Employee Benchmarking: How Many People Do I Need in the Product Function?

When building or scaling a product function, leadership often wonder how many people they should hire. This is for good reason, as getting it right is critical for product success.

Benchmarking your product function? Search benchmarks

Under-staff the product function and you may see slower time-to-market, overloaded Product Managers (PMs), unclear ownership, and backlog chaos. Over-staff it and you risk inefficiencies, duplicated roles, and inflated costs.

Employee benchmarking is the practice of using internal data alongside external data to determine “good” staffing levels. Finding the right headcount for the product function can be particularly tricky, as the scope of “product” changes by company size, industry, and maturity.

Table of Contents

In this guide we will take you through how to think about benchmarking your product team, from defining the roles, understanding key ratios, looking at how these evolve with company stage and industry, and applying real data. Here’s the full table of contents:

• Product Function Overview
• How Companies Determine Ideal Benchmarking Ratios
• 3 Key Ratios and Metrics for Product Function Benchmarking
• Product Benchmarking From Startup to Enterprise
• How Product Headcount Compares Across Industries
• Factors That Influence Optimal Product Team Size
• Using Benchmark Data to Make Headcount Decisions
• Common Pitfalls in Product Function Benchmarking
• Conclusion

Product Function Overview

Before you can benchmark the number of employees, we must be clear on what it is that you are benchmarking. The “product function” tends to cover a range of roles, including but not limited to:

• Product Managers (PMs) - responsible for product strategy, roadmap, prioritization, and working with the engineering team.
• Product Owners (POs) or Technical PMs – sometimes taking a more delivery-oriented role (epics, user stories) especially in agile engineering organizations.
• Product Operations – typically responsible for analytics, tooling, process, and ensuring PMs are enabled.
• Product Marketing – there to bridge the gap between the product and audience, working with PMs to ensure that what’s built aligns with market needs.

The specific mix depends heavily on the company size and industry. For example, in a startup (<50 people) “product” may be the founder or a single generalist PM. Whereas in a large enterprise (>5K employees) you might have multiple spans and layers in the product function.

How Companies Determine Ideal Benchmarking Ratios

Benchmarking isn’t about copying one number from a competitor - It's about using data to inform decisions within the context of your organization. The approach is typically as follows:

1. Collect internal data

How many product roles do you currently have, what do they own, with how many engineers / designers / support? What outcomes (time-to-market, backlog size, feature velocity) are you seeing?

2. Gather external benchmark data

Collate relevant information from peer companies, industry surveys, databases of headcount by function, ratio of PMs to engineers, and product team size as % of the overall workforce. CompanySights is a leading provider of external benchmarks for the product function in many industries – Access database.

3. Contextualize

Benchmarks are a starting point, not a hard and fast rule. It’s important to filter benchmarking data for things like industry, geography, product complexity (platform vs feature), company stage (startup vs enterprise), and employee distribution.

4. Make decisions

With reference to your benchmarking data, set target headcounts or ratios, align them with revenue / strategic goals, monitor, and revisit periodically (usually every year).

3 Key Ratios and Metrics for Product Function Benchmarking

Here are some of the more frequently cited benchmarks for product teams:

1. Product as % of Employees

To get started a useful metric is what share of headcount works in product. Based on our experience, we often see product headcount as a share of the total employee population between 1 – 8%. Note that this percentage varies based on industry, geography, organization size, and maturity stage.

2. Product to Engineer Ratio

This is a useful ratio given the high levels of collaboration required between product and engineering. In a sample of 50 tech scale-ups by Synq, the product to engineering ratio was reported at 1 product person per 8 engineers. Similar to the caveat above, this ratio can vary based on variables like industry, company size, and product complexity.

3. PM to Engineer Ratio

Drilling into the product function further, a widely used benchmark is how many engineers are supported by one PM. For many tech organizations, the ballpark ratio is between 1 PM : 5 - 10 engineers. Per above, this can often vary based on product complexity, business size, and maturity stage.

Looking for product function benchmarks? Search here

While the revenue per employee metric isn’t exclusive to the product function, it can be helpful for providing broader context around headcount efficiency. For example, our benchmarking data for the Software industry shows an average RPE of $290K. This doesn’t tell you “how many product people”, but it reminds us that headcount decisions tie back to business outcomes such as revenue, growth, and efficiency.

Product Benchmarking From Startup to Enterprise

How you staff the product function will vary significantly based on the maturity stage of your company. Here’s a rough breakdown that you can use:

Early-stage (1-50 employees, maybe <US$10M ARR)

• Often a single or very few PMs, or the founding team is managing product.
• Engineering teams may be small and generalist in nature.
• Ratio might be tighter (e.g., 1 PM : 3 - 7 engineers).
• Product operations are minimal or nonexistent.

Growth stage (50-500 employees, say US$10M-US$100M ARR)

• You’ll start to see multiple PMs, maybe some specialization (growth PMs, platform PMs).
• Engineering headcount is increasing, so you’ll need more PMs to maintain oversight.
• Ratios like 1 : 6 - 9 engineers per PM can apply.
• You’ll likely bring in product operations or analytics support.

Scale / Enterprise stage (>500 employees, ARR hundreds of millions to billions)

• Product function is layered with roles like; Associate PMs, PMs, Senior PMs, Group PMs, Directors, and VPs.
• Engineers may be grouped into product teams, platform teams, or infrastructure teams.
• The ratio of product to engineers may widen. For example, one PM might oversee two or more teams, or product may shift into portfolio / feature-owner model rather than one-to-one team.
• The key is delegation, tooling, and product operations to enable fewer PMs to manage more.

Please note that these numbers are illustrative. You will need to adjust them for things like your product complexity, platform vs feature orientation, and business model.

How Product Headcount Compares Across Industries

Industry context matters a lot. Here are some key factors that differ by industry:

• Consumer / PLG (product-led growth) - High product visibility, rapid feature cycles, and large engineering teams may require more PMs per engineer due to user feedback loops and experimentation.
• Enterprise / B2B - Often more complex integrations, slower release cycles, and more stakeholder coordination that may demand more PM time per engineer.
• Regulated industries (e.g. healthcare and fintech) - Higher complexity per feature and more compliance requirements that tend to push the PM : engineer ratio downward as more coordination is required.

In short, when benchmarking it’s very important to confirm that the data is specific to your industry.

Factors That Influence Optimal Product Team Size

The number one rule when it comes to benchmarking is that the benchmarks must be relevant to your organization. If not, then you’re unlikely to convince key stakeholders that they are meaningful. Here are some key factors to consider:

1. Industry – This is the most important factor. Make sure that you find and apply benchmarks specific to your industry. For example, a HealthTech company will have greater regulatory requirements when it comes to the product relative to a general software company, driving a higher number of product employees.
2. Company size – The size of the organization is also important, as a larger company will likely have a more efficient product function due to economies of scale. We typically view size based on annual revenue or the total number of employees.
3. Product complexity and lifecycle – A simple SaaS feature set is different from a large platform, micro-services ecosystem, or multi-tenant architecture. More complexity often means you need more PM time (thus fewer engineers per PM).
4. Geography and distribution – Remote teams, time zones, cross-location coordination may require more product roles (or product ops) to maintain alignment. Make sure to filter for this, if possible.
5. Maturity of tooling and process – If you have strong product operations, analytics, tooling, you can scale PMs more effectively. A PM supported by strong ops may manage more engineers.

Find benchmarks based on industry, geography, and size

Using Benchmark Data to Make Headcount Decisions

So how do you translate these benchmarks into decision-making? Here’s our four step process:

1. Calculate your current baseline

• How many product employees do you currently have?
• How many engineers do you have?
• What are your current product ratios?

Gather your internal employee data and crunch those numbers.

2. Source and compare to benchmarks

• Source relevant and reliable benchmarks for your product function – Search here
• Equipped with external benchmarks, compare the key product ratios to your baseline.

3. Set target ratios or headcount

Based on the benchmarks and your plan (engineers ramping up, new product lines, platform work), set a headcount target for your product team over the next 12 months.

4. Review your ratios

Review your product ratios each year to see how your team has performed and whether they are getting closer to benchmarks.

As the company grows and markets shift the “right” ratio may change. Therefore, be sure to access the latest and most relevant benchmarking data.

Common Pitfalls in Product Function Benchmarking

Benchmarking is helpful, but there are common mistakes that organizations make when applying them, such as:

• Blind copying of ratios - Using a benchmark without considering your context (industry, complexity, stage) will lead to sub-optimal staffing.
• Ignoring role definitions - Not all “PMs” are equal: generalists vs specialists, strategic vs delivery, etc. If you treat all PMs the same, you will mis-interpret ratios.
• Over-emphasis on input metrics, not outcomes - Having the “correct” ratio is meaningless if your backlog still grows unchecked or features don’t deliver value.
• Assuming ratios remain static - As companies scale, the ratio will need to change as well as the world of work changing around us due to the increased usage of AI.

Conclusion

Benchmarking your product function provides valuable guidance, but it isn’t one-size-fits-all. While common ranges like one Product Manager per 5 - 10 engineers offer useful reference points, the right staffing depends on your company’s size, stage, product complexity, and goals.

Combining these benchmarks with internal data and outcomes ensures informed, context-aware decisions, and revisiting the structure as your organization evolves keeps your product team balanced, efficient, and impactful.

Be a data-driven product leader – Leverage benchmarks