The numbers, fully loaded
Comparing delivery models on hourly rate is the wrong frame. The right frame is fully-loaded cost-per-shipped-feature, including the spec, the engineering, the QA, the security review, the deploy, and the time to revenue. We model this against equivalent scopes across our engagements and customer-side baselines [1].
We've seen CFOs collapse procurement decisions onto a vendor-rate spreadsheet that misses 60% of true cost. The hourly rate is one input among many - and not the dominant one. Cycle time dominates; rework dominates; opportunity cost dominates. A pod billing 1.4× the SI rate that ships in 1/4 the time clears the same scope at 35% of total cost.
The four hidden costs CFOs miss in vendor comparison
The four most expensive cost lines in an AI engagement are rarely on the vendor invoice. They live in your team's calendar, in delayed revenue, in incident response, and in the cost of running a system nobody owns confidently. Each is measurable; each is large; each compounds.
- Internal coordination overhead - the meetings, ticket queues, and handoff cycles that external delivery requires. Typically 15–25% of customer-side effort across the engagement.
- Rework from spec misalignment - work shipped, rejected, redone. Median 35% of effort in waterfall SI engagements; 6% in forward-deployed [2].
- Delayed revenue - every month a feature isn't live is a month of forgone value. At a $4M annualized impact, 6 months of delay is $2M of value left on the table.
- Operational debt at handoff - eval suite gaps, missing runbooks, untyped tool contracts. Pay it now or pay it during the first incident; pay rate is 5–10× during incident.
| Cost line | In-house build | External / SI | Forward-deployed pod |
|---|---|---|---|
| Spec + design | 4 wk × 3 PMs | 6 wk × 2 PMs (us) + 2 (you) | 2 wk × 1 PM |
| Engineering | 12 wk × 5 engineers | 20 wk × 6 engineers | 8 wk × 5 senior engineers |
| Eval + QA | 6 wk × 2 QAs (often skipped) | 4 wk × 2 QAs | Included (eval-gated CI) |
| Security review | 3 wk delay + 2 cycles rework | 4 wk delay + 3 cycles rework | 1 wk (engineered for audit) |
| Production hardening | 8 wk + 1 incident | 12 wk + 2 incidents | Included |
| All-in cost (USD, indicative) | ~$1.4M | ~$2.1M | ~$420K |
| Time to revenue | 9 months | 12+ months | 10 weeks |
View data table· Source: Techimax engagement data 2024–2026
| Series | K |
|---|---|
| External / SI | 2100 |
| In-house build | 1400 |
| Forward-deployed pod | 420 |
The lifetime cost story
Forward-deployed engagements look 3–4× better on shipped-feature cost. They look even better on a 3-year TCO basis because the eval suite, telemetry, and runbooks transfer to your team. Three years out, the maintenance cost of a forward-deployed-built feature is comparable to in-house. The maintenance cost of an external-SI feature is typically 1.5–2× higher because the team doesn't own the engineering.
View data table· Source: Techimax engagement TCO modeling 2023–2026
| Series | USD (thousands) |
|---|---|
| M0 | 0 |
| M3 | 350 |
| M6 | 690 |
| M12 | 980 |
| M18 | 1180 |
| M24 | 1340 |
| M30 | 1480 |
| M36 | 1610 |
Time to value: the line that dominates ROI
Build cost is half the equation; time-to-value is the other half. A $4M-impact feature delivered 6 months earlier is worth ~$2M in incremental value. Most CFO build-vs-buy comparisons ignore this entirely because timing variance is hard to predict - but it's what dominates the lifecycle ROI.
Forward-deployed engagements typically ship 6–8 months earlier than SI equivalents on mid-scope agentic features. Even with a richer hourly rate, the value delta usually swamps the cost delta. We model this conservatively - 6-month delay, 60% probability of full impact realization - and the ROI gap remains 4–7×.
| Delivery model | Time to revenue | Year-1 value captured | Year-3 ROI multiple |
|---|---|---|---|
| Forward-deployed pod | 10 weeks | $3.2M | 11.7× |
| In-house | 9 months | $1.0M | 5.4× |
| External SI | 12+ months | $0.4M | 2.1× |
| Do nothing (status quo) | - | $0 | 0× |
Hourly rate is one input among many - and not the dominant one. Cycle time dominates. Rework dominates. Opportunity cost dominates.
When in-house actually wins on cost
We don't argue forward-deployed wins everywhere. Below ~$200K all-in scope, in-house typically wins on simplicity - the coordination cost of a vendor relationship dominates the savings. Above ~$500K, forward-deployed wins by a wide margin. Between $200K–$500K it's situational and depends on three variables: existing team capacity, evals discipline already in place, and timing pressure.
Decision rule we share with CFOs: if your in-house team has shipped two production AI features with calibrated eval suites in the last 12 months, build in-house. If they haven't, the rituals are the bottleneck - bring in a pod that brings the rituals.
References
- [1]DORA 2024 State of DevOps Report - Google Cloud / DORA (2024)
- [2]The state of AI in 2025: Agents, productivity, and risk - McKinsey & Company (2025)
- [3]Generative AI: A creative new world - Sequoia Capital (2024)
- [4]AI in the enterprise: friction and value - MIT Sloan Management Review (2025)
- [5]Cloud FinOps for AI workloads - FinOps Foundation (2025)
Frequently asked questions
Are these numbers from public benchmarks?
Mix. The cost line items are from our own engagement data + customer baselines they shared at engagement start. The cycle-time multipliers are consistent with published industry data on AI delivery velocity.
How sensitive is the comparison to scope?
More sensitive at small scope (where in-house wins on simplicity) and less sensitive at large scope (where the cycle-time compression dominates). Below ~$200K all-in, in-house often wins; above ~$500K, forward-deployed wins by a wide margin.
What about open-source agentic frameworks?
We use them. They're orthogonal - they reduce engineering effort within either model. Forward-deployed teams use them more aggressively because the eval discipline lets us swap providers and frameworks safely.
How do you handle scope creep in fixed-fee engagements?
Pod weeks are the unit. Scope changes mid-engagement either trade other work out of the pod's queue or extend pod weeks at the same rate. We don't ratchet rates mid-engagement; the rate sheet is set at kickoff.
What's the ROI threshold you advise CFOs to require?
We default to a 3× year-1 ROI requirement on AI engagements. Below that, scope risk usually exceeds payoff. Above that, the engagement should fund itself. Forward-deployed engagements typically clear 5–10× in year 1 because of the time-to-revenue compression.
How do model API costs factor in?
Run-rate costs (model spend, retrieval, ops) are separate from build cost and apply equally to all delivery models. Typical mid-scope agent: $0.04–$0.20 per resolved interaction. We model these against expected traffic at kickoff so finance has a Year-1 OpEx number, not just a CapEx one.
Do you publish a rate sheet?
Yes - share on request. Pod-week rates are flat per-engineer-tier; we don't run partner-discount-rebate pricing games. The math should be transparent enough that procurement can model alternatives.