Data sourced from Dakota Marketplace, the global LP and GP intelligence platform trusted by thousands of investment professionals. Learn More | Book a Demo
Public pension funds operate under a simple but demanding rule: disclose what you pay. Fee schedules for private equity, private credit, private real estate, infrastructure, and hedge fund commitments have to be made public. That transparency requirement, repeated across the public pension system and thousands of fund relationships, has quietly created one of the richest and least-utilized datasets in private markets.
Most of the industry treats this data the way it was always handled: one filing at a time, one deck at a time, manually assembled into a spreadsheet whenever someone needs to answer a fee question. Dakota has taken a different approach: aggregating fee schedules across thousands of funds into a single searchable, filterable layer inside Dakota Marketplace, organized by asset class and sub-asset class, with fields users can customize to fit exactly what they're trying to compare.
What This Actually Solves
For an investment firm raising a new fund, fee schedule benchmarking is an important exercise.
What is a typical management fee a pension pays for a middle market buyout fund right now? Where does a first-time infrastructure fund typically land on carry? Are private credit fees compressing or holding?
These aren't questions with static answers: they shift by strategy, vintage, and fund size, and getting them wrong in either direction costs a firm either investors or fee revenue.
Historically, answering these questions meant exactly what one prospect's team was doing before they found Dakota: manually building a fee schedule market map by pulling numbers out of individual decks and filings, one at a time, through another data provider that didn't actually organize the information for this purpose. It's slow, it's incomplete, and it goes stale the moment it's finished.
A Real Example
During a recent Dakota Marketplace walkthrough with a prospective client, this exact scenario played out live.
Dakota's team walked the prospect through Joe, AI account summaries, the Outlook integration, benchmarks, and conferences, a full tour of Dakota Marketplace. But the moment that visibly changed the energy of the call was fee schedules.
The prospect's team had been doing precisely the manual, deck-by-deck fee mapping described above. Seeing Dakota's data already aggregated and instantly filterable by asset class and sub-asset class was, in their words, a genuinely exciting moment. They said they hadn't seen this capability anywhere else, naming two of the largest incumbents in private markets data as platforms that simply don't offer it. The customizable field selection landed just as strongly: the ability to choose exactly which data points to surface, rather than being stuck with a fixed report format, was functionality they said neither competitor had.
Curious what your own fee benchmarking would look like without the manual, deck-by-deck grind? Dakota Marketplace's fee schedule database covers 13,000+ funds across private equity, private credit, private real estate, infrastructure, and hedge funds, filterable by asset class, sub-asset class, fund size, and vintage year. Book a demo to see it filtered to your next raise.
Why This Is Bigger Than a Feature
It would be easy to file this under "nice-to-have dashboard filter." It deserves more scrutiny than that, for a few reasons:
It's a data problem before it's a product problem. Thousands of fee schedules only become useful once they're extracted, standardized, and kept current. That's unglamorous, ongoing work: pulling structured terms out of unstructured filings and decks, at scale, continuously. It's exactly the kind of curation work that's easy to underestimate and hard to replicate quickly.
It turns public disclosure into private advantage. The underlying data is technically public. The value Dakota creates isn't in owning information no one else can see; it's in making information everyone theoretically has access to actually usable in seconds instead of weeks.
It compounds with everything else Dakota already does. Fee benchmarking sits next to allocator data, investment firm data, transaction history, and AI-powered account intelligence, all inside Dakota Marketplace. A fundraiser isn't just checking a fee number; they're checking it in the same workflow where they're already tracking the allocator relationship, the fund's competitive set, and the broader market context.
It's hard to fake with a general-purpose data platform. Broad private markets databases have historically optimized for coverage of performance and deal data. Fee terms require a different kind of structured extraction discipline, one that has to be built specifically, not bolted on.
The Bigger Signal
Feedback like this is a useful gut-check on where Dakota's real differentiation lives. It's rarely the flashiest feature on a walkthrough that wins the room: it's the one that solves a problem the prospect has been grinding through by hand for months. Fee schedules did that on this call, and the credit for it sits squarely with the data team's work turning scattered public filings into something a fundraiser can actually filter, compare, and act on in real time.
That's the pattern worth watching: not just more data, but more usable data, delivered in a way that makes a manual process disappear.
See fee schedule data for over 13,000 funds inside Dakota Marketplace, filterable by asset class, sub-asset class, fund size, and vintage year. Whether you're benchmarking a lower-middle-market buyout fund's management fee or checking where infrastructure carry is trending, the data is already aggregated.
Book a demo to see Dakota's fee schedule benchmarking in action.
