Work out what your helpdesk, NOC and support operation actually costs in-house — per ticket, per endpoint, and for true 24/7 coverage — in about 60 seconds. Then model the same operation white-labelled.
Work out what your helpdesk, NOC and support operation actually costs in-house — per ticket, per endpoint, and for true 24/7 coverage — in about 60 seconds. Then model the same operation white-labelled.
No sign-up. Nothing leaves your browser. The calculation runs locally, exactly like the formula shown below. A calculator gives you a modelled range — a discovery call models it on your real PSA data.
Step 1 — Your MSP today
Step 2 — What do you want to evaluate?
Step 3 — Your in-house costs
| Model | Annual cost | Cost / ticket | Coverage |
|---|---|---|---|
| In-house (your numbers) | $734,880 | $77 | Only as staffed — nights/weekends need the FTE in the calculator |
| White-label — modelled low saving | $477,672 | $50 | Under your brand, your PSA/RMM, your SLAs |
| White-label — modelled high saving | $367,440 | $38 |
Fully-loaded cost per tech / year
= Salary × (1 + Burden%) + (Tools per month × 12) + (Salary × Hiring%)
In-house annual cost = Fully-loaded cost per tech × Technicians
Cost per ticket = (In-house annual cost ÷ 12) ÷ Tickets per month
Cost per endpoint / month = (In-house annual cost ÷ 12) ÷ Endpoints
Productive hours per FTE / year = 2,080 × (1 − 15% PTO, sick & training) = 1,768 hrs
FTE for ONE coverage seat = (Coverage hrs/week × 52) ÷ 1,768
→ 24/7: (168 × 52) ÷ 1,768 ≈ 4.9 FTE for a single always-on seat
Techs needed for demand = (Tickets × Handle time in hrs)
÷ [(1,768 ÷ 12) × 75% target utilisation]
Tickets per endpoint = Tickets per month ÷ Endpoints
White-label modelled cost = In-house cost × (1 − Saving band for the service)
Service Desk 35–50% · NOC 40–60% (published on mokstech.net)
Dedicated Engineers 30–50% · Admin 60–70% (published on mokstech.net)
Pressure Score (0–100, higher = more strained) = 40% ticket intensity (tickets/endpoint vs the 0.5 benchmark) + 40% capacity strain (demand hours vs your team’s ticket capacity) + 20% coverage strain (coverage hours promised vs a standard 40-hr week). Bands: 0–39 Healthy · 40–64 Stretched · 65–84 Overloaded · 85–100 At risk. It is a decision aid, not a benchmark of your business.
Deliberately excluded: management time, office/workspace, SLA credits and downtime losses, overtime premiums, attrition beyond the hiring % you set, and project work. In most MSPs those lines make in-house cost higher than this model — add them on a discovery call. Salary defaults are indicative US benchmarks — the result is only as defensible as the salary you enter.
Send us 90 days of PSA data — ticket volume, tiers, handle time, after-hours split — and we will re-run this calculator on your actuals, with a fixed delivery model and SLA, not a range.
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Everything you need to know about partnering with Moksh Tech and scaling your MSP.
One seat must be filled 168 hours a week, 8,736 hours a year. One technician provides about 1,768 productive hours a year after holidays, sickness and training. 8,736 ÷ 1,768 ≈ 4.9 — before overtime, handovers or a second pair of eyes. That arithmetic, not the hourly rate, is why most MSPs white-label nights and weekends.
It depends on tier mix and handle time, which is why the calculator uses your numbers. Compare yours against your price per endpoint: if cost per endpoint per month is close to what you charge per endpoint, service delivery is consuming the margin the contract was sold on.
No. They are a model using the saving ranges Moksh Tech already publishes (40–60% for NOC vs in-house, 60–70% for administrative services vs an in-house FTE; wider service bands are labelled as modelled on the page). A real figure needs your ticket volumes, tiers, SLAs and tool stack — that is the discovery call.
None in particular. Moksh Tech engineers work inside ConnectWise, Autotask, HaloPSA, Kaseya, Datto, NinjaOne and Syncro, with Microsoft 365, Azure and AWS. Enter whatever you pay per technician for your PSA/RMM stack in Step 3.