How to find and evaluate reliable tech salary benchmarks

If you're benchmarking compensation for a tech company, generic market data isn't enough. Reliable tech salary benchmarks scope the data to your actual peer group – the companies at a similar stage, size, and location that you actually compete with for talent.

And because base salary alone doesn’t show how startups actually pay, tech benchmarks should cover total compensation (salary, bonuses, equity, and benefits) so you can make offers that reflect the full value of what you’re offering candidates.

Otherwise, decisions can quickly fall out of line with the market – creating real risks such as uncompetitive offers, missed hires, inflated salaries, and high attrition rates.

In this guide, we cover key tech salary benchmarks, where to find reliable data, and how to evaluate whether benchmarks are robust enough to support pay decisions.

Quick refresher: What are tech salary benchmarks?

Tech salary benchmarks are market compensation data showing how companies across the tech industry pay their employees – including technical roles like software engineers, data scientists, and product managers.

HR and compensation teams use these benchmarks to compare internal pay against market rates, helping them set competitive offers in the tech talent market, build salary bands, and review compensation as the market evolves. 

Salary benchmarks in tech rarely refer to base salary alone though. They usually reflect different components of compensation, including:  

• Base salary: the fixed annual pay for a role
• Bonuses: performance-based cash compensation where applicable
• Equity: stock options or share grants that form a significant part of compensation in many tech companies
• Benefits: non-cash perks such as healthcare, pensions, and allowances
• Total compensation: the combined value of salary, bonuses, and equity.

Because equity and variable pay are common in the tech industry, looking only at the base salary can give you an incomplete picture of how companies actually compensate employees.

Compensation structures can also vary significantly across the tech sector. 

Startups and scaleups, for example, often combine lower base salaries with meaningful equity, while more mature organisations rely more heavily on cash compensation.

As a result, tech salary benchmarks need to reflect the specific talent markets you compete in, rather than the broader talent market.

How are tech benchmarks different than general salary benchmarks?

Tech salary benchmarking requires a more specialised approach than general salary benchmarking. Several factors make tech salary benchmarking different:

1. Tech companies compete within specific peer groups
In tech, compensation varies significantly by your peer group – companies at your size, revenue, and stage that you compete with for talent. Benchmarks, therefore, need to reflect where you actually hire from and lose talent to, so your compensation is fair and competitive. 

2. Equity is a core part of tech compensation
Many tech companies include stock options or in their compensation packages. As a result, benchmarking base salary alone rarely shows the full value of compensation offered to employees.

3. Tech markets move quickly
Demand for technical skills can change rapidly as technologies evolve and new roles emerge. For example, hiring demand for AI and Machine Learning roles alone has grown by 88% this year. That’s why it’s essential for tech salary benchmarks to be based on recent market data, not outdated figures.

4. Global and remote hiring is common 

Many tech companies hire across multiple countries or operate fully remote or hybrid teams. This means compensation teams often need benchmarks that support location-based pay decisions – not data limited to a single national market.

5. Tech salaries vary widely across global markets
Pay for the same role can differ significantly across regions. For example, software engineering salaries in the US may be far higher than those in the UK, Europe, or India. Reliable tech benchmarks need strong geographic coverage to capture these differences.

6. Tech companies have different role structures
Tech companies rely on roles such as software engineers, data scientists, and infrastructure specialists, while other industries operate with entirely different job structures. As a result, wider salary benchmarks don’t reflect the roles tech companies actually hire for. 

Key 2026 tech salary benchmarks 

In 2026, tech salaries in Europe average around £50-70k with demand for AI and ML roles growing (more on tech compensation and hiring trends below). 

Here’s a breakdown of market median salary range for key IC (non-management) roles in tech, from P3 seniority level (established, mid-level) to P4 (advanced):

Software Engineer (P3-P4)

• UK: £70,000-£88,900
• Germany: €78,700-€94,000
• France: €68,000-€83,500

Machine Learning Engineer (P3-P4)

• UK: £69,400-£87,100
• Germany: €80,200-€94,400
• France: €72,600-€89,000

Cloud Engineer (P3-P4)

• UK: £70,300-£88,400
• Germany: €76,400-€91,900
• France: €69,700-€80,800

DevOps Engineer (P3-P4)

• UK: £68,400-£87,200
• Germany: €76,900-€92,900
• France: €68,400-€81,300

Data Scientist (P3-P4)

• UK: £68,300-£85,800
• Germany: €79,200-€95,300
• France: €68,100-€83,900

Cyber Security & Risk Management (P3-P4)

• UK: £64,400-£82,700
• Germany: €71,700-€87,000
• France: €60,800-€77,500

IT Manager (P3-P4)

• UK: £55,200-£72,800
• Germany: €64,700-€80,700
• France: €57,300-€74,200

Please note that as of March 2026, these are accurate benchmarks derived from Ravio’s real-time compensation benchmark tech dataset. 

The exact benchmarks always vary by the specific role and job level, and the target percentile. If you’re looking to determine compensation for different job positions and target percentiles, try out Ravio’s 3 free benchmarks:

Key compensation drivers and trends in tech 2026

• Median salaries for Software Engineering grew just 1-2% across Europe in the last year, much slower growth in benchmarks compared to previous years, suggesting a market stabilisation.
• Salary premiums increase as companies progress through funding stages, with larger gaps at senior levels according to Ravio’s 2026 Compensation Trends report.
• Senior roles command significantly higher premiums, especially at late-stage startups. Late-stage startups pay 31–34% more for senior talent – nearly double the premium seen at mid-level.
• The UK leads Europe in employee equity participation. 58% of UK tech startups now offer equity to all employees, while Germany, France, and the Netherlands lag behind, and France and Sweden have seen declines.
• Hybrid work remains the dominant model. Most European tech companies now offer hybrid setups (e.g. 85% in the UK), with a growing share (39%) also supporting fully remote roles.
• Hiring rates have stabilised after earlier slowdown – holding steady at 29% in 2025 (unchanged from 2024), indicating a more cautious but consistent approach to team growth.
• Entry-level hiring has dropped sharply. Hiring for junior roles (P1–P2) has fallen by 73% year-on-year, with the steepest declines in People, Marketing, and Engineering roles.
• Administrative roles are declining as automation increases. Administrative hiring is down 32.5% globally, reflecting the growing use of AI tools to replace routine and repetitive tasks according to Ravio's tech job market report.
• AI roles are commanding a growing pay premium. Demand for AI and machine learning talent is rising rapidly, with AI/ML hires increasing by 88% in 2025 based on an analysis of Ravio's compensation database. 

Why does tech salary benchmark quality matter for pay decisions

Because compensation teams rely on benchmark data to make core pay decisions – including setting salary bands, structuring job offers, and responding to retention risks – the quality of your tech benchmarks directly affects how competitive those decisions are.

If the data guiding your pay decisions is inaccurate, outdated, or not sufficiently comparable to your talent market, those decisions can quickly move out of line with the market. In turn, leading to missed hires, inflated offers, or avoidable employee turnover.

In fact, when benchmark data is unreliable, several compensation problems eventually follow:

• Inaccurate salary bands. Salary bands are set too low or too high when benchmarks are outdated or based on the wrong peer group.
• Uncompetitive job offers. You lose candidates to better-paying competitors when offers rely on inaccurate benchmarks.
• Overpaying for new hires. Hiring managers increase offers to secure candidates, inflating pay levels across the team without reliable benchmarks.
• Weak retention and counteroffer decisions. Counteroffers miss the mark when compensation teams use outdated market data.
• Delayed response to market shifts. You react late to shifts in pay and growing demand for roles and skills when benchmarks aren’t real-time, putting you on the back foot in hiring and retention decisions.

These issues often stem from the quality of the underlying tech compensation data.

Many free salary data sources rely on self-reported inputs, outdated surveys, or limited datasets that don’t align with your hiring market. 

“Usually, what we find in the big surveys is that larger companies are the only ones to participate, rather than the smaller tech companies that might be more relevant to us,” explains Bolt’s Senior Compensation Manager, Evert Kraav. 

But when you’re making compensation decisions in tech, you need data from companies that mirror your own structure and growth trajectory in real-time – not  Google salaries that you can’t compete with. 

Meaning: not every salary figure you see online is suitable for making pay decisions.

Compensation teams like Bolt instead rely on decision-grade market data designed to support pay decisions. This typically includes:

• Fresh data that reflects the current hiring market
• Sufficient sample sizes for each role and location
• Verified compensation inputs, rather than self-reported salaries or manual submissions 
• Comparable peer groups, such as companies of a similar size, stage, or industry.

These characteristics ensure that the salary bands, job offers, and retention decisions you make are based on reliable market data. 

Where can you find tech salary benchmarks?

From real-time benchmarking platforms to traditional salary surveys and publicly available salary reports, there are multiple places you can find tech benchmarks.

Each source provides a different level of reliability, coverage, and usefulness depending on how you plan to use the data. 

Let’s look at 5 broad sources for tech salary benchmarks (specific top providers and how to evaluate them is in the sections below): 

1. Real-time compensation benchmarking platform (e.g. Ravio, Pave)

Real-time benchmarking platforms use HRIS and ATS integrations to collect and continuously update compensation data directly from organisations – often tech companies depending on the provider’s focus.

This ensures benchmarks are up-to-date, accurate, and role-specific; capturing the quickly changing tech talent market.

Best for: Tech companies that hire regularly and need reliable benchmarks aligned to their roles, locations, and peer group.

2. Salary survey providers (e.g. Radford, Korn Ferry) 

Many large global HR consultancies run salary surveys using a give-to-get model, collecting compensation data from large corporations and publishing results annually or biannually. 

You can purchase either the full dataset or a custom tech group report tailored to your hiring location and industry.

Best for: Multinational corporate tech companies with in-house teams handling manual survey submissions to access and use data.

3. Employee-reported salary sites (e.g. Glassdoor) 

Websites like Glassdoor collect salary data directly from employees who anonymously submit their salary, location, job title, and other details. The data is then aggregated to produce averages that give you a salary range per role.

Best for: Early-stage companies that need a rough sense of market pay and don’t have stakeholder buy-in for salary benchmarking tools (yet).

4. Job advert salary ranges (e.g. LinkedIn, Indeed)

Many tech companies share salary ranges in their job postings, which you can review on LinkedIn or Indeed to get a rough sense of market rates. Some job boards, like Reed and Michael Page, also aggregate this data from job adverts into free salary checker tools.

Best for: Spot-checking current hiring trends and teams that don’t have stakeholder buy-in for salary benchmarking tools.

5. Industry salary reports (e.g. Fruition’s tech salary guide) 

Hiring and HR organisations – such as recruitment agencies, consultancies, and software providers – often publish annual tech salary reports for tech roles, typically based on community surveys or internal hiring data.

Best for: Understanding general pay trends in your industry.

How to evaluate tech salary benchmark reliability  

As the sources above show, not all salary benchmarks are equally reliable. The way data is sourced, verified, updated, and delivered all affect how relevant and usable it is.

Equally important here is assessing whether the data reflects your roles, locations, and company context – such as company size, stage, and industry.

So before using any benchmark to guide pay decisions, ask these 7 questions to evaluate its quality:

• Does the data reflect your peer group? Look for data from tech companies of a similar size, stage, industry, and location – ideally those you compete with for talent. Ask for a participant list to assess whether the benchmarks reflect your actual talent market.
• Is there enough data for your roles and locations? Small sample sizes can make benchmarks unreliable, especially for niche roles or specific locations. Look for benchmarks with sufficient data points for the roles and regions you’re hiring in.
• What does the data include? Some benchmarks only cover base salary, while others include bonuses and equity. For tech salary benchmarking, total compensation often matters more than base salary alone, so it’s important to understand which compensation components are included. 
• Is the data recent? Tech job markets change quickly, especially for in-demand roles. Benchmarks based on data that is months old don’t reflect current salary expectations or hiring conditions.
• How is data collected? Manual submissions can introduce errors, while automated collection – for example via HRIS integrations – pulls and updates data directly from source systems, improving accuracy.
• Is the data verified? Raw, unverified data can include duplicates, outliers, gaps, or inconsistently mapped roles, which reduces accuracy. Reliable benchmarks use structured verification processes to clean and validate data, converting it into statistically meaningful benchmarks.
• How are roles mapped and levelled? Manual job mapping can be time-consuming and interpretation errors often lead to inconsistent comparisons. Reliable benchmarks use automated or expert-led mapping, giving you usable benchmarks (rather than raw compensation data), with roles aligned to comparable levels and functions.

The bottom line: Reliable tech salary benchmarks are recent, verified, usable, and based on comparable companies and roles. Without these factors, salary data may not be accurate enough to guide pay decisions.

8 best sources for tech salary benchmarking 

Different benchmarking providers vary significantly in how they collect data, how often it updates, and how usable it is for making competitive offers to new hires. 

Below are the top 8 sources for tech salary benchmarks:

1. Ravio

Best for: High-growth tech companies, especially those hiring across Europe and globally. 

Data methodology: Real-time compensation data for 100+ roles collected directly from scaling tech companies via HRIS integrations, with structured verification and human-led job mapping.

Key strengths: 

• Total compensation coverage (base salary, equity, variable pay, and employee benefits).
• Real-time, continuously updated benchmarks via live HRIS integrations with 1,500+ tech companies like Wise, Deezer, and Bolt among others.  
• Raw data verified and used to build statistically validated benchmarks by a team of data scientists.
• Benchmarks tailored to tech startups and scaleups, with deep insights for niche and emerging roles. 
• Expert-led job mapping during onboarding when the team maps your roles and levels to our job architecture. 
• Advanced filters for industry (e.g. fintech salaries vs overall tech), company size, headcount, location, and funding stage for relevant comparisons.

Limitations: 

• Less relevant for non-tech organisations.

2. Pave

Best for: US-based tech startups and VC-backed companies looking for accurate North American benchmarks. 

Data methodology:  Real-time compensation data via ATS integrations, heavily weighted toward US companies.

Key strengths: 

• Strong US market coverage (majority of dataset).
• Total compensation visibility (salary, equity, and variable pay).
• AI-powered job mapping.

Limitations:

• Limited European and global coverage.
• No equity, benefits, and variable pay benchmarks outside of Pave core market. 
• Fully AI-led job mapping without human oversight to correct nuances can reduce accuracy.

3. Carta Total Comp

Best for: US-based, VC-backed private companies already using Carta, the cap table management platform.

Data methodology: HRIS integrations and Carta’s proprietary dataset of private market companies. 

Key strengths: 

• Strong equity benchmarking tied to ownership data.
• Comprehensive salary and equity data coverage across the U.S. private market.
• Native Carta cap table integration for tracking and forecasting ownership, share allocations, and vesting schedules.

Limitations: 

• Dataset limited to private companies.
• Poor coverage in Europe and other international markets.
• Salary benchmarks are less robust than equity data due to Carta’s cap table origins.

4. Assemble (by Deel)

Best for: US-based private healthcare and biotech companies.

Data methodology: Combination of manual uploads, a proprietary dataset called CompGrid, and private benchmarks via an integration with Carta.

Key strengths: 

• Strong niche datasets (biotech, private companies).

Limitations: 

• Relies heavily on manual data uploads and job mapping as it’s based on aggregating data from multiple sources.
• Carta-sourced equity data is mature, while salary data is less robust.

5. CompUp

Best for: India-based startups and mid-market companies. 

Data methodology: Real-time benchmarks from HRIS and payroll integrations across 250+ Indian startups. 

Key strengths: 

• Strong India-specific market coverage.
• Total compensation data (salary, equity, variable pay).
• Peer-group filters to review against Indian startups by stage, size, and industry.

Limitations: 

• India-only dataset with no global coverage. 
• Less reliable for niche, senior, or global roles since CompUp has a startup-focused data scope.
• No advanced filter for funding stage or custom cohorts.

6. Willis Towers Watson

Best for: Large global tech enterprises looking for broad data in the US and UK.

Data methodology: Annual or periodic salary surveys with manual data submission. 

Key strengths:

• Broad industry-specific compensation datasets.
• Strong brand credibility (helps with leadership buy-in).

Limitations: 

• WTW benchmarks can be outdated due to survey cycles.
• Broad dataset that poorly reflects high-growth tech companies.
• Manual submissions introduce risk of reporting errors.
• Manual job mapping increases workload and inconsistency.
• Less granular data for niche roles like ML Engineering or emerging markets like Portugal. 

7. Mercer

Best for: Multinational tech enterprises that need global compensation data. 

Data methodology: Large-scale salary survey with give-to-get data submission model. 

Key strengths:

• Total rewards data coverage. 
• Trusted brand among corporate leadership.

Limitations: 

• Mercer benchmarks are often outdated by the time they're published and aren't refreshed until the next survey cycle.
• Manual submission increases risk of reporting error, impacting data accuracy. 
• Manual job mapping required to match internal data to Mercer’s huge job catalogue.

8. Radford (Aon)

Best for: Large global tech enterprises.

Data methodology: Large-scale annual or biannual surveys with separate tech-specific surveys. 

Key strengths: 

• Broad, global total compensation data coverage. 
• Established provider well-recognized among corporate leadership.

Limitations: 

• Radford's annual and biannual refresh cycles means data isn’t up-to-date.
• Manual submissions to access data.
• Manual job mapping to providers’ complex job roles and level taxonomies.

Make competitive offers with the right tech benchmarks

Most compensation decisions don’t fall short because teams lack data – they fall short because the data isn’t reliable enough to use.

The most useful tech salary benchmarks reflect your hiring market, use verified data, and provide usable comparisons across roles and levels.

Once you have that, setting salary bands, making competitive offers, and responding to retention risks becomes far more straightforward.

If you’re looking to benchmark pay against companies similar to yours – using up-to-date compensation data – it’s worth exploring Ravio’s 3 free benchmarks designed specifically for tech companies and scaling teams.

FAQs 

Where can I find free tech salary benchmarks?

Free tech salary benchmarks are available on platforms like Glassdoor, job boards, and industry salary reports. These can give a rough market view but often lack data verification, context, and recency. Use them for early research, but the data isn’t reliable enough for setting salary bands or making competitive job offers.

How do companies benchmark tech salaries?

Companies benchmark tech salaries by comparing internal roles to external market data This involves matching roles and levels, selecting relevant peer groups, and analysing total compensation. The goal is to set salary bands, make competitive offers, and ensure pay aligns with current market conditions. 

How often should tech salary benchmarks update?

Tech salary benchmarks should update frequently – ideally in real time or at least monthly. In fast-moving hiring markets, annual or quarterly data can quickly become outdated. Regular updates ensure your salary bands and offers reflect current market conditions, helping you stay competitive and avoid overpaying or losing candidates.

How do tech companies benchmark equity?

Tech companies benchmark equity by comparing stock options or share grants against market data for similar roles, locations, and company stages. This includes analysing equity ranges, vesting structures, and dilution impact. Equity benchmarks are typically sourced from specialised platforms that provide total compensation data, including salary, equity, and variable pay.

What is the best salary benchmarking platform for startups?

The best salary benchmarking platform for startups provides real-time, peer-group relevant benchmarks, including salary and equity benchmarks. Salary benchmarking tools like Ravio, Pave, and Carta are commonly used. Each differs in where its data is strongest, how the data is verified, and how accurately roles are mapped for like-for-like comparisons.

What compensation benchmarking tools exist for tech companies?

Compensation benchmarking tools for tech companies include real-time platforms like Ravio and Pave, and traditional survey providers such as Mercer, Korn Ferry, and Radford. The best platforms provide up-to-date, relevant, and usable benchmarks that reflect your specific hiring market (where you hire, the roles you’re benchmarking, and the companies you compete with for talent) – not broad or outdated salary data.

Are employee-reported salary sites reliable for benchmarking?

Employee-reported salary sites like Glassdoor are not fully reliable for benchmarking. The data is unverified, often outdated, and lacks context such as company size or compensation structure. While useful for directional insights, you shouldn’t use them to set salary bands or make compensation decisions.