after v1.
Cloud & model cost$/monthMonthly infrastructure, LLM API, and hosting spend for the in-house stack.
03

Buy scenario

AutoRFP.ai subscription, implementation, and admin time.

AutoRFP.ai annual subscription$/yearIllustrative annual AutoRFP.ai investment; adjust to your quote.
Implementation effortweeksInternal weeks to integrate AutoRFP.ai (one person-week ≈ 40 hours at your blended rate).
Internal admin timehrs/monthHours per month spent administering AutoRFP.ai after go-live.
04

Performance assumptions

Editable benchmarks: tune each path’s time reduction and uplift.

Build time reduction%Expected fraction of response hours an in-house build eventually removes (0–100%).

Editable benchmark

AutoRFP.ai time reduction%Expected fraction of response hours AutoRFP.ai removes (aligned with the ROI calculator).

Editable benchmark

Build win-rate upliftptsExpected percentage-point lift from an in-house platform once live.
AutoRFP.ai win-rate upliftptsExpected percentage-point lift from AutoRFP.ai once live (aligned with the ROI calculator).
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 rate = current rate + each path’s uplift (capped at 100%), counted over each path’s active months within the 36-month horizon.

3-year comparison

$544,000

Build (3-year total)

$134,200

Buy (3-year total)Recommended

Buy saves $409,800 over 3 years. With Buy, AI starts helping 7 months earlier, about 58 additional responses in that window (≈1,195 hours at your AutoRFP.ai time-reduction rate).

Time to value

Build8.0 months
Buy (AutoRFP.ai)1.0 months

Cumulative cost over 36 months

BuildBuy (AutoRFP.ai)

Outcomes

BuildBuy
Projected win rate45%50%
Wins influenced1229
Hours saved / year1,2802,048

Build costs were estimated via:

  • Responses per year: 100 responses
  • Hours per response: 32 hours
  • Blended internal hourly cost: $85 /hour
  • Current win rate: 40 %
  • Engineers allocated: 2 engineers
  • Fully-loaded engineer cost: $180,000 /year
  • Initial build time: 4 months
  • Expected rebuild cycles: 2 cycles
  • Months per rebuild: 2 months
  • Ongoing maintenance FTE: 0.5 FTE
  • Cloud & model cost: $2,000 /month
  • Build time reduction: 40 %
  • Build win-rate uplift: 5 pts

Buy costs were estimated via:

  • Responses per year: 100 responses
  • Hours per response: 32 hours
  • Blended internal hourly cost: $85 /hour
  • Current win rate: 40 %
  • AutoRFP.ai annual subscription: $30,000 /year
  • Implementation effort: 4 weeks
  • Internal admin time: 10 hrs/month
  • AutoRFP.ai time reduction: 64 %
  • AutoRFP.ai win-rate uplift: 10 pts

These are illustrative estimates using editable benchmarks, not a quote or guarantee. Actual costs and outcomes depend on scope, stack, team, and product mix. Confirm AutoRFP.ai pricing with a live demo.

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FAQ

Frequently asked questions

Something else on your list? Bring it to a demo and we will answer it on one of your own questionnaires.

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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 win-rate uplift for AutoRFP.ai, compared to a 40% time reduction and a 5-point uplift for an in-house build.

Does the calculator consider the impact on win rates for both scenarios?

Yes. For each path it adds that path’s uplift (5 points for build, 10 points for buy) to your current rate, then counts the deals it influences during the months that path is active 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.

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Ready to pressure-test the build case?

Every draft carries a Trust Score for its sources and a Feedback Score for how fully it answers. Quality is measured by how little your team changes, and every approval records it. In a recent RFP, Red Rover had 95% of responses automated (83 out of 87 requirements). Measure it on your own RFPs in a two-week proof of concept.

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  1. [01]A 45 minute walkthroughBuilt around the questionnaires your team answers today, not a generic demo.
  2. [02]A free two-week proof of conceptProve it on your own RFPs, with your content and your reviewers.
  3. [03]First answers, cited to your contentAI-drafted answers in as little as 48 hours of bringing your content in.
See it on your own RFPsBook a demo