How we estimate compensation, and why our numbers read lower, and truer, than most salary sites.
Most salary sites quote numbers that feel high, because they blend in stock, bonuses, sign-on, and variable pay, and lean on self-reported figures that skew upward. salaryband does the opposite. We model Fixed Pay Total: your base plus fixed allowances, annualised. We deliberately exclude RSUs, performance bonuses, and other variable components, because those swing wildly and inflate the picture of what people actually take home as salary.
The result: our numbers are usually lower than what you'll see on aggregators, and closer to reality.
We calibrate against a blend of public compensation sources, including Levels.fyi, AmbitionBox, Instahyre, and Glassdoor. Because self-reported data on those platforms tends to run above the true workforce median, we adjust toward the estimated median for each segment rather than taking reported figures at face value.
A FAANG engineer, a funded-startup engineer, and an IT-services engineer with identical titles and years of experience are not in the same compensation market; treating them as one is the single biggest reason generic salary numbers feel wrong. We model compensation across distinct company tiers and roles separately, so your estimate reflects the market you're actually in, not an average smeared across all of them.
Some role, tier, and experience combinations have a lot of supporting data; others have less. Rather than present every estimate with false confidence, we flag the cells where our data is thinner. When you see a confidence note next to your result, that's us being upfront about how much to lean on that specific number.
salaryband gives you modelled estimates and positioning guidance, grounded in real market data and a transparent approach. It is not a guarantee of any salary or offer, and not financial or career advice. Compensation depends on factors no model can fully capture: your specific company, negotiation, timing, and the person across the table. We give you a well-calibrated starting point and a clear sense of where you stand.
As we accumulate real, anonymous salary submissions from users, our model gets sharper, and we'll share more of the detail behind it here over time. Transparency is the whole point of a compensation product; we're building toward more of it, not less.