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Hourly rate calculator — 2025 U.S. Benchmark

Find the right hourly rates for your team and contractors

Not sure if youʼre charging the right rates? Our hourly rate calculator uses benchmark industry data to help you set competitive rates for your employees and contractors.
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Select your industry and role to see benchmark hourly rates based on anonymized, real-world data. We’ll show you what similar teams are charging so you can confidently set fair, competitive rates — whether you’re pricing employee time or contractor work.

Benchmark Rate Range

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Suggested rate range based on the role and industry you selected, compiled from anonymized benchmark data sourced from real businesses.

Why use an hourly rate calculator?

Pricing is one of the trickiest parts of running a professional services business. Charge too little, and you risk burnout. But charge too much? You might price out great clients.

That’s where our hourly rate calculator comes in. It helps professional services businesses and freelancers calculate how much to charge per hour based on U.S. benchmark industry data. By comparing role and industry to benchmarked data from other real businesses, you can confidently start making smarter, more strategic pricing decisions. Keep in mind that this 
calculator should be used for orientation and as a starting point. Factors like your specific location, project scope, overhead costs, and market demand may all influence your final rates.

Where does our data come from?

This benchmark is powered by anonymized data from thousands of businesses in the United States. To build it, we analyzed the roles and rates from thousands of currently active customers in the United States who have tracked time since 2022. We calculated median hourly rates by role and industry, and included the 40th and 60th percentiles to give you a sense of the range.

To keep the data accurate and meaningful, we only included roles that were active and filtered out any accounts that didn't have enough real-world usage. We also excluded any roles with too little data and did some additional manual cleanup to group similar titles, remove outliers, and standardize naming where needed.

 

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