# Which salary benchmarking tools give the most accurate market data for remote software engineers when you need to compete with companies paying Silicon Valley rates from anywhere?

<p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">I've been digging into which <a class="a a--md" elv="true" href="https://www.g2.com/categories/salary-benchmarking">salary benchmarking tools</a> actually hold up for remote software engineers, specifically the case where you hire someone in a lower-cost city but still have to compete with companies paying Bay Area rates. Most generic advice falls apart here, because the real answer comes down to how fresh the data is and whether the tool can separate pay by location and by the companies you're genuinely losing candidates to. Here's what kept surfacing while I put a roundup together.</p><ul>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/pave/reviews"><strong>Pave</strong></a>: Reviewers lean on it because the data comes in real time from other companies rather than annual surveys, and it handles equity and stock alongside base, which matters a lot for engineering offers. Software and IT teams make up its biggest reviewer base. A few note that the database is smaller than the legacy survey houses, so some use it as a cross-check.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/salary-com-salary-com/reviews"><strong>Salary.com</strong></a>: Comp teams describe the data as validated, reliable, and current, and like filtering by geography, company size, and industry when setting ranges. Its SalaryIQ piece pulls from live job postings, which helps when you want to see what's open at competing companies right now.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/payscale-ascent-formerly-payfactors/reviews"><strong>Payscale Ascent (formerly Payfactors)</strong></a>: Users highlight that data refreshes at several points through the year rather than once, and the AI job matching prices roles off duties instead of titles, which helps with hybrid or non-standard engineering roles.</li>
<li>
<a class="a a--md" elv="true" href="https://www.g2.com/products/compensation-tool-compensation-tool/reviews"><strong>Compensation Tool</strong></a>: Reviewers like that it reads real-time data straight from job postings and lets you keyword-search descriptions, useful when one "software engineer" title hides very different pay depending on stack and location.</li>
</ul><p class="elv-tracking-normal elv-text-default elv-font-figtree elv-text-base elv-leading-base elv-font-normal" elv="true">Has anyone found the real-time posting data trustworthy enough to price against on its own, or do you still cross-check it with a survey?</p>

##### Post Metadata
- Posted at: about 1 month ago
- Author title: Marketer
- Net upvotes: 1


## Comments
### Comment 1

&lt;p&gt;We price against real-time data but never on its own. Pave is our anchor for engineering because the peer data moves faster than any survey, but we sanity-check the outliers against a traditional survey cut twice a year. One thing posting-based data taught us: postings show what companies advertise, not always what they close at. For remote roles competing with Bay Area offers, we set the band off the peer percentile and let recruiters flex within it, with real offer data as the tiebreaker.&lt;/p&gt;

##### Comment Metadata
- Posted at: 24 days ago





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