An analysis of capacity utilization rates across 50,752 hours worked and 38,364 billable hours from Autotask PSA. This report compares two capacity models and breaks down utilization per resource to show where your team's time actually goes. PSA
An analysis of capacity utilization rates across 50,752 hours worked and 38,364 billable hours from Autotask PSA. This report compares two capacity models and breaks down utilization per resource to show where your team's time actually goes. PSA
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Operations managers, service delivery leads, and MSP owners managing capacity
How often: Weekly for scheduling, monthly for utilization reviews, quarterly for staffing decisions
An analysis of capacity utilization rates across 50,752 hours worked and 38,364 billable hours from Autotask PSA. This report compares two capacity models and breaks down utilization per resource to show where your team's time actually goes. PSA
Overall capacity utilization metrics across two calculation methods.
EVALUATE
ROW(
"TotalLoggedHours", [Company - Hours Worked],
"CapacityAutotask", [Capacity Total (Autotask)],
"CapacityProxuma", [Capacity Total (Proxuma)],
"UtilAutotask", [Capacity Utilization Rate (Autotask)],
"UtilProxuma", [Capacity Utilization Rate (Proxuma)]
)Top 10 resources ranked by total hours worked. Utilization rates shown for both capacity models.
| Resource | Hours | Billable | Util. (Proxuma) | Util. (Autotask) |
|---|---|---|---|---|
| Kevin Allen | 2,060 | 3,488 | 59.1% | |
| Chelsea Thomas | 1,780 | 3,480 | 51.1% | |
| James Li | 2,136 | 4,352 | 49.1% | |
| David Hunt | 1,862 | 4,352 | 42.8% | |
| Becky Johnson | 1,239 | 4,352 | 28.5% | |
| Dr. Amber Ayala DVM | 2,400 | 8,720 | 27.5% | |
| Andrew Roberts | 1,888 | 8,720 | 21.6% | |
| Virginia Combs | 932 | 4,352 | 21.4% | |
| Darren Alexander | 1,224 | 6,640 | 18.4% | |
| Jennifer King | 1,585 | 8,720 | 18.2% | |
| Marie Fisher | 1,256 | 6,976 | 18.0% | |
| Jerry Mcfarland | 1,554 | 8,720 | 17.8% | |
| Deborah Young | 626 | 8,720 | 7.2% | |
| Joshua Hernandez | 446 | 8,720 | 5.1% | |
| Bryan Myers | 149 | 8,600 | 1.7% |
EVALUATE
TOPN(
15,
FILTER(
ADDCOLUMNS(
SUMMARIZECOLUMNS(
BI_Autotask_User_Details[resource_user_name],
"LoggedHours", [Total],
"CapacityProxuma", [Capacity Total (Proxuma)]
),
"UtilPct", DIVIDE([LoggedHours], [CapacityProxuma])
),
[CapacityProxuma] > 100 && [LoggedHours] > 50
),
[UtilPct], DESC
)
ORDER BY [UtilPct] DESCWhy the two utilization rates differ so dramatically, and which one to use.
The gap between 6.4% (Autotask) and 39.7% (Proxuma) comes down to how each system defines "capacity." Autotask uses a broad calculation: it counts all calendar hours for every resource that has ever been active in the system. This includes weekends, public holidays, vacation days, and hours outside business time. The result is a capacity pool of 787,858 hours, which makes any real work look like a rounding error.
Proxuma takes a different approach. It uses the configured working hours per resource, typically 40 hours per week minus holidays and leave. That brings the capacity pool down to 127,800 hours. When you divide the same 50,752 hours worked by this smaller (and more accurate) denominator, you get 39.7% instead of 6.4%.
For day-to-day decision making, use the Proxuma rate. It reflects how much of your team's actual working time is being filled with logged work. The Autotask rate is technically correct but practically useless for staffing decisions.
One more thing to keep in mind: these numbers are lifetime totals. They span the entire dataset period, which means they include onboarding ramps, seasonal slowdowns, and periods where resources were partially active. For staffing decisions, filter by a recent month or quarter to get a snapshot that reflects current workload.
At 39.7%, the Proxuma capacity rate paints a usable picture. It means roughly 40% of your team's configured working hours are filled with logged work. The Autotask rate of 6.4% is mathematically correct but inflated by calendar hours that were never available for work. Use the Proxuma rate for all staffing and planning conversations.
Even with the Proxuma model, 60.3% of configured working hours have no time entries. Some of this is expected (meetings, admin, breaks), but if your target utilization is 65-75%, there is room to take on more client work or reduce headcount. Filter by recent months to confirm whether this gap is stable or shrinking.
Of the 50,752 hours worked, 38,364 are billable. That is a 75.6% billable-to-total ratio, which is solid for an MSP. It means the time your team does log is largely revenue-generating. The gap between utilization (39.7%) and billable ratio (75.6%) shows the issue is not billability but total volume of logged hours against capacity.
The single most impactful step is to filter utilization by a specific period. Lifetime totals are useful for spotting long-term trends, but they smooth out the very peaks and valleys you need to see for staffing decisions. Set up a monthly or weekly utilization view in Power BI so you can answer "are we overstaffed right now?" rather than "were we overstaffed on average over the last few years?"
Second, compare resource-level utilization on a monthly basis. The per-resource numbers in section 2.0 look uniformly low because the capacity denominator covers the entire dataset. A resource working 2,400 hours over three years has a very different utilization profile than one doing 2,400 hours in 18 months. Monthly slicing will surface those differences.
Third, define a target utilization range. Most MSPs aim for 65-75% utilization (Proxuma model) as the sweet spot between keeping the team busy and leaving room for reactive work, training, and internal projects. Once you have a target, you can build Power BI alerts that flag when any resource drops below 50% or exceeds 85% in a given month.
EVALUATE
ROW(
"StatusAutotask", [Capacity Utilization Status (Autotask)],
"StatusProxuma", [Capacity Utilization Status (Proxuma)]
)Autotask calculates capacity using all calendar hours for every active resource, including nights, weekends, and holidays. Proxuma uses the configured work schedule (typically 40 hours/week minus leave). The Autotask number answers "what percentage of all possible time was used?" while the Proxuma number answers "what percentage of working time was used?" For staffing decisions, the Proxuma rate is the one that matters.
Because it divides hours worked by all calendar hours, not just working hours. A year has 8,760 hours. If a resource works 1,800 hours in a year, Autotask sees that as 20.5% of calendar time. But nobody works 24/7. The Proxuma model uses ~2,080 hours (40h x 52 weeks) as the denominator, which gives a realistic 86.5% for that same resource. The Autotask rate is always going to be a fraction of the Proxuma rate.
Using the Proxuma model (configured work hours), most MSPs target 65-75% utilization. Below 60% typically signals overcapacity or a time-tracking gap. Above 80% means the team is running hot with limited room for reactive tickets, training, or internal projects. The sweet spot depends on your service mix: reactive-heavy MSPs should aim lower (60-65%) to absorb spikes, while project-heavy shops can push toward 75%.
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