Private Equity Databases: PitchBook vs Preqin vs Cambridge Associates

What a Private Equity Database Actually Does (and Why It Matters at Scale)

Global private equity assets under management surpassed $8 trillion in 2023, according to Preqin's 2024 Global Private Equity Report. At that scale, the difference between a well-sourced allocation and a mediocre one comes down to data access. A private equity database is the infrastructure that makes informed manager selection, deal sourcing, and performance benchmarking possible.

The catch: institutional-grade database access costs $20,000 to $50,000 per year, with enterprise tiers reaching six figures. That price point is a feature, not a bug. It filters out retail noise and concentrates the best data in the hands of allocators who treat PE as a core portfolio pillar rather than a satellite bet.

If you are deploying $1M+ into private markets, the database subscription is not overhead. It is the prerequisite for the decision.

Types of Private Equity Databases and What Each One Is Actually For

Not all private equity databases serve the same workflow. The distinction matters more than most allocators realize, because buying the wrong tool for your use case is expensive in both dollars and opportunity cost.

Fund Performance and LP Benchmarking Databases

These are the workhorses for passive LP allocators and family offices evaluating fund commitments. Preqin and Burgiss (now integrated into MSCI's private assets platform) dominate this category. Burgiss aggregates audited cash flow data from over 10,000 funds representing more than $13 trillion in committed capital, making it one of the most LP-sourced and audited PE performance datasets available. Cambridge Associates occupies a similar niche, with benchmark data that institutional LPs treat as the industry standard for quartile comparisons.

If your primary question is "which fund manager should I back, and how does their track record compare to peers," this is your category.

Deal-Level and Direct Investment Databases

Family offices and entrepreneurs considering direct investments or co-investments alongside sponsors need transaction-level data: deal comps, sector roll-ups, management team track records, and acquisition multiples. PitchBook and FactSet's Mergerstat are the tools of choice here. PitchBook tracks over 3.5 million private companies, 100,000+ funds, and more than 700,000 deals globally, according to its 2024 Annual Global Private Market Fundraising Report. Preqin and Burgiss, by contrast, skew toward LP and fund-of-funds workflows and are less useful for direct deal analysis.

This distinction is underappreciated. If you are an operator or entrepreneur looking at co-investments, you need PitchBook's deal layer, not Preqin's fund performance layer.

Regulatory and Public Disclosure Databases

The SEC's Investment Adviser Public Disclosure database (IAPD) provides free access to Form ADV filings for registered investment advisers, including PE fund managers. It is not a substitute for paid databases, but it is a useful baseline layer for due diligence on fund managers, particularly for verifying AUM claims, regulatory history, and ownership structure. Pair it with a paid subscription, not instead of one.

Specialized and Sector-Focused Databases

Infrastructure, venture capital, and real assets each have dedicated data providers. PEI's Infrastructure Investor database and Venture Capital Journal serve allocators with concentrated sector exposure. These are generally supplementary rather than primary tools, but they fill genuine coverage gaps that generalist platforms leave open, particularly in emerging market infrastructure and early-stage venture.

How Private Equity Databases Track Fund Performance and IRR

Performance data is where the quality gap between databases becomes most consequential. The core metrics, Internal Rate of Return (IRR), Multiple on Invested Capital (MOIC), and Public Market Equivalent (PME), are only as useful as the underlying data they are calculated from.

Cambridge Associates' benchmark data shows that long-run median net IRRs for top-quartile buyout funds consistently exceed public market equivalents over 10-year horizons. More importantly, the performance gap between top-quartile and median PE funds is wider than in any other asset class, often 600 to 1,000+ basis points of net IRR difference. That dispersion is the entire argument for rigorous manager selection, and rigorous manager selection requires reliable benchmark data.

The challenge is that PE performance reporting is not standardized. Different GPs calculate IRR using different methodologies, report at different intervals, and define terms like "committed capital" or "vintage year" inconsistently. The Institutional Limited Partners Association (ILPA) has developed standardized reporting templates that hundreds of GPs and LPs have adopted, and understanding those standards is essential for interpreting the performance data surfaced by any major database.

Burgiss addresses this partly by sourcing data directly from LP cash flows rather than GP self-reporting, which reduces the selection bias inherent in voluntary disclosure. Cambridge Associates uses a similar LP-sourced methodology. Preqin relies more heavily on GP-reported data, which introduces more variability but also broader coverage.

When you are comparing fund performance across databases, check the sourcing methodology first. A 200 basis point IRR discrepancy between two platforms for the same fund is not unusual, and it is almost always explained by methodology, not error.

PitchBook vs. Preqin vs. Cambridge Associates: What the Differences Mean for Your Workflow

The three dominant platforms serve meaningfully different use cases, and the right answer depends on what you are actually trying to do.

A few practical notes on this table. Cambridge Associates pricing is often embedded in advisory relationships rather than sold as a standalone subscription. Burgiss data, now part of MSCI, is increasingly accessible through MSCI's broader platform bundle. PitchBook's pricing scales significantly based on seat count and data modules, so a family office with two analysts will pay materially less than a 20-person PE firm.

Most serious allocators run at least two platforms in parallel. The most common combination for LP allocators is Preqin plus Cambridge Associates. For direct investors and co-investment sourcing, PitchBook plus FactSet Mergerstat covers the majority of use cases.

How High-Net-Worth Individuals Can Use PE Databases to Source Co-Investment Opportunities

Co-investments now represent a meaningful and growing share of LP deployment, according to McKinsey's 2024 Global Private Markets Review. The data access and deal sourcing capabilities that enable co-investment participation are increasingly what separates sophisticated LPs from passive fund investors.

For FATFIRE-level allocators pursuing co-investments, the database workflow looks different from standard fund selection. You are not primarily benchmarking fund IRRs. You are evaluating specific transactions: the sponsor's track record in a given sector, comparable deal multiples, management team history, and exit comparables.

PitchBook's deal-level data is the most useful tool for this workflow. Specifically:

• Sponsor track record by sector: Filter a GP's historical deals by industry vertical to assess whether their claimed expertise is reflected in actual transaction history.
• Deal comp analysis: Pull transaction multiples for comparable companies in the target sector to stress-test the sponsor's entry valuation.
• Management team history: PitchBook's executive profiles link individuals to prior companies, funding rounds, and exits, which is useful for independent verification of team claims.
• Portfolio monitoring: Track existing portfolio companies for signs of operational stress or upcoming exit activity that might generate secondary opportunities.

The SEC's IAPD database adds a useful verification layer here. Before committing to a co-investment alongside a sponsor, pull their Form ADV to verify AUM, regulatory history, and any disclosed conflicts of interest. It takes 20 minutes and costs nothing.

For leveraging data for strategic decisions at the deal level, the combination of PitchBook for sourcing and IAPD for regulatory verification covers most of the due diligence infrastructure a family office needs before engaging legal and financial advisors on a specific transaction.

What Private Equity Database Subscriptions Cost (and How to Structure the Deduction)

The cost reality is straightforward: institutional-grade access to the major private equity databases runs $20,000 to $50,000 per year per platform, with enterprise pricing reaching six figures for large teams or full data API access. Individual investor tiers are often unavailable or require negotiation directly with the vendor's sales team.

That pricing structure creates a practical barrier. It also creates a tax question worth addressing directly.

Under IRS Publication 550, investment research and data subscription costs may be deductible as investment expenses. However, the Tax Cuts and Jobs Act of 2017 suspended the miscellaneous itemized deduction for individuals through 2025, which effectively eliminates the individual-level deduction for most taxpayers under current law.

The workaround that most FATFIRE allocators use: hold the subscription through an entity structure. A family office LLC, an RIA, or an investment holding company can deduct database subscription costs as ordinary business expenses, bypassing the TCJA limitation entirely. If you are paying $30,000 per year for PitchBook access as an individual, you are likely leaving a meaningful deduction on the table. If your family office LLC holds the subscription, that same cost reduces taxable income at the entity level.

This is worth a specific conversation with your tax attorney, particularly if you are in the process of formalizing a family office structure. The database cost alone rarely justifies entity formation, but it is one of several line items that aggregate into a compelling case for the structure.

For a broader view of key industry trends and insights that inform how allocators are structuring their PE exposure, the data from Preqin and Cambridge Associates on vintage year performance and sector rotation is worth reviewing alongside your tax planning.

Limitations and Data Gaps Every Serious Allocator Should Know

The databases are good. They are not complete, and treating them as authoritative without understanding their gaps is a real risk.

Survivorship bias in historical returns. Funds that underperform often stop reporting data voluntarily. GP-sourced databases like Preqin are more susceptible to this than LP-sourced databases like Burgiss. When you see a manager's historical fund performance in a database, ask whether the data includes all funds or only the ones the GP chose to disclose.

Reporting lags. PE fund valuations are typically reported quarterly with a one-quarter lag. In volatile markets, the NAV you see in a database may be 6 months stale. This matters most for secondary market transactions, where you are pricing against a moving target.

Emerging market coverage gaps. Coverage of North American and Western European funds is robust. Coverage of Southeast Asian, African, and Latin American funds is materially thinner across all major platforms. If your allocation thesis involves emerging market PE, supplement database research with local placement agents and regional LP networks.

Valuation methodology inconsistency. Private company valuations are inherently subjective, and different GPs apply different methodologies to mark their portfolios. A fund showing strong interim IRR may be using aggressive valuation assumptions that compress on exit. The ILPA reporting templates help standardize this, but adoption is not universal.

The regulatory context. The SEC's 2023 Private Fund Adviser Rules attempted to mandate standardized quarterly statements and annual audits for private fund advisers. The Fifth Circuit partially vacated those rules in 2024. If reinstated in some form, the rules would materially improve the quality and comparability of data flowing into PE databases. Active LP allocators should monitor this regulatory development, as it directly affects the reliability of the performance data they rely on for due diligence.

The practical response to these limitations: triangulate. No single database is authoritative. Cross-referencing Preqin fund data against Burgiss benchmarks and Cambridge Associates quartile rankings for the same vintage year and strategy gives you a much more reliable picture than any single source alone.

How Family Offices Use Private Equity Databases for Direct Deal Sourcing

The family office use case for PE databases differs fundamentally from the institutional LP use case, and most database vendors still optimize for the latter.

A family office deploying $50M into direct deals over a three-year period needs deal flow, not fund performance benchmarks. The database workflow centers on identifying target sectors, mapping the competitive landscape of potential acquisition targets, and tracking sponsor activity in adjacent deals that might generate co-investment invitations.

PitchBook is the most commonly used tool for this workflow. The practical applications include:

• Sector mapping: Build a target list of companies in a specific vertical by filtering on revenue range, geography, ownership structure, and recent funding activity.
• Sponsor relationship development: Identify which PE firms are most active in your target sector, track their recent acquisitions, and use that data to prioritize relationship-building with GPs who are likely to generate relevant co-investment opportunities.
• Competitive intelligence: Before approaching a target company, pull its competitive set, recent transaction comps, and any disclosed financing history to inform valuation and negotiation positioning.
• Exit monitoring: Track portfolio companies held by PE sponsors for signs of upcoming exit processes, which can surface secondary opportunities or provide advance notice of competitive dynamics in your target sector.

Understanding the deal sourcing and closing process in detail is essential context for using these tools effectively. The database surfaces the opportunity. The relationship and the diligence process close it.

For family offices earlier in building their direct investment infrastructure, reviewing essential software tools alongside database selection helps clarify which capabilities belong in the database layer versus the deal management layer.

The Regulatory and Data Standardization Trends Reshaping PE Databases

The quality of private equity database data is not static. Two forces are actively improving it, and one is creating uncertainty.

ILPA standardization. The Institutional Limited Partners Association's reporting templates have been adopted by hundreds of GPs and LPs. These templates standardize how fund managers report capital calls, distributions, NAV, fees, and carried interest. As adoption increases, the comparability of data across funds in major databases improves. If you are evaluating a fund manager and they are not using ILPA-standard reporting, that is a due diligence flag worth noting.

AI and machine learning integration. PitchBook and Preqin are both investing heavily in AI-driven data collection and verification. The practical benefit for users is faster updates, better coverage of smaller funds and companies, and more sophisticated pattern recognition in deal flow. Natural language search capabilities are improving, which reduces the time required to extract actionable insights from large datasets.

The SEC regulatory uncertainty. The 2023 Private Fund Adviser Rules, partially vacated in 2024, would have required standardized quarterly statements and annual audits for private fund advisers. The underlying regulatory intent, improving transparency and data quality in private markets, has not disappeared. Some version of enhanced disclosure requirements is likely to resurface. Allocators who build their due diligence processes around current data quality standards should build in flexibility for a world where reporting becomes more standardized and more comparable.

For context on where the industry is heading, reviewing market trends and key insights from Preqin's annual reports alongside comprehensive market analysis reports from PitchBook gives a useful dual perspective on both the LP and deal-flow sides of the market.

Choosing the Right Private Equity Database for Your Specific Situation

The selection decision comes down to three variables: your primary use case, your entity structure, and your budget relative to deployment scale.

A few practical considerations before you sign a contract:

Negotiate on data modules, not just seat count. Most vendors will unbundle their platforms. If you need PitchBook's deal data but not its fund performance layer, you can often negotiate a lower price by scoping the subscription to the modules you actually use.

Request a 30-day trial with your actual use cases. Run your real workflows, not the vendor's demo scenarios. The difference between a database that looks impressive in a sales presentation and one that actually surfaces the data you need for your specific sector and geography can be significant.

Factor in the entity structure. If you are not yet operating through a family office LLC or similar entity, the database subscription cost is one more reason to formalize the structure. The deductibility difference between individual and entity-level subscriptions is real money at $30,000+ annual spend.

Cross-reference before you commit capital. Regardless of which primary database you choose, validate critical performance data against at least one other source before making a fund commitment. The 600 to 1,000 basis point IRR dispersion between top-quartile and median funds makes manager selection the highest-leverage decision in PE investing. It is worth the cost of a second data source to get it right.

For a broader view of how the major platforms compare on industry rankings and performance metrics, and to stay current on how the data landscape is evolving, leading sources for industry insights can supplement database subscriptions with qualitative context that raw data alone does not provide.

Understanding the investment lifecycle stages also matters for database selection: the data you need for seed-stage venture sourcing differs materially from what you need for large-cap buyout fund evaluation, and not every platform covers both with equal depth.

Finally, for a consolidated view of top platforms for professionals beyond the major database vendors, the broader ecosystem of data providers, news sources, and analytical tools rounds out the infrastructure picture.

References

• Preqin - "Global Private Equity Report" (2024)
• Cambridge Associates - "US Private Equity Index and Selected Benchmark Statistics" (2024)
• PitchBook - "Annual Global Private Market Fundraising Report" (2024)
• SEC - Form ADV and Investment Adviser Public Disclosure Database (IAPD)
• Burgiss / MSCI - "Private Capital Benchmarks" (2024)
• Institutional Limited Partners Association (ILPA) - "ILPA Reporting Template and Data Standardization Guidelines" (2023)
• IRS - "Publication 550: Investment Income and Expenses" (2023)
• McKinsey & Company - "McKinsey Global Private Markets Review" (2024)