LP Database Guide: Choosing the Right Investor Intelligence for PE Fundraising

Executive Summary

An LP database is a subscription platform that catalogs institutional investors' allocation history, fund commitments, contact details, and stated preferences to help fund managers identify and prioritize potential capital sources. These tools are useful but often misunderstood, with many GPs wasting resources on broad exports instead of strategic targeting.

Most GPs start their fundraise by buying a database. They export a list, blast emails, and wonder why conversion rates sit below 2%. The database isn't the problem. The approach is.

I've watched firms spend $50,000 on Preqin licenses and close zero new LPs from it. I've also watched a first-time fund manager use a $16,500 Dakota subscription to book 40 meetings in 90 days. The difference wasn't the tool. It was whether the GP understood what LP databases actually do, and more importantly, what they don't.

What Is an LP Database?

An LP database is a subscription platform that catalogs institutional investors' allocation history, fund commitments, contact details, and stated preferences to help fund managers identify and prioritize potential capital sources.

That definition sounds simple. The execution is anything but. These platforms aggregate data from regulatory filings, public disclosures, conference registrations, and self-reported profiles. Some scrape. Some survey. Some do both. The result is a snapshot of allocator behavior that's always at least partially stale.

The institutional investor universe is massive. Preqin tracks over 4,800 private equity investors globally. PitchBook covers the broader financial landscape with LP profiles embedded in a much larger dataset. Dakota focuses specifically on U.S. institutional allocators with around 4,000 profiles. FINTRX catalogs more than 4,400 family offices.

None of them are complete. That's the first thing you need to accept.

The Major LP Databases Compared

Five platforms dominate the fundraising intelligence market. They're not interchangeable.

Preqin is the legacy standard. Coverage spans global alternatives, including PE, real estate, infrastructure, hedge funds, and private debt. Pricing runs $25,000 to $81,000 per year depending on modules and seats (Vendr 2025 transaction data). Strengths: fund performance benchmarking, historical commitment data, global scope. Weakness: contact data decays fast, and the platform wasn't built for fundraising workflows. You're buying research, not a prospecting tool.

PitchBook costs $12,000 to $70,000 per year and is the broadest platform, covering companies, deals, funds, and LPs in a single interface. It's the right choice if your firm does both deal sourcing and fundraising from the same seat. The LP data is solid but secondary to PitchBook's core strength in deal and company intelligence. Auto-renewal clauses include 5-10% annual escalators, so negotiate caps upfront.

Dakota Marketplace starts at roughly $16,500 per year ($1,000 for each additional user). Dakota takes a different approach: narrower dataset, deeper fundraising-specific workflow. It tracks institutional allocators, consultants, and intermediaries with an emphasis on meeting readiness. The platform includes CRM-like features for tracking LP engagement. It's less useful for benchmarking and global coverage, more useful for actually booking meetings.

FINTRX focuses on family offices and RIAs. With 4,400+ family office profiles and growing coverage of registered investment advisors, it fills a gap the others mostly ignore. Pricing isn't published but runs in the mid-five-figure range for institutional access. If family offices are your primary LP target, FINTRX is the specialized tool.

Altss is the newest entrant, built on OSINT (open-source intelligence) methodology. It emphasizes live-verified contacts, behavioral signals, and mandate-level data. Early pricing positions it competitively against Preqin and PitchBook. The platform validates contacts monthly through human verification, which produces lower bounce rates. Coverage is still growing compared to established players.

Here's the honest comparison at a glance:

What Commercial Databases Miss

Here's the part vendors won't tell you. Every commercial LP database suffers from the same structural problem: the data describes what allocators said they'd do, not what they're actually doing.

An endowment's stated allocation target might be 15% to PE. Their actual deployment over the last 18 months might be 4%, because their existing portfolio is over-allocated and distributions have dried up. Only 327 U.S. PE funds reached final closes in 2024, the lowest count since 2013 (PitchBook 2025 Annual US PE Breakdown). The gap between listed allocators and actual capital deployment has never been wider.

Stale contact data compounds the problem. Investment professionals change roles frequently. A database might list a CIO who left 14 months ago. The email bounces. The new CIO has different priorities. You've wasted a touchpoint and potentially burned the relationship.

Behavioral lag is the real killer. When an LP commits to a fund, that commitment might not appear in any database for 6 to 12 months. By the time you see it, the LP's allocation budget for that vintage year could be fully deployed. You're prospecting against a reality that no longer exists.

The databases also miss informal channels entirely. A family office principal who commits $5M to a fund through a personal introduction won't show up in any regulatory filing. A pension fund's emerging manager program that allocates through a consultant intermediary gets attributed to the consultant, not the underlying LP.

(I once watched a GP spend three months chasing an LP whose listed allocation to PE was $200M. The LP had quietly paused all new commitments eight months earlier. That information existed in the market. It just wasn't in any database.)

Primary-Source LP Intelligence

At Praxis Rock, we maintain a proprietary network of over 300,000 contacts built from primary sources. Not scraped. Not aggregated from public filings. Sourced from direct interactions, verified through ongoing relationships, and enriched with behavioral data that commercial databases can't capture.

The distinction matters for a specific reason: primary-source intelligence reflects what LPs are actually doing right now, not what a database entry from eight months ago suggests they might consider.

When LPs tell us what they want in 2026, that intelligence flows directly into how we advise GPs. We know which allocators are actively deploying, which ones are paused, which have just received distributions that freed up capacity, and which are rotating away from certain strategies. Commercial databases can't capture that because they don't have the relationships to source it.

This isn't a pitch to skip databases altogether. It's context for why databases alone produce 2% conversion rates while relationship-backed introductions convert at 10x that level.

How to Choose an LP Database

The right database depends on four things: your fund size, your strategy, your LP target profile, and your internal resources.

If you're raising under $250M for a first or second fund. Dakota is the right starting point. The fundraising-specific workflow matters more than global coverage when your universe is 200 to 400 realistic LP targets. At $16,500, the capital efficiency is hard to beat. Pair it with FINTRX if family offices represent more than 30% of your target allocation.

If you're raising $250M to $1B with an established track record. Preqin's benchmarking data becomes essential. LPs at this level will compare your fund against vintage-year quartiles, and you need the same data they're using. The $25,000 to $50,000 tier gives you what you need without paying for modules you won't touch.

If your firm does both fundraising and deal origination. PitchBook's integrated dataset avoids the cost of maintaining two separate subscriptions. The trade-off is less depth on LP-specific intelligence, but the workflow efficiency matters for lean teams running dual processes.

If you're targeting international allocators. Preqin is the only option with meaningful global coverage. Dakota and FINTRX are primarily U.S.-focused. PitchBook has international data but it's thinner outside North America and Western Europe.

Two mistakes I see repeatedly. First, buying the most expensive platform because it feels comprehensive. Comprehensive doesn't mean useful if your team doesn't have the bandwidth to work the data. Second, treating the database as a substitute for LP relationships rather than a supplement to them. The firms that convert database leads into commitments are the ones that already understand their investors. The database just helps them find more of the right ones.

PE fundraising declined for three consecutive years through 2024, with only $906.9 billion raised through the first nine months of 2025, down from over $1.7 trillion in 2022 (PitchBook). In that environment, the quality of your LP targeting matters more than the quantity of your outreach. Pick the tool that matches your actual workflow, not the one with the most impressive demo.