Top Private Equity Fund Performance Databases: 2026 Guide

Data sourced from Joe, the private fund performance platform powered by Dakota. Learn More | Request Access

Ask two different databases for a private equity fund's performance and you can get two different answers — not because either is wrong, but because they measured different things. One reports a pooled, anonymized median for the fund's vintage-year cohort. Another reports the individual fund's own Net IRR, sourced from LP cash flows rather than a GP's own marketing deck. A third looks through the fund entirely to the performance of the underlying portfolio companies.

For most diligence questions, that distinction matters more than which logo is on the platform. This guide compares the databases most commonly used to evaluate private equity fund performance in 2026, organized around how each one actually measures and sources its numbers — not just what asset classes it covers.

How Private Equity Performance Data Gets Built

Before comparing platforms, it helps to know the three methodologies underneath them:

• Pooled and anonymized. Managers or LPs report into a peer cohort, and the platform publishes an aggregate — a median IRR for a 2019-vintage buyout fund, a top-quartile TVPI threshold. This is the dominant model for institutional benchmarking.
• Individual and named. Performance is reported and displayed at the level of a specific, named fund, so a user can see exactly which fund produced a given Net IRR or TVPI, not just where an anonymous aggregate sits.
• Deal-level look-through. Performance is decomposed further, into the operating results of the individual portfolio companies that produced a fund's returns, rather than stopping at the fund-level multiple.

Most platforms commit to one of these three. That commitment is the real basis for comparison.

Top Private Equity Fund Performance Databases

Joe (Powered by Dakota)

Joe takes the individual-and-named approach as its starting premise, not as an add-on to a pooled benchmark. It tracks Net IRR, TVPI, DPI, and RVPI on 18,000+ private funds — including private equity buyout, growth equity, secondaries, special situations, and fund-of-funds vehicles — reported at the level of the specific, named fund that produced them.

That matters for a specific kind of PE diligence question: not "where does this fund sit in a vintage-year cohort" but "how did this exact fund perform, and how has this exact GP's fund series trended over time." Every fund record connects to Dakota's GP and sponsor intelligence — 20,000+ investment firms — and to the same allocator relationship graph that powers Dakota Marketplace, so a performance number sits next to the firm context a pooled benchmark strips out. The dataset updates daily and is queryable through Claude, ChatGPT, and CoPilot connectors, alongside API and MCP access.

Where Burgiss and Cambridge Associates require institutional relationships and Preqin and PitchBook run enterprise, quote-only contracts, Joe is priced at $9,500/year for up to 5 users, published openly.

Best for: consultants, allocators, and GPs who need an individual, named PE fund's actual track record — not a pooled cohort position — without an enterprise budget.

If your diligence question is about one specific fund rather than a cohort average, it helps to see the difference directly. Request access to Joe.

Preqin

Preqin's private equity coverage is built on the pooled-and-anonymized model. Its benchmarking tools let a user compare a fund's Net IRR, TVPI, DPI, and RVPI against a peer cohort segmented by vintage, strategy, and geography, alongside broader fundraising and investor data.

Preqin was acquired by BlackRock in 2024 for $3.2 billion, pairing its dataset with BlackRock's Aladdin and eFront platforms (BlackRock, 2024). The benchmarking methodology has stayed cohort-based: strong for seeing where a fund sits in a vintage-year distribution, limited for identifying which specific funds make up that distribution.

Best for: benchmarking a PE fund against a broad, anonymized vintage-year cohort.

PitchBook

PitchBook treats fund performance as one layer of a broader deal, valuation, and company-intelligence platform rather than a specialized benchmarking product. IRR and multiples appear alongside deal terms, dry powder, and portfolio company data, which makes it useful when a performance question is really a deal-sourcing or valuation question in disguise.

Its fund performance methodology leans on a mix of GP and LP-sourced data without the cash-flow-level verification that dedicated benchmarking providers apply, and it remains Morningstar-owned with pricing that scales by seat and module.

Best for: teams whose primary need is deal and valuation intelligence, with fund performance as a secondary layer.

Burgiss

Burgiss is widely regarded as the highest-quality source of institutional PE performance data, because its numbers are derived directly from LP cash flow reporting rather than GP self-reporting. That sourcing discipline is why pensions, endowments, and academic researchers treat Burgiss benchmarks as a credible reference point.

Burgiss remains a pooled-and-anonymized model: the cash-flow rigor applies to how the aggregate is built, not to whether an individual fund's number is disclosed by name.

Best for: institutional-grade, cash-flow-verified benchmarks where the peer group's methodology matters as much as the number itself.

Cambridge Associates

Cambridge Associates functions less as a lookup database and more as the benchmark index itself — the line an investment committee measures a manager against. Its private equity indices are widely cited in institutional reporting and asset allocation research.

Like Burgiss, it is a pooled-and-anonymized methodology: authoritative as a reference index, not built for pulling an individual, named fund's track record.

Best for: investment committees that need a credible index to measure portfolio and manager performance against.

Hamilton Lane Cobalt

Cobalt is built for what an allocator does after they have performance data, not for sourcing it fresh: commitment pacing, cash flow forecasting, scenario analysis, and portfolio construction across a diversified private markets book. Fund performance feeds into those models rather than being the primary product.

Best for: institutional allocators optimizing pacing and portfolio construction across existing PE commitments.

CEPRES

CEPRES takes the deal-level look-through approach: rather than stopping at a fund's TVPI, it decomposes performance into the operating results of the underlying portfolio companies, which is useful for investors trying to understand what actually drove a return, not just its size.

Best for: due diligence teams that need to look through fund-level multiples to the portfolio companies producing them.

Which Database Is Right for Your Team?

Most PE diligence workflows need more than one methodology. A pooled benchmark from Preqin, Burgiss, or Cambridge Associates is the right tool for a directional, cohort-level question. Joe is the right tool the moment the question narrows to a specific, named fund or GP series — the point where an anonymized aggregate stops being able to answer what's actually being asked.

For more information on Joe, request access.