Pave Compensation Data: How It Works, Where It Wins, and What HR Teams Should Watch For

Introduction

Pave compensation data has become a go-to benchmarking source for HR and Total Rewards teams at tech companies seeking real-time market insights beyond traditional salary surveys. This guide breaks down exactly how Pave collects and structures its compensation data, where it excels, where gaps appear, and how it compares to modern real-time salary intelligence platforms like SalaryCube.

This article focuses specifically on Pave’s data methodology, coverage, and practical applications for U.S.-based compensation teams—not a comprehensive review of every compensation platform on the market. The scope includes salary and equity benchmarking, give-to-get data models, integration requirements, and workflow fit. Topics outside this scope include Pave’s performance management features, international compensation strategy, and detailed pricing comparisons.

The target audience is HR Directors, Compensation & Benefits Managers, and People Ops leaders at mid-market and growth-stage companies who are evaluating whether to move beyond spreadsheets and legacy surveys. If you’re exploring how to benchmark compensation more efficiently and wondering whether Pave fits your organization’s needs, this article will help you make an informed decision.

What is Pave compensation data? Pave compensation data is a real-time dataset built from anonymized employee pay records contributed by participating companies through HRIS and cap table integrations. The dataset covers base salary, variable pay, and equity grants, primarily for VC-backed tech companies in the U.S. and Canada. It’s one of several modern sources HR teams can use for compensation benchmarking—but it operates on a give-to-get model that requires data sharing for full access.

By the end of this article, you will:

Understand exactly what’s inside Pave’s compensation dataset, including coverage, structure, and real-time aspects
Recognize the strengths and blind spots of Pave compensation data compared to traditional surveys and real-time, open-access platforms
Learn when Pave is a strong fit and when alternative tools like SalaryCube may be more defensible or efficient
Get a concrete evaluation checklist for choosing a compensation data provider in 2025–2026
Know how to mitigate common challenges when relying on Pave as a primary data source

Understanding Pave Compensation Data

Pave operates as a compensation management platform, but its core value for benchmarking teams rests on the underlying compensation dataset. Before evaluating whether Pave fits your organization, you need to understand where the data comes from, how current it is, and which roles and geographies it actually covers.

What “Pave Compensation Data” Actually Is

Pave’s dataset is built from integrated HRIS and cap table management platform connections at participating companies, creating a give-to-get model where organizations contribute anonymized employee pay data to unlock access to benchmarks. The data covers base salary, target variable pay (bonuses and commissions), and equity grants for employees across job levels—from junior individual contributors to executives.

The scope focuses primarily on private tech companies in the U.S. and Canada, with smaller coverage in Europe and limited support for non-tech or blue-collar roles. Pave significantly expanded its data pool through the 2022 Option Impact acquisition, which increased depth for startup and high-growth tech equity benchmarks. This dataset powers Pave’s benchmarking tools, compensation bands, and planning modules that help companies price jobs and run merit cycles.

Give-to-Get Data Model and Integrations

The give-to-get model means companies contribute anonymized employee compensation data via HRIS integrations to receive access to market benchmarks. This is fundamentally different from traditional salary surveys where participation is optional and data collection happens through periodic submissions.

Typical integrations include HRIS platforms like Workday, BambooHR, Gusto, and Rippling, along with cap table management platforms such as Carta and Pulley. Data flows into Pave on a recurring basis through real-time integrations, creating a continuously updated data pool rather than a static annual snapshot. The advantage is fresher data; the tradeoff is that unlocking benchmarks requires IT and legal sign-off, integration work, and organizational comfort with sharing sensitive pay data.

HR teams should anticipate internal change management requirements. Data privacy concerns, SOC 2 compliance verification, and executive approvals often slow down implementation timelines. Organizations with strict data-sharing policies may find the give-to-get model creates friction that delays access to compensation data.

What’s Inside the Dataset: Roles, Levels, and Equity

Pave’s compensation data focuses heavily on VC-backed tech roles across engineering, product, go-to-market, and G&A functions. Coverage spans junior through executive levels, with particularly strong data points for technical roles common at startups and scale-ups.

The dataset includes:

Base salary benchmarks by role family, level, and location
Bonus and commission data for variable pay components
Equity grants including new hire awards, refresh grants, and unvested holdings (with advanced equity data available to Pro subscribers)
Common startup locations including San Francisco, New York, Seattle, Austin, Toronto, and other major tech hubs

Jobs are mapped into standardized families and levels using machine learning algorithms that match diverse job titles to Pave’s proprietary architecture. This normalization matters because it allows your “Staff Engineer” to be compared against a consistent market level rather than relying on title-matching alone. However, the quality of your benchmarks depends on how accurately your roles map to Pave’s structure.

Understanding what’s in the dataset is only the first step. HR teams also need to assess how fresh, defensible, and complete those benchmarks are compared to alternatives—which is where direct comparisons become essential.

How Pave Compensation Data Compares to Other Market Data Sources

Now that you understand Pave’s data foundation, the next question is how it stacks up against traditional salary surveys, employee-reported sites, and modern real-time salary intelligence platforms. Each source has different strengths, and compensation leaders often need multiple sources to make defensible pay decisions.

Pave vs Traditional Salary Surveys

Traditional survey providers like Radford, Mercer, ERI, and Culpepper operate on annual or biannual cycles. Participating companies submit data, which is then aggregated, validated, and published—often months after collection. The output is typically static PDFs or Excel files that reflect market conditions from the previous year.

Pave’s approach contrasts sharply with this model. By pulling data continuously from HRIS connections, Pave can reflect compensation changes within weeks rather than waiting for the next survey cycle. For fast-moving tech companies where market rates shift rapidly, this real-time approach can better capture current conditions between 2022 and 2026.

The tradeoffs are meaningful. Traditional surveys offer broader industry coverage spanning manufacturing, healthcare, public sector, and other industries where Pave has minimal data. Survey providers also have decades of established methodology that auditors and compensation committees recognize. Pave’s tech-heavy dataset may be more current for startups but lacks the industry breadth that diversified organizations require.

When it comes to defensibility—justifying compensation decisions to Finance, audit committees, and executives—both approaches have merit. Survey data carries legacy credibility; Pave data offers recency. Many compensation teams use both, applying Pave for tech roles and surveys for non-tech functions.

Pave vs Employee-Reported and Job Ad Data

Employee-reported sources like Glassdoor, Levels.fyi, and Blind provide accessible salary data without requiring formal participation. Job ad scraping from Indeed and LinkedIn pay ranges offers another free data source. These options are tempting because they require no integration work and provide instant access.

However, these sources are self-reported and unverified, often missing critical context like company stage, equity mix, or geographic adjustments. A salary reported on Glassdoor may reflect a signing bonus, unusual circumstances, or outdated information. For formal compensation programs where pay decisions must be defensible, employee-reported data should not serve as a primary source.

Pave improves on this dynamic with verified, employer-submitted data processed through machine learning for quality control. The give-to-get model ensures data comes from actual company records rather than individual self-reports. That said, Pave’s dataset is still a closed ecosystem—you can’t access benchmarks without contributing your own data or paying premium fees.

This limitation points toward a different category of tools: real-time salary intelligence platforms that provide access to compensation data without requiring data sharing.

Pave vs Modern Real-Time Salary Intelligence Platforms (e.g., SalaryCube)

Real-time salary intelligence platforms like SalaryCube represent a distinct approach to compensation benchmarking. These platforms provide continuously updated U.S. compensation data without requiring HRIS participation or give-to-get contributions.

Key differentiators include:

No data-sharing requirement: Access benchmarks immediately without integration projects or legal reviews
Broader role coverage: Support for hybrid roles, blended responsibilities, and emerging titles beyond traditional tech startups
Unlimited reporting: CSV, Excel, and PDF exports without additional fees or usage caps
Transparent methodology: Clear documentation of data sources and validation processes for audit defensibility

Pave remains strong when a company already uses its full compensation management suite and is comfortable with the give-to-get model. The equity data depth and integration with compensation planning workflows create value for organizations committed to the platform.

However, gaps appear for non-tech roles, non-VC-backed companies, and highly hybrid positions. Teams wanting quick, on-demand market data without integration projects may find platforms like Bigfoot Live more efficient for day-to-day salary benchmarking needs.

Summary comparison:

Factor	Pave	SalaryCube
Data access model	Give-to-get (requires data sharing)	Open access (no sharing required)
Primary coverage	VC-backed tech, U.S./Canada	U.S.-wide, multiple industries
Hybrid role support	Limited	Strong
Equity benchmarks	Deep (Pro tier)	Salary-focused with equity guidance
Reporting limits	Tier-dependent	Unlimited exports
Integration required	Yes (HRIS/cap table)	No
Understanding these differences helps compensation teams choose the right tool for each use case—or combine multiple sources for comprehensive coverage.

Practical Ways HR Teams Use Pave Compensation Data

Moving from comparisons to application, let’s examine how HR and compensation teams actually use Pave data during compensation planning cycles, hiring, and pay strategy design. These workflows illustrate where Pave adds value and where alternative approaches may complement or replace it.

Benchmarking Roles and Building Compensation Bands

A typical workflow starts with mapping internal jobs to Pave’s standardized job families and levels. Compensation teams export their job catalog, align each role to Pave’s architecture, and then pull benchmarks for base salary, variable pay, and equity by level and location.

From there, teams use Pave benchmarks to create or refresh salary ranges for core tech roles—Software Engineer II, Senior Product Manager, Head of Engineering, and similar positions. The data allows teams to set range minimums, midpoints, and maximums based on market percentiles (typically P25, P50, P75).

Many users replace spreadsheet-based compensation bands with Pave’s built-in band management tools, which connect directly to the benchmarking data. However, even with automated tools, teams should validate ranges against business constraints, internal equity, and role-specific factors that benchmarks may not capture.

SalaryCube’s salary benchmarking tools offer a similar workflow for building pay ranges with real-time data, particularly for organizations that want quick benchmarks without the integration overhead.

Supporting Offers and Merit Cycles with Market Data

Day-to-day compensation workflows often require fast answers. Recruiters need to know competitive offer ranges for a Senior Data Engineer in Denver. Hiring managers ask whether a candidate’s expectations are reasonable for a Series B company in Austin. These decisions happen on tight timelines.

Pave users access benchmarks through the platform to set initial offer ranges by role, level, and location. During annual or biannual merit cycles, Total Rewards teams use the same benchmarks to identify employees who have fallen below market or exceeded their compensation bands. This analysis feeds into merit increase budgets and promotion adjustments.

Key metrics derived from Pave benchmarks include compa-ratios (individual pay divided by range midpoint), band penetration (where employees fall within their range), and market position relative to target percentiles. These metrics help managers and executives understand compensation fairness across the organization.

For teams wanting to run quick market checks before opening requisitions—without waiting for annual survey cycles—Bigfoot Live data provides on-demand U.S. compensation insights that complement or replace slower data sources.

Equity Benchmarking and Total Rewards Communication

Pave’s depth in equity data is a significant differentiator. Pro subscribers access benchmarks for option grants, RSU levels, refresh equity, vesting schedules, and burn rates—segmented by company stage, funding round, and location.

Practical applications include:

Benchmarking initial equity grants for engineering and leadership hires against market norms
Modeling dilution and equity refresh strategies using market anchors
Creating total rewards statements that communicate the full value of salary and equity packages

Pave’s visual offer letter feature addresses a common pain point: candidates and employees often misunderstand stock option value. By presenting equity in clear, visual formats alongside salary data, companies can communicate total rewards more effectively.

This capability is increasingly important in 2024–2026 as many tech companies navigate underwater options, valuation resets, and repricing conversations. Clear employee communication about equity value—including realistic projections versus guarantees—reduces confusion and retention risk.

SalaryCube encourages a “salary-first plus transparent equity” approach, where salary ranges and equity guidelines are managed together but may draw on different data sources. Salary data from real-time benchmarks provides the guaranteed component; equity benchmarks from Pave or other sources inform the variable component.

Key takeaways from this section:

Pave data supports full-cycle compensation workflows from benchmarking through offer communication
Equity depth is a differentiator but requires Pro subscription
Fast, on-demand market data needs may require supplementary tools
Total rewards communication is critical for talent attraction and retention

Evaluating the Quality and Fit of Pave Compensation Data

Not all compensation datasets serve every organization equally. Before relying on Pave—or any data source—as your primary benchmark, HR teams must assess coverage, methodology, and practical fit against their specific needs.

Coverage: Geography, Industry, and Role Mix

Start by investigating what Pave actually covers versus what your organization needs. Key questions include:

What proportion of Pave data is U.S. and Canada versus Europe versus other regions? (Answer: primarily U.S./Canada with limited international coverage)
Which industries dominate the dataset? (Answer: SaaS, fintech, developer tools, and other VC-backed tech verticals)
How deeply are non-tech functions represented? (Answer: limited coverage for operations, manufacturing, healthcare, and field roles)

Compare these answers against your workforce profile and hiring roadmap for 2025–2027. If you’re hiring primarily for tech roles at a venture-backed company in San Francisco or New York, Pave coverage likely fits well. If you’re adding operations centers in the Midwest or expanding into healthcare, you’ll encounter data gaps.

SalaryCube focuses on U.S. data with broader role coverage, including hybrid and blended jobs that don’t fit neatly into Pave’s tech-centric architecture. This can complement or replace Pave data when coverage gaps appear.

Recency, Stability, and Methodology

Real-time data sounds appealing, but “real-time” needs guardrails. Benchmarks that swing wildly week-to-week create volatility that undermines pay programs; benchmarks that lag months behind market reality erode relevance.

Questions to ask any data provider, including Pave:

How often are benchmarks refreshed from HRIS feeds? (Daily, weekly, monthly?)
How are outliers, data errors, and nonstandard titles cleaned or excluded?
What minimum data thresholds exist for publishing a benchmark? (Pave requires at least three companies)
How do calculated or modeled benchmarks differ from raw aggregated data?

Pave uses machine learning to generate “Calculated Benchmarks” in data-sparse areas, providing coverage where traditional surveys would show no results. While this extends usability, modeled outputs may carry higher margins of error than benchmarks based on large sample sizes. Pave’s data consistency labels help users understand statistical quality, but teams should treat low-consistency benchmarks with appropriate caution.

SalaryCube publishes its methodology transparently and maintains defensible, audit-ready processes designed for Board and audit committee scrutiny. When pay decisions face internal challenges, methodology documentation becomes essential.

Data Access, Workflow Integration, and Reporting

Practical usability matters as much as data quality. Consider:

Speed to answer: How quickly can HR get from “What’s market for a Senior Data Engineer in Denver?” to a usable number? Minutes or days?
Reporting flexibility: What export options exist (CSV, Excel, PDF)? Are there limits or extra fees for reports?
Self-service capability: Can managers, Finance, and executives access dashboards without extensive training?

Pave’s compensation management platform offers integrated workflows, but the give-to-get model means access depends on completed integrations. Organizations still in the approval process may face delays before they can view data.

Modern product-led tools like SalaryCube emphasize quick searches, unlimited reporting, and intuitive interfaces that reduce consulting dependencies. The workflow difference can be significant: getting a benchmark in two minutes versus scheduling a call with a vendor representative.

Understanding these usability dimensions helps HR teams anticipate the operational reality of relying on any data source—not just the theoretical data quality.

Common Challenges With Pave Compensation Data and How to Mitigate Them

Even strong datasets have limitations. Compensation leaders should anticipate potential issues with Pave data and build safeguards—often by combining multiple sources including real-time tools that don’t require data sharing.

Over-Reliance on Tech-Heavy Benchmarks

Problem: Applying tech-startup-driven benchmarks to non-tech business units can distort compensation strategy. A benchmark derived from San Francisco SaaS companies may not reflect market rates for an operations role in Indianapolis.

Solution: Segment your population and use Pave primarily for appropriate tech cohorts. Supplement with U.S.-wide, industry-flexible data from platforms like SalaryCube for roles outside Pave’s core coverage. Where needed, specialized surveys can fill remaining gaps.

Problem: Legal, security, and IT concerns can slow or block HRIS and cap table integrations. Some organizations have policies that prohibit sharing employee compensation data with external platforms, regardless of anonymization safeguards.

Solution: Prepare a cross-functional approval path before initiating the integration process. Request clear security documentation (SOC 2 reports, data handling policies) from Pave. Maintain a backup source of real-time salary data—like Bigfoot Live—that doesn’t require give-to-get participation. This ensures you have market data access even if integrations stall.

Misalignment Between Job Mapping and Internal Structures

Problem: Pave’s standardized job and level architecture may not match your bespoke titles. Poor mapping—like incorrectly classifying a “Lead Engineer” as a “Manager”—produces benchmarks that don’t reflect your actual roles.

Solution: Invest time in one-time job architecture alignment before relying on benchmarks. Use tools like SalaryCube’s Job Description Studio to clarify role scopes and FLSA status. Always sanity-check mapped benchmarks against internal incumbents and performance levels. If a benchmark seems off, investigate the mapping before adjusting pay.

Equity Volatility and Communication Risk

Problem: Equity benchmarks can shift quickly with market volatility. Employees may misinterpret modeled equity values as guarantees, creating disappointment when actual outcomes differ.

Solution: Use Pave equity data as directional guidance, not promises. Pair equity insights with clear communication frameworks that distinguish between guaranteed compensation (salary) and variable projections (equity). Rely on salary-focused benchmarks from SalaryCube to anchor the guaranteed portion of total compensation, keeping projections and guarantees distinct in employee communication.

These challenges aren’t reasons to avoid Pave—they’re factors that require mitigation strategies. Most organizations benefit from combining multiple data sources rather than depending on any single provider.

Conclusion and Next Steps

Pave compensation data is a powerful, tech-centric benchmark source that delivers real-time insights for VC-backed companies with strong HRIS integrations. When its coverage aligns with your workforce—primarily tech roles in U.S. and Canadian markets—it provides defensible data for compensation decisions, equity benchmarking, and total rewards communication.

However, HR teams should evaluate Pave against their specific needs for U.S. coverage breadth, hybrid role pricing, and operational simplicity. Organizations with diverse workforces, data-sharing restrictions, or immediate benchmarking needs may find real-time salary intelligence platforms like SalaryCube more efficient for day-to-day compensation workflows.

Actionable next steps:

Audit your current compensation data stack (surveys, Pave, ad-hoc sources) and identify gaps by role family and geography
Request methodology and coverage documentation from Pave and any other provider you’re considering
Pilot a modern real-time salary intelligence tool like SalaryCube alongside Pave during an upcoming hiring push or merit cycle—compare speed, coverage, and decision confidence
Standardize an internal playbook for when to use which data source (Pave for tech equity, SalaryCube for hybrid roles, surveys for specialized industries)
Establish data quality thresholds defining minimum sample sizes and consistency requirements for benchmark-driven pay decisions

Related topics worth exploring include pay range design methodologies, compa-ratio analysis for merit cycles, FLSA classification workflows, and pay equity audit processes.

If you want real-time, defensible U.S. salary data that HR and compensation teams can actually use alongside or instead of Pave, book a demo with SalaryCube or watch our interactive product tours.

Additional Resources

For teams evaluating Pave alternatives or looking to supplement their data sources, these resources provide practical starting points:

SalaryCube Salary Benchmarking – Real-time U.S. compensation data for salary benchmarking without give-to-get requirements
Bigfoot Live – Deep market insights with daily-updated salary data
Free SalaryCube Tools – Compa-ratio calculator, salary-to-hourly converter, and wage raise calculator for quick analysis
Methodology and Security – Documentation explaining how real-time U.S. compensation data is collected, cleaned, and protected

Evaluation questions for your next RFP:

What geographies and industries does the data cover, and what are the sample sizes for our key roles?
How often is the data refreshed, and what lag exists between collection and publication?
What methodology governs outlier detection, job matching, and benchmark calculation?
What are the integration requirements, and can we access data before integrations complete?
What reporting limits apply, and are there additional fees for exports or API access?
How does the provider handle data privacy, and what security certifications are in place?