Latest Median Hardware Series D+ Valuation, Size & Dilution
Series D+ Benchmarks for Hardware Startups
Median deal size, valuation, and dilution for Hardware companies raising a Series D+—powered by Carta.
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Data as of: May 2026
Benchmark metrics
Annual median metrics for startups in Hardware in All of USA
Date range: Q1 2023 – Q1 2026
Median deal size
Median post-money valuation
Median dilution
Data table
| Start time | End time | Funding stage | Sector | Region | Median deal size | Median pre-money val | Median post-money val | Median dilution |
|---|---|---|---|---|---|---|---|---|
| Q1 2023 | Q1 2026 | Series D+ | Hardware | All of USA | $81.3M | $769.7M | $840.6M | 11.1% |
Series D+
Hardware • All of USA
Q1 2023 - Q1 2026
Deal size
$81.3M
Pre-money
$769.7M
Post-money
$840.6M
Dilution
11.1%
Methodology
About the Data
This dataset covers approximately 20,000 primary equity fundraising rounds completed by Carta cap table customers from January 2021 onwards. All data is aggregated and anonymized. Only companies headquartered in the United States are included. Bridge rounds and convertible instruments are excluded.
Regions are defined by U.S. Census Bureau Metropolitan Statistical Areas (MSAs). Bay Area combines two MSAs to cover the full SF + South Bay tech ecosystem; the others map to a single MSA each.
- Bay Area— San Francisco–Oakland–Berkeley MSA & San Jose–Sunnyvale–Santa Clara MSA
- NYC — New York–Newark–Jersey City MSA
- LA — Los Angeles–Long Beach–Anaheim MSA
- Boston — Boston–Cambridge–Newton MSA
- Austin — Austin–Round Rock–Georgetown MSA
- Non-Major Metros — All other U.S.-headquartered companies
How the Filters Work
The charts are designed to visualize temporal trends, while the table is optimized for cross-sectional analysis.
In the charts, this tool aggregates data from all selected sectors and regions into a single metric to show one line per stage over time. It also aggregates data from the selected quarters into their corresponding years to show annual trends.
In the data table, this tool aggregates data across the selected time period. It segments each combination of the selected stages, sectors, and regions into separate rows for comparison. It also includes top-level benchmarks (all-sector, all-region) for direct comparison against granular segments.