P

Pave

Builds software for managing employee pay

Overview

Pave builds compensation software for human resources and finance teams to set pay, run salary reviews, and explain total rewards to employees. Its Market Pricing workspace turns job structures and market data into salary ranges and equity targets, while Compensation Planning manages budgets, approvals, and pay recommendations. Employee portals and interactive offer letters show salary, benefits, and equity together, including scenarios for the future value of stock awards. The company sells access to its compensation platform through paid packages and optional product modules for organizations from startups to global enterprises. Pave was founded in 2019 and is headquartered in San Francisco.

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About Pave

Simplify's Rating
Why Pave is rated
B-
Rated B on Competitive Edge
Rated B on Growth Potential
Rated C on Differentiation

Industries

Data & AnalyticsEnterprise Software

Company Size

51-200

Company Stage

Series C

Total Funding

$486.1M

Headquarters

San Francisco, CA

Founded

2019

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Simplify's take

What believers are saying

  • Pave reaches large compensation budgets: customers manage more than $190 billion in pay through its workflows.
  • Engineers get room to experiment: Pave's annual R&D Hack Week has produced shipped features and internal tools.
  • Enterprise adoption spans departments: Databricks uses Pave for company-wide compensation planning across its multinational organization.

What critics are saying

  • Paying traction is harder to judge: Pave's Market Data Lite gives salary and equity benchmarks away.
  • Nearby staff face limited location freedom: Pave asks employees near its offices to work onsite four days weekly.
  • International coverage has holes: Pave excludes some pay types in certain locations when data falls below sufficiency standards.

What makes Pave unique

  • Survey-free benchmarks stay current: Pave says over 9,000 integrated companies feed its compensation dataset.
  • Peer groups sharpen comparisons: Pave lets customers filter benchmark companies by industry, headcount, investment, and geography.
  • AI recommendations show their reasoning: Pave Agent names analyzed datasets and scores their underlying statistical consistency.

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Funding

Total Funding

$486.1M

Above

Industry Average

Funded Over

7 Rounds

Series C funding is usually for startups that are doing well and are looking for more money to fuel major growth, such as acquiring other companies, expanding into global markets, or launching new product lines. Investors typically include larger venture capital firms and private equity.
Series C Funding Comparison
Above Average

Industry standards

$50M
$50M
$62M
$100M
$100M

Benefits

  • Competitive salary
  • Equity
  • Medical, dental & vision coverage
  • Commuter benefits
  • Catered lunch
  • Unlimited PTO policy

Growth & Insights and Company News

6 month growth

0%

1 year growth

0%

2 year growth

1%
The Richmond Times-Dispatch
Nov 4th, 2026
How big of a raise can you expect in 2027?

How big of a raise can you expect in 2027? * Josh Steinfeld for Pave * Nov 4, 2026 Updated 1 hr ago * 0 Josh Steinfeld for Pave As companies are going through their annual budgeting exercises for 2027, one of the most impactful decisions they will make is how much additional budget to allocate to employee salaries. This number dictates how much most employees will see their pay increase for 2027. While this increase in budget tends to be relatively stable from year to year, that doesn't mean all employees receive the same increase every year. To get a better understanding of how companies are approaching these pay increases, as well as a number of other decisions heading into 2027, Pave partnered with Alpine Rewards to survey major employers across industries. People are also reading... Merit raises are steady, and so are promotion budgets. The median merit budget among U.S. companies is 3.5% of payroll for 2027, which is almost identical to what companies actually spent in 2026 and to last year's version of this survey, which also found a 3.5% median planned budget for 2026. Raises have settled into a pattern. The budget set aside for promotions and market adjustments also looks similar, with a median of about 1%. Outside the U.S., the story is much the same: A 3.5% raise may not feel exciting to some employees, but there are other levers available to companies to reward employees. Top performers are rewarded with equity. Here is the catch with an overall budget of 3.5%: It is not what everyone gets. Most companies will use performance to differentiate increases for high performance. In fact, the report found that 87% of companies use individual performance ratings to set merit increases, and only 9% hand out the same increase across the board. Beyond salary, companies are leaning on stock to reward high performers. The research found that 61% of companies use equity to deliver additional compensation to their top-performing employees. The key takeaway here is that while salary increases sit within tight boundaries, equity is where companies have room to pay a standout employee more. The fastest route to a big raise is a promotion. If you want a double-digit pay increase, the data says to get promoted. The most common average promotional increase is 9% to 12%, roughly three times a typical merit raise. Promotions are also not rare. Thirty-two percent of companies promoted between 10% and 15% of their workforce over the past 12 months, and another 29% promoted more than 15%. Overall, 61% promoted more than 1 in 10 employees. Timing is flexible too: 57% of companies promote employees off-cycle rather than making them wait for the annual review. A promotion can bring equity along with it. Among companies that offer it, 51% typically make employees eligible for equity in an off-cycle promotion. Two other interesting findings from the survey revolve around headcount growth and approaches to pay transparency. 2. Pay transparency is also a manager skill. Companies are still figuring out how transparent they want to be around pay. In recent years, providing salary ranges in job postings has become common, with many candidates seeing this as a minimum expectation for applying for a job. Based on the survey, 80% of companies include a salary range in every U.S. job posting, even where the law does not require it. Beyond putting salary ranges in job postings, approaches to pay transparency are inconsistent. But what many companies are doing is training the people who have the conversations. About half of companies (51%) plan to increase manager training or guidance on pay in 2027, the most common planned approach to transparency identified for 2027. Many of these companies are building on an already established foundation: 42% already give formal pay training to all people managers and 54% provide written talking points. The reason is simple. A pay decision is only as good as the explanation that comes with it. When managers can clearly describe how a raise, a range or a promotion was determined, employees understand what they are paid and why. If you are not sure how your own pay is set, ask your manager where you sit in your range - and what it would take to move up. Methodology Survey data: Pave and Alpine Rewards partnered to run a survey on "2027 Trends and Budgeting," in which 251 companies participated, mainly from the technology and life sciences industries. Full survey results of the 2027 Compensation Budgets & Planning Report with breakouts for company size, industry, and more can be found in Pave Data Lab. Turnover data: Pave's real-time compensation dataset consists of 1M+ employees from 9,000+ companies. Turnover data is calculated as the rate of employees from 12 months ago that are no longer at the company. This story was produced by Pave and reviewed and distributed by Stacker.

PR Newswire
Oct 28th, 2026
Pave and Levels.fyi announce strategic alliance, bringing Levels.fyi data into Pave Agent and Market Pricing.

Pave and Levels.fyi announce strategic alliance, bringing Levels.fyi data into Pave Agent and Market Pricing. Oct 28, 2026, 17:27 ET Compensation decisions draw on more data than ever, and that data is scattered across surveys, spreadsheets, and tools. Pave has built the place where it all comes together: one platform, powered by real-time intelligence and open to every source compensation teams trust. Today's strategic alliance with Levels.fyi is the latest step in that effort. SAN FRANCISCO, Oct. 28, 2026 /PRNewswire/ - Pave, the Agentic Compensation Management platform, today announced a strategic alliance with Levels.fyi, a compensation data platform that shows what work pays by company, level, location, and skill. The alliance builds on Pave's partnerships with The Croner Company, Korn Ferry, WTW, and others and reflects a continuing commitment: making it easier for customers to bring the critical data behind their pay decisions into one place. Pave will continue to expand the network of data partners available on the platform, so that compensation teams don't have to stitch together sources by hand or choose between the ones they trust. Through this strategic alliance, Pave customers can now access Levels.fyi data inside Pave Agent as well as Pave's Market Pricing tool to compare pay for a role across sources, benchmark against real-time Market Data, and price jobs and build competitive pay ranges. Customers can analyze Levels.fyi data alongside their own compensation data and any other data sources they choose to use. Pave Agent can use Levels.fyi data as a reference input to deliver compensation intelligence, so teams can ask a question in plain language and see the answer across Levels.fyi, their other sources, and real-time intelligence from 9,000-plus companies in Pave's dataset. Levels.fyi adds a distinct lens on the market. Its data is contributed directly by the people earning it and backed by proof documents such as offer letters, W-2s, and pay stubs. Alongside survey and real-time views, it shows compensation teams what talent is actually earning. With Levels.fyi data in Pave, mutual clients can: * Ask Pave Agent to compare pay for a role across Levels.fyi, WTW, Korn Ferry, Croner, and Pave's real-time Market Data, all within one platform * See self-reported Levels.fyi data next to survey and real-time benchmarks to understand where the market views agree and where they diverge * Analyze Levels.fyi data alongside their own compensation data to see where their pay stands * Give every analyst on the comp team, not just the person who manages the survey license, direct access to Levels.fyi data in their daily workflow "Compensation decisions are only as good as the data behind them, and no single source tells the whole story," said Matt Schulman, CEO of Pave. "Our goal is for Pave to be the central source for the data compensation teams use to make decisions, with real-time intelligence from 9,000-plus companies as the foundation and every source a client trusts available alongside it. Croner, Korn Ferry, WTW, and now Levels.fyi are steps in that effort, and we will keep adding more. Clients shouldn't have to choose between the data sources they trust. They should have them all in one place." "Effective compensation benchmarking starts with a simple question, 'How do we compare with the companies we actually compete against?'" said Zuhayeer Musa, co-founder of Levels.fyi. "Our data gives customers a current, company-level view of the market, backed by real proof such as offer letters and pay stubs. Bringing that perspective into Pave means teams can build the peer groups that matter to them, focus on a single competitor, and combine Levels.fyi's rich intelligence with the rest of their stack. Pave has set the standard for connected compensation, and we're excited to keep helping our mutual customers make faster, more confident pay decisions." Levels.fyi data is now available to mutual customers through Pave Agent and Pave's Market Pricing tool. Existing customers can contact their account teams for access and availability details. About Pave Pave is the Agentic Compensation Management platform, powered by real-time compensation intelligence from 9,000-plus companies and a growing network of data partners. Beyond Market Pricing, Pave's platform spans Market Data benchmarking, Compensation Planning, Total Rewards, Team View, Visual Offer Letters, and Pave Agent, giving compensation teams one connected system to price roles, plan cycles, and communicate pay across the business. To learn more, visit www.pave.com. About Levels.fyi Levels.fyi is a compensation data platform that shows what work pays by company, level, location, and skill, and offers a different perspective of the market. Its data is contributed directly by the people earning it, backed by proof documents like offer letters, W-2s, and pay stubs. Levels.fyi reports 1M+ compensation data points, 2,100+ companies with fresh data, and 8,500+ company job ladders mapped, and says more than 3M professionals use the platform every month. To learn more, visit www.levels.fyi. SOURCE Pave

PR Newswire
Aug 17th, 2026
Pave adds forward deployed engineering and manufacturing benchmarks across 29 new locations

Pave has released its August market data update, introducing compensation benchmarks for Forward Deployed Engineering — a hybrid engineer-consultant role pioneered by Palantir and adopted by AI labs including OpenAI. The update also extends coverage to manufacturing roles and adds 29 new locations. The release includes ten new job families and expands coverage to 29 locations, including international cities and US metros where talent is relocating toward manufacturing hubs. Manufacturing benchmarks address industry demand for 3.8 million workers by 2033. Pave's platform now integrates live job posting data with its Recent Hire Filter through the Pave Agent. This pairing gives compensation teams both forward-looking salary ranges from public postings and actual recent hire benchmarks. The company collects data through automated connections to HR systems, enabling monthly updates rather than annual cycles. All updates are available to Market Data Pro clients.

PR Newswire
Aug 13th, 2026
Pave's AI compensation analyst now reasons across merit cycles, pay ranges and job postings

Pave has expanded its AI compensation analyst, the Pave Agent, to analyse a company's entire compensation system. The platform can now work across merit cycle history, pay ranges, survey methodology, real-time job posting data, and uploaded documents. The Agent provides recommendations that compensation teams can trace and question. It references merit cycles from Pave Compensation Planning, works from Market Pricing data including survey uploads and blending logic, and analyses Pave's job posting dataset updated daily. Companies can also upload documents like pay philosophy and equity plans for analysis. The system processes up to 100MB of files, with uploads secured to individual customer tenants and never used to train models. "Compensation leaders need an AI that understands how their organisation actually pays people," said Matt Schulman, Pave's chief executive officer. The new capabilities are available to Pave customers today.

Pave
Jun 17th, 2026
See what the market is paying right now: Pave's Recent Hire Filter.

See what the market is paying right now: Pave's Recent Hire Filter. Pave's new Recent Hire Filter is giving compensation teams real-time hiring intelligence so you can validate recruiter escalations with data, not gut feel. June 17, 2026 Compensation benchmarks are supposed to reflect the overall market. But there are times when benchmarking the entire population might not be enough. In today's hiring market, some roles are seeing compensation move faster than others (check out Pave's Hot Job Index) and blending those in with the overall market might be creating a compensation blind spot. Let's say a recruiter escalates an offer above your band, citing a competing offer 15% over your P75. Your comp team has no way to verify whether that reflects a genuine market shift or a one-off negotiation tactic. So you're left with two options: approve the exception and risk disrupting the entire band, or deny it and risk losing the candidate. This is the problem Pave, Inc. set out to solve with the Recent Hire Filter, now available in Market Data Pro. The gap between your ranges and reality. Compensation leaders already know the theoretical issue with benchmark lag. Traditional surveys collect data annually, process it over months, and publish results that reflect a point in time that has already passed by the time you read them. Pave's real-time dataset of 9,000+ companies already solves that problem at the macro level - but even real-time data includes employees hired years ago whose compensation may reflect a different labor market. Pave, Inc. is seeing this matter more in certain job families - AI engineering, cybersecurity, specialized product roles - where the market can shift meaningfully in a matter of months. When that happens, the full-population benchmark smooths out the very signal you need to see. This is the gap that creates friction across the hiring process. Recruiters escalate exceptions. Hiring managers question your ranges. Candidates walk away. And the comp team is stuck defending the numbers without a clear way to show whether they still hold. How the Recent Hire Filter works. The Recent Hire Filter gives you a diagnostic lens that isolates what's happening right now in the market for any role. Here's how it works: Filter Recent Hires. The feature automatically isolates employees hired within the last six months from Pave's real-time dataset. This gives you a clean view of what companies are actually paying to bring people in the door today. Compare Against the Full Population. You can view recent hire compensation side-by-side with the complete benchmark at every percentile - i.e., P25, P50, P75 - to see where pay is shifting and by how much. This comparison is the key, as it helps you analyze the single data point you are hearing from a candidate against the actual market conditions. Assess Statistical Significance. This is where it gets interesting. Not every movement in compensation data means the market has shifted. Small samples, seasonal hiring patterns, and outlier offers can all create misleading signals. The Recent Hire Filter flags whether the difference between recent hires and the broader population is statistically significant - a real signal - or just noise. Why Statistical Significance matters. Let's pause on that third point because it's what separates useful market intelligence from misleading data. There are tools in the market that will show you offer data. That's helpful at a surface level. But raw data without statistical rigor can actually make your decisions worse. If five companies in your market made aggressive offers for a single role last quarter, that can look like a market shift when it's really a small-sample anomaly. Without a mechanism to distinguish real movement from noise, you're just replacing one form of guessing with another. Pave's statistical significance scoring solves this. When you pull up the Recent Hire Filter for a given role and market, you're seeing whether that number is backed by enough data, across enough companies, to represent a meaningful trend. That distinction gives you confidence to either hold your ranges with evidence or escalate with a defensible case. Either way, you're operating from a position of data, not intuition. When to use the Recent Hire Filter (and when not to). The Recent Hire Filter is designed to complement your existing census benchmark, not replace it. Think of it this way: Your full census benchmark remains the right tool for setting and maintaining compensation ranges, annual planning, pay equity analysis, board reporting, and governance. It provides maximum sample size, smooths out short-term fluctuations, and is built for defensibility. The Recent Hire Filter is for a different set of questions: Has the market moved for this role? Is the recruiter's escalation backed by real data? Are its ranges still competitive for the talent Pave, Inc. is trying to hire right now? It surfaces shifts before they appear in full-population data, giving you an early warning system for market movements. The Recent Hire Filter is a diagnostic tool that shows you where the market is moving so you can make informed decisions in real time. It's built to validate escalation requests, inform recruiter conversations, and build the case for your next range review. It does not export to Market Pricing or automatically reset your bands, because that's not the right workflow for directional intelligence. It informs decisions; it doesn't make them for you. Used together, the full census benchmark and Recent Hire filter give you both the stability you need for governance and the agility you need for real-time talent decisions. Turning an escalation into a data-backed decision. To make this concrete, imagine this scenario: A recruiter is pushing to offer a Senior Software Engineer above your band, insisting the candidate has competing offers well above your P75. Without the Recent Hire Filter, you're choosing between blowing up your band structure or potentially losing the candidate. With the Recent Hire Filter, you pull the data for Senior Software Engineers in that market. You see exactly how recent hires across 9,000+ companies are trending relative to the full population. The statistical significance indicator tells you whether this is a real shift or an outlier. If the data supports a move, you arm the recruiter with evidence to meet the candidate at market - without setting a precedent that undermines your entire range. If it doesn't, you have the data to hold your line confidently. Comp team credibility stays intact. The candidate receives a competitive, data-driven offer. And your band structure survives to fight another day. The bottom line. The market doesn't wait for your annual survey cycle, and the candidates you're competing for certainly aren't checking whether your ranges were refreshed this quarter. Every week that your team operates with stale intelligence is a week of unnecessary risk - overpaying where you didn't need to, losing candidates where you shouldn't have, and defending ranges with nothing more than conviction. The Recent Hire Filter gives you direct insight into what companies are paying right now, validated by statistical significance, layered on top of the deepest real-time compensation dataset on the market. It's the difference between reacting to the market and seeing it move in real time. Ready to see the Recent Hire Filter in action? Book a demo of Market Data Pro today.

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