The best free salary data sources help People and Rewards teams build market awareness – not full compensation strategies – by offering high-level insights into pay across roles, locations, and industries.
They’re an accessible starting point for when you have no budget or buy-in to invest in dedicated salary benchmarking tools.
But free salary data varies widely in accuracy and is rarely reliable for up-to-date, accurate total compensation insights based on a consistent, representative sample.
The result? Teams often end up working with outdated averages, inconsistent role definitions, or partial views of compensation that don’t reflect how salary, equity, and benefits actually come together.
To use free salary data more deliberately, it’s worth understanding both the available sources and the limits of what they can support.
To that end, this guide walks you through:
You’ll leave knowing how to interpret free salary data and its limitations before applying it to compensation decisions.
This list covers free salary data sources that span crowdsourced platforms, modelled estimates, real-time data from specialised compensation software, and government or industry surveys – each with different strengths and limitations.
Let’s go:
Ravio is a specialised compensation data provider that integrates with HRIS systems to give high-growth tech companies total rewards data in real-time.
It gives you three free benchmarks for three roles of your choice (no credit card required) to assess how relevant the salary data is for you.
Pros | Cons |
|---|---|
Real-time, global total compensation data with particularly strong coverage in Europe. | Data less relevant for organisations with few tech roles. |
Data verification process with data scientists reviewing data for accuracy and consistency across industries and roles. | |
Advanced filters for location, industry headcount, company size, and funding stage to support relevant salary benchmarking. |
Pave is a US-based compensation benchmarking company that gives you real-time pay data via HRIS integrations.
It comes with a free tier plan for startups (teams with less than 200 employees) called Market Data Lite that offers compensation data in the US and one additional market of your choice.
Pros | Cons |
|---|---|
Real-time total compensation data in the US and Canada, as 67% of its benchmarks come from the region. | Limited EU total compensation data, as only 14% of Pave’s benchmarks are from Europe. |
Real-time hiring trends data via Pave’s integration with ATS systems. | No real-time market trends analysis, such as salary increases and attrition rates across different roles and locations. |
Poor global data coverage for fast-growing teams that hire outside the US. |
Salary Insights is a free salary data tool by Deel, a global payroll and compliance platform. It provides market-level salary data across multiple roles and geographies.
Salary Insights sources real-time data from Deel’s platform activity, covering payroll and contractor engagement data across global workers.
Pros | Cons |
|---|---|
Real-time salary data from up-to-date platform activity. | No insights into benefits, equity, bonuses, and structured pay bands – all essential for competitive compensation planning. |
Global coverage with data for over 150 countries. | Limited transparency on the data verification process, with no publicly available details on how data is cleaned or weighed. |
Filters available to find data by role level, seniority level, and location. | Data may be biased toward Deel’s remote, contractor-heavy user base, limiting its representativeness across industry-specific markets and roles. |
Glassdoor is an employer review and job marketplace that collects self-reported salary data from employers and former employees who anonymously submit their job title, salary, location, and other details.
The data is then aggregated to produce the Glassdoor salary index that provides a salary range per role.
Because Glassdoor is based on a give-to-get model, you can only access the data by submitting yours.
Pros | Cons |
|---|---|
Large volume of salary data for common roles. | Unverified data, as it's crowdsourced from website users with no checks to make sure the submitted data is accurate. |
Outdated salary averages with displayed ranges based on the average of all salaries ever submitted for that role, with no outliers taken into account. | |
No way to identify up-to-date shifts in the salary based on market trends. | |
No filters by company type or size – making it challenging to make accurate, like-for-like pay comparisons with your peers. |
Indeed is a global job search engine that aggregates salary data via job postings with disclosed pay, employee self-reported submissions, and the platform’s statistical estimates.
Pros | Cons |
|---|---|
Easy to access by simply setting up a free account. | Data variance as different companies calculate compensation in different ways. |
Broad role and geography coverage. | Inconsistent transparency around how data is weighed between sources. |
Useful for understanding market-level salary ranges. | Data isn’t designed for fair and competitive compensation planning. |
Easy to access by simply setting up a free account. | Data variance as different companies calculate compensation in different ways. |
LinkedIn job adverts are job postings that employers publish, sometimes including salary ranges due to company policy or regional pay transparency laws such as the EU Pay Transparency Directive.
You can manually review these listings on LinkedIn to gauge market rates.
Pros | Cons |
|---|---|
Real-time reflection of hiring market signals. | Unreliable data as employer-submitted information is not standardised and lacks context around the company’s compensation philosophy and target percentile – making it hard to understand what a salary range actually reflects. |
Direct employer intent rather than retrospective reporting | Small sample size with manual sourcing, which makes the data statistically unrepresentative of the market. |
Data is based on the interpretations you make of the job role and level to understand whether it’s for the same role and level you’re looking for. | |
Equity and variable compensation insights are usually high-level or omitted. |
Salary.com is a compensation data website offering market estimates, with a free online salary tool called Salary Wizard.
It collects free pay data from a mix of employer-reported insights, salary surveys, and third-party sources, with limited public detail on data collection and verification methodology for free insights.
Pros | Cons |
|---|---|
More structured role definitions than crowdsourced platforms. | Free data is high-level and lacks customisation. |
Clear percentile framing for salary ranges. | Limited transparency around data verification methodology for its free tool. |
Limited to no data on fast-changing or niche roles. |
Levels.fyi is a compensation data platform focused primarily on tech roles across multiple regions and industries like retail, accounting, investment banking, gaming, and more.
It offers pay breakdowns (salary ranges and median pay) at large technology companies – including salary range heatmaps and charts.
Levels.fyi sources free data from self-reported employee submissions.
Pros | Cons |
|---|---|
Compensation data updates in real-time, giving you fresh insights. | Free data doesn’t have a stringent verification process. |
Detailed levelling and role context for major tech companies. | Salary data leans heavily toward big tech and venture-backed startups. |
Limited usefulness for non-technical or non-US roles. |
Salary Expert is a compensation comparison site powered by the Economic Research Institute’s (ERI) Assessor Platform that provides salary estimates across roles, locations, and seniority levels.
It aggregates data via a combination of salary surveys, employer reports, and statistical modeling, with methodology summaries provided at a high level.
Pros | Cons |
|---|---|
Useful salary insights for international or relocation comparisons. | Free data lacks the granularity needed for compensation decisions – making it less effective for company-level benchmarking. |
Salary insights backed by geographic and cost-of-living context. | Limited visibility into sample size for specific roles. |
Unreliable data freshness with most data sources updating infrequently rather than in real-time. |
WageIndicator is a non-profit organisation that collects wage data and labour market information across multiple countries to support labour market transparency.
It aggregates pay insights from national websites and databases, field research, and online surveys it conducts to assess salaries and working conditions.
Pros | Cons |
|---|---|
Provides insights into gig worker pay. | Data is refreshed annually – if there’s no research to back the updates, WageIndicator adjusts data according to annual inflation. |
Open access with broad international coverage. | Survey participation varies significantly by country, making the data highly variable across locations. |
Useful for macro-level wage comparisons. | Limited relevance for company-specific benchmarking – with data suited for policy analysis rather than compensation planning. |
Robert Half is a global staffing and recruitment firm that publishes annual salary guides and offers a salary calculator to give you salary ranges for specific roles across seven fields in the US.
Robert Half sources the salary data from its own talent staffing activity and third-party job postings – using AI to analyse the compiled data against key market conditions, and current and projected talent supply and demand.
Pros | Cons |
|---|---|
Partly employer-informed data rather than purely crowdsourced inputs. | US-only salary data with limited transparency into sample sizes. |
Data is reported in three levels: low, mid, and high to account for pay variability based on employee skills, experience, and organisational factors like industry, company size, and revenue. | Compensation data is limited to salary insights and seven industries, including legal, tech, and creative. |
Data typically refreshes annually as new survey results come in. |
Government pay data sources are official, publicly funded datasets produced by national statistics offices and labour departments.
They’re designed to track labour market trends and wage levels – collecting data via large-scale employer surveys and mandatory statistical reporting, often combined with census data to drive official statistical estimates.
Some examples of governmental salary data sources include:
Pros | Cons |
|---|---|
Data collection methods are standardised and methodologically rigorous, making the data credible. | Salary data isn’t fresh often since surveys typically run annually and are published months after collection. |
Large sample sizes and national coverage. | Role definitions are broad and slow to adapt to niche or emerging job roles. |
Useful for big picture salary and trend analysis. | No job levelling granularity, making the data unsuitable for pay banding, equity planning, or hiring decisions. |
Industry- and location-specific salary reports are surveys produced by industry thought leaders, communities, organisations, or trade groups.
They focus on a defined role set, function, or region and use employee-reported surveys to gather data, with surveys typically accessible through the publisher’s community, website, newsletter, or member network.
Survey sample size and methodology vary by publisher, but these are not robust compensation benchmarking surveys and should not be relied on for this purpose.
Examples of these industry- and location-specific salary surveys include:
Pros | Cons |
|---|---|
Typically free to access data. | Unreliable insights as data is self-reported, where even minor reporting errors can skew results. |
Often more up-to-date than free governmental salary data sources. | Data is limited to averages per role, which isn’t robust enough to generate usable benchmarks for compensation planning. |
Limited role standardisation across companies. |
Short answer: No, free salary data isn’t reliable for compensation planning.
Sure, free salary data is useful for building market awareness and sense-checking assumptions. But compensation planning requires accurate, relevant data in real-time that’s comparable across levels and peer groups. Most free salary data isn’t built to meet that bar.
As a result, free salary data is not reliable enough for:
That leaves free salary data appropriate only for:
Free sources typically provide high-level salary ranges for broad roles and industries, with limited transparency around data freshness, sample size, and verification.
In contrast, specialised benchmarking providers offer verified, up-to-date, granular data aligned to your hiring philosophy and the specific roles you’re hiring for.
Here’s a detailed comparison:
| Free salary data | Paid salary benchmarking tools |
|---|---|---|
Data freshness |
Often irregularly updated or based on historical averages, giving outdated salary data.
|
Depending on the tool, data is continuously refreshed in real-time. |
Compensation dataset
|
Typically limited to base salary ranges, with inconsistent or missing insights into variable pay or equity. |
Usually includes base salary, variable pay (bonuses, commissions, incentives), and equity compensation
|
Data relevance |
Drawn from broad roles, industries, and regions, with limited filtering by company size, stage, or peer group. |
Different providers offer stronger data coverage in specific industries and regions.
Allow filtering by role, level, location, industry, company size, and revenue.
And many tools also support percentile targeting aligned with compensation philosophy.
|
Data reliability
(collection methods) |
Often relies on self-reported employee data or traditional salary surveys, which increases the risk of reporting errors impacting data accuracy.
|
Commonly collected via HRIS integrations (direct employer data) reducing manual entry errors and improving data accuracy. |
Data reliability
(verification and maintenance processes) |
Limited or no formal data validation. Data is also often based on small or inconsistent sample sizes. |
Many providers apply data validation processes, including outlier detection, stale data removal, and provide visibility into sample size and confidence levels.
|
Data granularity
|
Broad or unstandardised role definitions limit usable granularity.
|
Offers role granularity (example, frontend, backend, and full-stack engineers) with some providers like Ravio giving you data for niche or emerging roles (example, AI engineers).
|
Traditional salary surveys are generally more reliable than crowdsourced platforms – but only under specific conditions when:
However, many free surveys still rely on self-reported data, increasing the risk of errors from manual submissions. And because surveys are typically conducted at a large scale only once or twice a year, results are often aggregated and published months after collection – making the data outdated by the time it reaches you.
Ultimately, the difference isn’t “survey vs crowdsourced.” It’s whether the data is current, accurate, and representative of your actual workforce context.
Free salary data can be genuinely useful as long as you’re clear on its intended use cases and limitations.
As you review free pay data sources, step back from the numbers to look at the signals behind them. Pay attention to whether:
Just as importantly, consider which roles, company sizes, and industries the data reflects – and which are underrepresented.
These checks aren’t about finding a perfect free source. They’re about understanding how much confidence a source actually deserves before you use it to inform real compensation decisions.
Ultimately, remember that free salary data is best used as a starting point, not a source of truth.
And if you’re ready to move beyond broad market averages, reliable, human-verified benchmarks can help. One place to start is Ravio’s three free benchmarks.
Free salary data sources are publicly accessible tools and reports that provide pay information at no cost. These include crowdsourced platforms, job ads, salary surveys, government datasets, and free tools from modern compensation benchmarking platforms. They’re designed for general reference and market awareness, not formal compensation planning.
Free salary data is collected in several ways. Some platforms rely on self-reported employee submissions. Others use employer-reported job ads, statistical modelling, or government surveys. Some salary benchmarking tools provide real-time data from HRIS integrations. Each method varies in accuracy, freshness, and reliability.
Trust salary data only as a starting point for high-level market analysis and sense-checking assumptions – not for compensation planning. Although free salary data gives you a broad understanding of market ranges, it lacks the reliability, relevance, and granularity you need to build pay bands or conduct equity analysis.
Relying on free salary data becomes risky when you use it for salary band design, equity compensation analysis, or competitive pay decisions. At this stage, small inaccuracies compound into long-term cost, retention, and compliance risks. Free data often lacks consistent levelling, representative samples, and up-to-date market signals required for precision.
The biggest limitations of free salary data are outdated insights, sample bias, limited role and company relevance, and incomplete total rewards data. Many sources rely on self-reported or averaged figures – making it difficult to get accurate data, compare insights for like-for-like roles, or understand how salary, equity, bonuses, and benefits work together.
Free salary data has limited value for pay equity analysis. While it can highlight high-level trends, it lacks the demographic controls, sample consistency, and standardized role definitions that you need for accurate equity assessments. Relying on it for pay equity decisions can lead to misleading conclusions and increased compliance risk.
People teams should validate free salary data by checking data recency, role and level consistency, and sample transparency. Confirm how data freshness is handled, whether roles are over- or under-segmented, and if contractor and full-time pay are separated. Also, assess whether benchmarks reflect total compensation and if contributing companies represent your industry, size, and hiring context. Finally, cross-check multiple sources for consistency, not precision.
The most up-to-date free salary data typically comes from compensation platform-derived free online tools such as Ravio’s free benchmarks, as it reflects real-time total compensation data that’s verified by a human team of benchmarking experts. Government datasets and salary surveys are also reliable, but update less frequently. Crowdsourced platforms often lag behind fast-moving labour markets.
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