Why Startups Get Salary Benchmarking Wrong
Most startup founders wing it. They offer the salary they can afford, not the salary that attracts great talent and sets employees up for success. Others copy what competitors are offering, creating a race to the bottom. Neither approach builds a sustainable hiring practice. Research from SHRM on compensation practices shows that pay transparency decreases intent to quit by 30 percent. For startups, those percentages directly impact your ability to scale.
The real problem: startups operate in markets with fragmented salary data. APAC is especially sparse : how do you know what a senior engineer in Singapore or Mumbai is worth if you're a 20-person Series A startup?
Glassdoor, Payscale, and Levels.fyi provide some data, but they're skewed toward large tech companies and don't capture startup compensation (which includes equity, lower base, and growth potential).
This guide provides a repeatable, low-cost process to establish fair salary ranges using publicly available data and strategic primary research.
Step 1: Define Your Job Architecture and Levelling
Before you can benchmark salaries, you need consistent job levels. Startups often skip this and create title inflation ("Senior Engineer" means different things to different people). Establish clear levels with descriptions of scope, impact, and responsibility.
Example framework for Engineering:
Document each level's scope, decision-making authority, and typical responsibilities. This becomes your levelling guide, use it consistently to categorize hires and ensure equity in compensation progression.
Step 2: Gather External Benchmarks From Public Data
Start with these free and low-cost sources:
- Glassdoor (free, location-filtered): Search your city and role. Filter by company size (startups), rating, and review date (recent only). Note: Glassdoor data skews toward larger companies; adjust down 10-15% for startups.
- Payscale, Salary.com, Levels.fyi: Similar to Glassdoor. Cross-reference multiple sources to identify ranges.
- Government/National Statistical Agencies: Singapore's MOM, Hong Kong's Census and Statistics Department, India's NITI Aayog publish wage indices by role and sector. These provide ground truth but are usually 6-12 months behind.
- Startup-specific surveys: 2025 Total Compensation & Benefits Benchmark Report , Carta's compensation dataset and industry-specific reports (e.g., Wellfound’s startup salary reports for tech roles).
- LinkedIn Salary: Filter by location and role, reviewing salaries posted by companies. Not always complete but offers trending data.
Aggregation method: For each role and location, gather 8-15 data points. Remove the top 25% and bottom 25% (outliers). Calculate the median and interquartile range. This becomes your external benchmark baseline.
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Step 3: Adjust for Company Stage and Cash Situation
External benchmarks show what all companies are paying, but startups operate differently. You need to adjust for stage, geography, and total compensation (base + equity).
Stage adjustments (% of large-company baseline):
Why? Startups offer lower cash but liquidity upside. A $100K role at a large company might be $85K + 0.3% equity at a Series A. The equity is worth nothing until exit, but it attracts growth-minded talent and aligns incentives. Be explicit about this trade-off with candidates.
Step 4: Conduct Primary Research With Peer Founders
Secondary data is incomplete. Talk directly to 3-5 founders in your sector and stage.
Ask:
- What are you paying for [role] in [location]?
- How much equity do you offer?
- What's your turnover in that role?
- What mistakes did you make with compensation?
This gives you context that spreadsheets don't. You'll learn whether certain roles in your market are tighter than expected, whether equity expectations differ from what you planned, and what compensation problems other startups face.
Create a peer compensation tracker (simple spreadsheet): company stage, role, base salary, equity %, location, hiring success (did they fill the role quickly? at what salary?).
Over time, you build pattern recognition. If three high-growth startups all increased engineering salaries in Q1 2025, that's a signal.
Step 5: Build Your Salary Bands and Document Assumptions
Synthesize all data into salary bands by role and location.
Format: Role | Level | Min | Midpoint | Max | Equity % | Notes
Document your assumptions in a methodology note: "Benchmarks sourced from Glassdoor, Payscale, Singapore MOM, and peer founder interviews.
Adjusted for Series A stage (85% of large-company baseline). External benchmarks current as of [date]. Equity percentages assume 12M fully diluted shares post-Series A."
Step 6: Test Your Bands With Early Hires and Iterate
Your first salary bands are hypotheses, not facts. Publish them internally (to your team) and use them for your next 3-5 hires.
Track outcomes:
- Are you attracting strong candidates, or are they rejecting offers?
- Are you over-paying or under-paying relative to candidate expectations?
- Are existing employees accepting the bands, or is there friction?
- Are you losing candidates in final negotiations to competitors?
After 5 hires, review and adjust. If you're losing every senior hire to a competitor paying 15% more, adjust your max band. If you're filling roles quickly with exceptional talent at the midpoint, you may have room to reduce the max. This iterative approach is much smarter than guessing.
Step 7: Build in Annual Refresh Cycles
Salary markets move. Update your benchmarks annually (Q1 is standard). Pull fresh Glassdoor/Payscale, revisit peer founder surveys, check government wage indices. Adjust bands by 3-5% for inflation and market movement.
This is especially important in APAC, where talent markets are shifting fast due to remote work normalization and increased venture activity in India and Southeast Asia.
Communicate changes transparently to your team. If you raised new funding and can now pay closer to market rate, tell them. If markets softened and you're holding bands flat, explain why. Transparency prevents resentment.
Common Pitfalls to Avoid
- Not adjusting for stage: Paying $150K base to a Series A hire when $100K base + 0.5% equity is market for that stage will burn cash and set unsustainable expectations.
- Inflating roles to justify high salaries: Calling someone a "Senior" to justify a $130K offer, when they're actually mid-level, creates leveling chaos later.
- Trusting only one source: Glassdoor data varies wildly by location and company size. Use at least 3-4 sources.
- Forgetting total comp: A $100K base looks expensive until you factor in that large companies add 20-30% in benefits, bonuses, and stock. Your $100K base + 0.3% equity might be competitive total comp.
- Not documenting: If you can't explain how you arrived at a salary, it looks like favoritism. Documentation protects you and your team.
Key Checkpoints for Implementation
Why This Matters for Your Startup
Compensation is often the largest operational expense for startups. A transparent, data-driven process ensures you're building sustainable salary structures, not overpaying or creating internal resentment through inconsistency. It also signals to candidates and investors that you run a professional operation. Investors increasingly scrutinize salary equity and transparency; founders who can articulate their compensation philosophy stand out.
This process takes 3-4 weeks to implement initially, then 1-2 hours per quarter to maintain. The ROI is substantial: better hires, lower turnover, fewer compensation disputes, and a foundation for scaling. Start this week.
