Are free salary sources accurate enough for pay decisions?

A Head of People at a 200-person SaaS company needs to make an offer for a Senior Data Engineer. She opens Glassdoor, finds "Senior Data Engineer: $125K–$165K," picks $148K, and sends the offer. The candidate declines — they had another offer at $175K. She goes back to Glassdoor. The number is still there. It still says $125K–$165K. Nothing is wrong with the tool. The problem is that she was using it to answer a question it was not built to answer.

Free salary sources are useful. They are not neutral. And the ways they fail matter more for employer pay decisions than most HR and TA teams realise.

What free sources are actually measuring

Glassdoor, LinkedIn Salary, Levels.fyi, Indeed Salary, and similar platforms all measure self-reported compensation. Users submit what they earn or were offered; the platform aggregates and surfaces ranges. That is a genuinely useful signal — but it is measuring what a broad cross-section of users chose to report, not what the market pays for the specific role you are trying to fill.

This matters because three structural biases run through almost all free crowd-sourced data:

1. Self-selection bias. People who fill out salary surveys skew toward those who are active in the job market, recently changed jobs, or feel strongly about their pay — either proud of a strong offer or frustrated that they earn below market. The contented median employee who has been at the same company for four years rarely fills anything in.

2. Title-only matching. Every free tool categorises by job title. "Senior Data Engineer" is one bucket. It does not distinguish between a data engineer building pipelines at a 30-person startup versus one managing petabytes at a late-stage public company. The scope, complexity, and market value of those roles can differ by $50K–$80K. The title is the same.

3. Stale data that lingers. Free platforms continuously ingest new submissions — but old ones stay in the aggregate unless actively decayed. A submission from two years ago carries equal weight to one made last week. In markets that moved significantly (and most tech and professional services markets have), older data anchors the distribution downward. You may be looking at a number that reflects the 2022 or 2023 market presented as current.

What each major source does well — and where it breaks down

Glassdoor is genuinely useful for per-company signals. You can see what Senior Engineers at a specific named competitor report earning, which no published salary survey provides. For understanding where one company sits relative to the market, it has real value. For setting your own offer, it has serious limitations: no sample-size disclosure per role per location, no confidence indicators, and a submission pool that skews toward dissatisfied employees and recent movers.

LinkedIn Salary has broad professional coverage and benefits from LinkedIn's verified employment data, which reduces (but does not eliminate) title noise. The weakness is opacity: LinkedIn does not publish its methodology, weighting, or how it handles outliers. You are trusting an algorithm you cannot inspect to produce a number you may anchor a pay decision to.

Levels.fyi is the most credible free source for software engineering total compensation at technology companies — because it requires offer-letter verification and captures base, bonus, and equity separately. Its coverage outside tech and outside the US thins out significantly. For a software engineering role at a mid-to-large tech company, it is an excellent sanity check. For most other roles and industries, sample sizes are too small to be reliable.

Indeed Salary pulls heavily from job postings, which is a real advantage for recency — advertised salary ranges reflect what employers are willing to pay today rather than what employees reported six months ago. The trade-off is that only a fraction of postings include salary ranges, so the sample is biased toward employers and markets with transparency norms. It also conflates ranges (a $90K–$150K posting is not the same as a $120K midpoint salary).

The employer-specific risks that free sources miss

Candidates using free tools to inform a negotiation can afford to get the number roughly right. The consequences of being 10% off are mild: a negotiation, a counter-offer, a conversation. Employers face a different set of consequences from the same imprecision.

Declined offers and wasted sourcing cycles. If your benchmark is systematically below market, you will disproportionately lose candidates at the offer stage — the most expensive point in the hiring process. Sourcing, phone screens, technical interviews, and final rounds are sunk cost. A benchmark miss that generates a decline costs far more than the subscription to a credible data source.

Compounding retention risk. Pay decisions made from stale or biased benchmarks create employees who are unknowingly underpaid. They find out — from recruiters, from Glassdoor, from colleagues who moved companies. Your highest performers, who are most marketable, find out fastest. Free sources are fine for a candidate doing quick research; they do not catch this drift systematically across a workforce.

Indefensible pay equity positions. When a pay equity review comes — whether internal or regulatory — "we checked Glassdoor" is not a methodology. Pay equity defence requires documented sources, sample sizes, and comparable group definitions. Free tools do not produce audit-ready evidence.

Budget planning errors. Finance teams using free sources as salary forecasting inputs will undercount. Crowd-sourced data systematically underweights high-end markets (candidates in those markets are less likely to report) and lags in fast-moving roles. A five-hire headcount plan built on Glassdoor figures rather than current market data can be $100K–$200K under-budget before you have sourced a single candidate.

When free sources are good enough

Free sources are not useless for employers. There are legitimate use cases where their resolution is fit for purpose:

• Order-of-magnitude sanity checks. Is this role in the $80K range or the $150K range? Almost any free source will answer this correctly. Use them to catch obvious misalignments before a search begins.
• Competitor pay intelligence. Glassdoor's per-company data is genuinely useful for understanding where a named competitor sits, independent of your own offer decisions.
• Initial budget conversations. When Finance asks for a rough estimate for headcount modelling, a triangulated free-source number (at least two sources, both read with appropriate scepticism) is a defensible starting point — as long as it is replaced with a proper benchmark before offer stage.
• Low-stakes or low-volume roles. For roles with high supply, stable pay, and low urgency, the cost of a benchmark miss is low enough that free sources may be proportionate.

The problem is not that employers use free sources. The problem is that they use them without understanding the confidence level — and apply them to high-stakes decisions where the margin for error is narrow.

What a source-cited benchmark actually gives you

A proper benchmark produces something free sources structurally cannot: a signal-strength-rated range anchored to a defined comparable set.

That means:

• You know how many data points the range is built on
• You know how recent they are
• You know which types of companies they came from (industry, size, stage)
• You know how the role description — not just the title — was matched to comparable positions
• You can see where sources agree and where they diverge, and why

This is the difference between "Glassdoor says $140K" and "across six sources triangulated for a 150-person Series C fintech, the 50th–75th percentile range is $155K–$170K, Excellent signal strength." One is a number; the other is a defensible pay decision.

Where EvenBetter fits

EvenBetter is built specifically for employer pay decisions, not candidate research. You paste the full job description — scope, responsibilities, requirements — and get a source-cited salary range triangulated across multiple LLMs (Claude, Gemini, ChatGPT, Grok) plus live market feeds covering job listings, salary surveys, and open-web compensation data. The result surfaces a signal-strength rating (Low / Good / Excellent) so you know how much weight to put on the output before acting on it.

Critically, the matching uses the full job description rather than the title alone — so a "Senior Data Engineer" role that involves managing a team of five and owning the infrastructure roadmap is matched against comparably scoped positions, not pooled with entry-level engineers who happen to share a title. You can review exactly how the sources are weighted and combined before relying on any output.

For teams making offers regularly or building salary bands, this replaces a patchwork of partially useful free tools with a consistent, auditable process. See our pricing for team access, and salary benchmarking software vs. salary surveys if you're weighing which type of tool fits your workflow.

The confidence problem is the core problem

Every free salary source presents a number. Almost none of them tells you how confident to be in that number, or what it would take for the number to be wrong.

That is not a small omission. A $148K midpoint with Excellent signal strength based on 400 recent, well-matched data points is a very different input to a pay decision than a $148K midpoint derived from 12 self-reported submissions from two years ago. If you cannot tell which one you are looking at — and with most free tools, you cannot — you are not benchmarking. You are anchoring.

For employers, the honest answer to "are free sources accurate enough?" is: accurate enough for orientation, not accurate enough for decisions. The stakes of getting a pay decision wrong — a declined offer, a retention gap, a pay equity exposure — are too high to rely on data you cannot evaluate. Understanding what salary benchmarking actually involves makes clear why the methodology behind a number matters as much as the number itself.