Salesforce Consulting Agency Costs Calculator
Hourly rate is not the cost. A "cost-effective" team often looks cheaper on the proposal - then shifts planning, review, rework, coordination, and risk onto your internal people. That second bill shows up later, and it is usually bigger.
AI makes the traditional split ("seniors for hard work, juniors for easy Setup/Flow tasks") outdated: execution is increasingly cheap compute, while judgment, architecture, and review stay expensive.
Below is an interactive model of two staffing shapes with the same knobs - only the defaults differ. Agency invoice and true cost (including internal shadow QA) are both shown. Change any assumption; the results update live.
We discuss the whole topic - including the risks and hidden costs - in our post The Real Cost of Salesforce Consulting Services.
Cost & time calculator
Two scenarios, the same set of knobs - only the defaults differ. Work is measured in abstract delivery points. Both teams get AI leverage; supervision, growing rework, and internal shadow-QA are priced into the true cost.
With these assumptions, Scenario B (senior-led) finishes about 7.3 months earlier (2.1× sooner), and its true cost is 38% of Scenario A (a saving of $1,094,584, 62%).
2 sr @$110 · 8 jr @$30 · 2 PM
agency $1,464,785 + 20% internal shadow
4 sr @$150 · 0 jr @$30 · 0 PM
agency $631,579 + 5% internal shadow
saves 62% · $1,094,584
7.3 months earlier
How the calculation works
Scope becomes months; months become invoice cost; invoice plus your internal load becomes true cost. Six stages, and what each one is accounting for - the worked numbers for both scenarios follow below.
The work is measured in abstract delivery points, so two differently-shaped teams can be compared on the same outcome. On top of the agreed size, a share of extra scope is added for what the original estimate missed - unasked questions, thin requirements, edge cases found during UAT. A team that consults up front discovers less of this later, so its gap allowance is smaller.
Each role converts billable hours into points at its own rate. Seniors produce more per hour on judgment-heavy work, but lose a slice of their delivery time to supervising - reviewing, unblocking, explaining - and that slice grows with the number of juniors per lead, while you still pay their full rate. AI multiplies output for both teams. Project managers cost money and produce no points.
Not all capacity moves the project forward. A share is spent redoing work: fixing defects, reopening closed tickets, chasing regressions. On weak foundations that share compounds month over month as technical debt accumulates, so late months deliver less real progress than early ones - while the invoice stays the same size. A capped ceiling keeps the model from reaching zero output.
Net points are added month by month until they cover the effective scope. Because the rework tax can grow, this is not simple division - a team losing capacity over time stretches out non-linearly, and each additional month carries a full month of burn. This is where a capacity gap turns into a schedule gap.
Monthly burn is every person on the contract at their rate, whether or not they produce points. Multiplied by the duration above, this is the number on the proposal - and the only one most comparisons look at. A low rate card attached to a long timeline can easily cost more than a high rate card attached to a short one.
Work that lands on your own people never appears on the vendor invoice: re-testing delivered features, re-reviewing pull requests, unblocking pipelines, standing in for consultants on stakeholder calls. Priced as a share of agency burn, this is the second bill - and it is the figure the comparison uses to decide which scenario actually wins.
Both scenarios use the same knobs - only the defaults differ. True cost includes internal dual-QA and coordination load.
How we got these numbers
The same six stages, worked out in full for each scenario. Every figure recomputes live from the sliders.
Scenario A · Cost-effective
Scenario B · Senior-led
"Just use record-triggered flows - it's faster and cheaper"
No questions asked about the post-MVP roadmap, other teams, or where the process is heading. Follow the cost curve of that single call over the life of the project.
The agency recommends record-triggered flows for the custom sales logic. Faster and cheaper to configure, they say. No questions about the post-MVP phase or adjacent teams.
Large record-triggered flows are created for Opportunities, Line Items, Quotes and Orders. Requirements are met 1:1.
A new product line needs different logic. Extra branches are bolted onto the existing flows.
Another segment, another set of branches. The flows now take a minute to scroll through.
Ramp-deal support added. Every change now risks breaking an existing path or hitting SOQL / CPU limits.
Async paths are introduced to dodge limits. Row-lock exceptions appear - and retries are not possible in flow.
Bug fixes consume roughly 80% of development time. New features stall on the backlog.
Verdict: migrate everything to Apex triggers. Six months, five people, no new customisations until it is done.
A 6-month, 5-person rewrite - plus a feature freeze while it happens. None of it appeared on the original quote. A senior team would have asked about the roadmap on day one and picked an architecture that scales.
Fourteen line items that never make the quote.
These are cost patterns you can observe on a program - not insults. Several are already priced as assumptions in the model above (rework, gaps, supervision, internal shadow). We discuss the whole topic, all of these risks, and the hidden costs in our post The Real Cost of Salesforce Consulting Services.
Early choices that skip future-state questions often force multi-month rewrites once scope grows.
Work matches the written requirement and the happy path - edge cases and design pushback appear only after UAT.
Unstructured or junior-heavy output needs line-by-line senior review, corrections, and re-review - hours that rarely show on the vendor rate card.
Velocity falls over time as fixes and discovery consume capacity that used to ship features - until remediation becomes its own project.
Critical pieces that only one person can safely change create delay and rewrite risk when they are unavailable.
Assumptions go unchallenged; wrong requirements get built "correctly," and rework shows up after go-live.
Merge conflicts, pipeline issues, and process gaps pull internal seniors into unblocking - time outside the agency quote.
Custom automation where a formula, config, or known library would suffice - more build cost and more long-term maintenance.
After repeated defects, internal teams re-test agency work end-to-end - you pay for delivery and for verification again.
A team that cannot own stakeholder communication usually needs an additional PM - extra burn with little delivery output.
Tickets close quickly, then reopen as bugs; later sprints spend capacity on fallout from earlier "completed" work.
When consultants cannot represent the work on a business call, your leads become permanent proxies - or reputation takes the hit.
Billable hours continue while access, answers, or environment setup are pending - and unblocked slices of work stay untouched.
Model-generated changes arrive without understanding; review comments bounce back to the model, so seniors still carry the real delivery risk.
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