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 Teams often end up working with outdated averages, inconsistent role definitions, or partial views of compensation that don’t reflect how salary, equity, and benefits actually come together.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:
- The 13 best free salary data sources in 2026
- How reliable free salary data actually is
- Where free salary data is helpful – and where it becomes risky
You’ll leave knowing how to interpret free salary data and its limitations before applying it to compensation decisions.
13 best free salary data sources in 2026
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:
1. Ravio
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.
There’s also a 30-day free trial available to access the platform’s full compensation benchmark dataset and live market insights.
What free salary data does Ravio cover:
- Real-time global base salary, variable pay, equity, and employee benefits data
- Total compensation data for niche roles in emerging markets like Estonia, Czechia, and Portugal is also available
Ravio free salary data pros and cons:
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. |
2. Pave
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.
What free salary data does Pave cover:
- Base salary, equity, and variable pay data in North America
- Base salary data (no equity, benefits, or variable pay benchmarks) for Europe or other regions.
Pave free salary data pros and cons:
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. |
3. Salary Insights by Deel
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.
What free salary data does Salary Insights cover:
- Base salary averages and market ranges
- Regional pay trends (across countries and major markets)
- No bonuses, benefits, and equity data available in the free salary data tool
Deel free salary data pros and cons:
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. |
4. Glassdoor
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.
What free salary data does Glassdoor cover:
- Base salary, with occasional insights into bonuses or additional compensation
- Equity, benefits, and compensation bands are inconsistently reported.
Glassdoor free salary data pros and cons:
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. |
5. Indeed
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.
What free salary data does Indeed cover:
- Estimated base salary ranges
- Hourly or annual pay, depending on role
- High-level role and location breakdowns
- Benefits, equity, and variable pay are rarely detailed
Indeed’s free salary data pros and cons:
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. |
6. LinkedIn job ads
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.
What data do LinkedIn adverts cover:
- Base salary ranges typically (when disclosed)
- Role title, seniority level, and location
- Occasional benefits, equity, or variable compensation insights
LinkedIn job adverts’ salary data pros and cons:
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. |
7. Salary.com
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.
What data does Salary.com cover:
- Base salary ranges
- Percentiles (e.g. median, 25th, 75th)
- Benefits, equity, and variable pay are typically gated behind paid products.
Salary.com free data pros and cons:
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. |
8. Levels.fyi
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.
What free salary data does Levels.fyi cover:
- Total compensation data (base salary, equity, and bonuses)
- Job leveling and title context
- Company and location breakdowns
Levels.fyi free salary data pros and cons:
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. |
9. Salary Expert
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.
What free data does Salary Expert cover:
- Average base salary estimates
- Cost-of-living adjusted figures
- General location-specific compensation comparisons
- Limited coverage of equity, bonuses, and benefits
Salary Expert free data pros and cons:
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. |
10. WageIndicator
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.
What free salary data does WageIndicator cover:
- Base salary data
- Regional cost of living averages
- Country- and region-level comparisons
- Gender pay gap indicators (in some regions)
- Limited benefits, equity, and company-level detail
WageIndicator free salary data pros and cons:
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. |
11. Robert Half
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.
What free salary data does Robert Half cover:
- Base salary ranges
- Hiring trend insights
- No bonuses, benefits, or perks data
Robert Half's free salary data pros and cons:
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. |
12. Governmental salary data sources
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:
- Office for National Statistics (UK). Gives you salary data in the UK, covering average earnings, industry-specific, and occupation data broken down by demographics and work status (full- or part-time).
- Bureau of Labor Statistics (US). Publishes data on annual salary, hourly wages, median/mean earnings, and pay by percentiles across occupations and industries, broken down by demographics, experience, and location.
- Eurostat (EU). Provides average salary ranges and minimum wages across EU member states – broken down by employee (age, gender, education, role) and employer (enterprise size) characteristics.
- Statistics Canada (Canada). Offers average wage data, weekly, hourly, and annual earnings, and income by industry, geography, and demographics.
- Australian Bureau of Statistics (Australia). Publishes average weekly and hourly earnings by occupation, industry, demographics, and state – as well as data on gender pay gap.
What free salary data do governmental sources typically cover:
- Median and mean salary ranges
- Industry- and region-level breakdowns
- Employment trends and projections
- No benefits, equity, and company-specific compensation structures
Free governmental salary data pros and cons:
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. |
13. Industry- and location-specific benchmark reports
Industry- and location-specific salary reports are survey-based benchmarks 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.
Survey sample size and methodology vary by publisher, with surveys typically accessible through the publisher’s community, website, newsletter, or member network.
Examples of these industry- and location-specific salary surveys include:
- Exit Five salary surveys for marketers. Gives you salary insights focused on B2B marketing roles, primarily in North America.
- Sales Enablement Collective’s Sales Enablement Salary and Landscape Report. Publishes compensation insights for sales roles based on survey responses from enablement professionals.
- The State of Dutch by Techleap. Covers compensation data for tech roles in the Netherlands.
- PNP Staffing Group. Publishes annual nonprofit salary surveys covering roles across major US Northeast cities.
- The Engineer’s Salary Survey Report. Gives you annual salary data for engineering roles in the UK, with breakdowns by discipline, experience level, location, and employment type
- Product Marketing Alliance’s Product Marketing Salary Survey. Provides global pay insights for product marketers based on community surveys.
What free salary data do industry- and location-specific reports data typically cover:
- Base salary ranges by role and seniority
- General location-based pay differences
- Occasional bonuses or variable compensation data
- Contextual insights tied to industry trends
Free industry and location-specific salary data pros and cons:
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. |
Is free salary data reliable for compensation planning?
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:
- Fair compensation decisions. Free data is often outdated, poorly matched to the roles you’re hiring for, and limited to average base salary ranges – allowing gaps in relevance and freshness to compound into unfair decisions.
- Internal levelling and role architecture. Job definitions vary too widely across sources – making it difficult to map roles consistently across levels and teams.
- Pay band design. Free data rarely supports consistent levelling or percentile targeting – both of which are essential for defining defensible ranges across roles and levels.
- Pay equity and pay gap analysis. Pay equity analysis depends on consistent data samples and demographic controls, both of which free data often lacks, weakening conclusions and increasing the risk of misleading or incomplete equity assessments.
That leaves free salary data appropriate only for:
- Market awareness. Understanding general pay levels for a role, function, or region.
- Directional checks. Sanity-checking whether your compensation is broadly above, below, or near market.
- Early-stage or exploratory research. Especially for new roles, early hiring, or budget discovery.
- Spotting macro trends. Such as rising demand in certain roles or regions.
How does free salary data differ from paid salary benchmarking tools?
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).
|
Are salary surveys more reliable than crowdsourced platforms like Glassdoor?
Traditional salary surveys are generally more reliable than crowdsourced platforms – but only under specific conditions when:
- The sample size is large and relevant to your company
- Roles and levels are clearly defined
- Methodology and data validation processes are transparent
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.
Wrapping up: Evaluate free salary data without over-trusting it
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:
- Salary estimates are current or influenced by outdated data
- Roles and levels are clearly defined rather than over- or under-segmented
- Compensation insights extend beyond base salary to include total rewards
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.
FAQs
What are free salary data sources?
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.
How do free salary data sources collect pay data?
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.
Should I trust free salary data?
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.
When does relying on free salary data become risky?
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.
What are the biggest limitations of free salary data?
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.
Can free salary data support pay equity analysis?
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.
How should People teams validate free salary data?
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.
Which free salary data sources are most up to date?
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.
