Build vs Buy AI Research Tools: What Analysts Actually Say
Rogo costs $3,300 per seat. Balyasny spent millions on custom AI. Most funds need both. Here is the honest build-vs-buy assessment from Wall Street analysts.
TL;DR
- Rogo ($750M valuation, 25,000 users) and Hebbia ($700M, ~$20K/seat) lead the AI research tools market. Analyst reviews: “mediocre and underwhelming” for deliverable output.
- Off-the-shelf tools cover 80% of use cases. The remaining 20%, where proprietary methodology lives, requires custom infrastructure.
- Full custom build (Balyasny model): $6-10M/year + 20-person AI team. Out of reach for most funds.
- The middle path: SaaS for commodity work, custom for the differentiated 20%. Most funds will run both.
- 95% of hedge fund managers now allow AI tools (AIMA, September 2025). The question is allocation, not adoption.
$3,300 Per Seat for “A Pretty Decent ChatGPT Wrapper”
The marketing from AI research vendors promises 10+ hours saved per week. The analyst reviews on Wall Street Oasis say “mediocre and underwhelming” and “not ready for prime time.”
Both statements are partially true, which is what makes the build vs buy decision for AI research tools in 2026 harder than it looks.
Here is what the landscape looks like with the promotional language stripped away.
What the Vendors Promise
Rogo ($750 million valuation, $75 million Series C led by Sequoia, January 2026) positions itself as a personal AI analyst. 25,000 finance professionals across Rothschild, Jefferies, Lazard, Moelis, Nomura, and others. It ingests 65 million sources including SEC filings, S&P Global data, FactSet, Crunchbase, and live news. The pitch: 10 or more hours saved per week on meeting prep, company profiling, and market research. Pricing: around $3,300 per seat per year on multi-year contracts.
Hebbia ($700 million valuation, $130 million Series B led by a16z) takes a different approach: multi-agent document processing. A grid interface where documents are rows, questions are columns, and AI-generated answers fill the cells. Every output cites its source. The platform claims over 40% of the largest asset managers by AUM as clients, managing decisions across $15 trillion in global assets. Pricing is not public but estimated around $20,000 per seat per year. No free trial, no self-serve.
AlphaSense is the incumbent: 300 million documents, NLP-powered search, integrations with expert networks via its Tegus acquisition. The broadest coverage of any platform in the space.
The marketing across all three tells a consistent story: AI saves hours, accelerates research, gives your team an edge. The analyst reviews tell a different one.
What Analysts Actually Say About AI Research Tools
Wall Street Oasis forums, G2 reviews, and Gartner Peer Insights paint a picture that vendor marketing carefully avoids.
On Rogo, analysts at Lazard and Moelis described the experience as “mediocre and underwhelming” and “not ready for prime time, more focused on selling a dream.” One anonymous investment banking analyst was blunter: the platform “also hallucinates sometimes,” requiring manual verification of deal values. The consistent complaint: “Doesn’t actually produce anything I can submit to a client or partner.” The main use case that stuck was summarizing earnings updates.
On Hebbia, G2 and community feedback flagged specific pain points: the Google Drive integration “didn’t work well enough,” Excel integration was “still early,” file management was “not easy,” and the platform could not export to Word or PDF. The recurring theme: powerful for internal exploration, weak for producing deliverable output.
On AlphaSense, Gartner reviewers noted that the “financials section frequently incomplete, stale, or has errors,” with broken filters, a steep learning curve, and strict 90-day contract terms.
These are not cherry-picked complaints. They represent a structural pattern: AI research tools work well for basic tasks (summarizing, quick lookups, getting up to speed on an unfamiliar company) and fall short for the work that actually matters at a fund (producing deliverable analysis, encoding proprietary methodology, and scaling reliably across thousands of documents).
The 80/20 Rule That Defines the Build vs Buy Decision
A credit fund’s internal analysis, cited by Resonanz Capital’s research on hedge fund AI adoption, found that off-the-shelf tools like Microsoft Co-Pilot covered 80% of general use cases. The remaining 20%, specific to their investment methodology, their risk scoring, their sector rotation logic, required custom infrastructure.
That 20% is where alpha lives. And it is precisely the part that no SaaS tool can address, because addressing it requires encoding how a specific firm thinks, not how firms in general think.
Rogo serves 25,000 users across hundreds of firms. It cannot simultaneously optimize for how a fundamental long/short fund in New York analyzes consumer internet stocks and how a credit fund in London evaluates distressed debt. The analysis engine is necessarily generic. That is not a criticism. That is the economics of SaaS at $3,300 per seat.
The Custom Build Extreme
At the other end, Balyasny Asset Management ($29 billion AUM) built “BAMChatGPT,” an internal AI platform used by 80% of staff. They recruited a 20-person AI team from Google, DeepMind, and the CIA.
| Approach | Annual Cost | Timeline | Control |
|---|---|---|---|
| SaaS (Rogo) | $3,300/seat | Immediate | Generic, 80% coverage |
| SaaS (Hebbia) | ~$20,000/seat | Immediate | Better depth, still generic |
| Full custom (Balyasny) | $6-10M/year | 18-24 months | Total, proprietary |
| Hybrid (SaaS + custom 20%) | $50-150K/year + seats | 4-8 weeks for custom | Best of both |
The custom build delivers maximum control: proprietary methodology encoded, complete data privacy, output tailored to the firm’s exact workflow. The cost is prohibitive for any fund that does not have the budget to hire a dedicated AI engineering team.
The Middle Path Most Funds Will Take
The pattern emerging across the industry, according to both Resonanz Capital and the AIMA’s research, is a hybrid approach. Most firms are using commercial tools for common tasks (earnings summaries, company profiles, quick research) while investing selectively in proprietary capabilities for the differentiated work.
This makes sense economically. A $3,300 Rogo seat covers the 80% where generic analysis is sufficient. Custom infrastructure covers the 20% where proprietary methodology matters. The total cost is a fraction of Balyasny’s approach, but the output on the differentiated work is far more useful than what any SaaS tool produces.
When to Use Off-the-Shelf AI Research Tools
- The task is common across the industry (earnings summaries, company profiles, meeting prep)
- Generic analysis is sufficient (getting up to speed on an unfamiliar name)
- Speed matters more than depth (quick lookup during a call)
- The output is for internal consumption only, not deliverable to clients or partners
When to Build Custom AI Research Infrastructure
- The analysis encodes your firm’s specific investment methodology
- You need cross-portfolio intelligence (how one position affects another)
- Institutional memory matters (why you exited a position three quarters ago, what signals you missed)
- The output feeds directly into investment decisions and needs to be auditable
- You need overnight scheduled research across your full position list, not one-off queries
- Data privacy is non-negotiable (no client data in shared infrastructure)
The Overlap Zone
- Connect custom infrastructure to the same data sources SaaS tools use (FactSet, S&P Global, Daloopa all publish MCP connectors)
- Use SaaS for breadth, custom for depth
- Let analysts choose the right tool for the task rather than mandating one platform
What This Means for Fund Managers in 2026
95% of hedge fund managers now allow employees to use AI tools, according to AIMA’s September 2025 survey. The question is no longer whether to adopt. It is how to allocate spending between generic and custom, and when to move from one to the other.
The honest assessment: start with Rogo or AlphaSense for the commodity work. They are good enough for 80% of use cases, and the per-seat cost is reasonable. Run that for a quarter. Pay attention to the moments where your analysts say “I wish it could do X” or “this doesn’t match how we think about Y.” Those moments map the 20% where custom infrastructure pays for itself.
Building the custom 20% does not require a Balyasny-scale investment. It requires identifying the three to five workflows where proprietary methodology matters most, connecting them to structured data sources, and delivering output in a format your team actually uses. The MCP connector ecosystem means the data integration is a solved problem. The engineering challenge is encoding your firm’s specific thinking into a system that runs reliably, every day, without anyone needing to maintain it manually.
The vendors will not tell you this. They want the full seat. The in-house team will not tell you this. They want the headcount. The right answer is almost always both, allocated deliberately, with clear boundaries between what generic tools handle and what requires your firm’s specific logic.
FAQ
Is Rogo worth the $3,300 per seat?
For commodity research tasks (earnings summaries, company profiles, quick meeting prep), yes. Analyst reviews confirm it handles these well. For producing deliverable analysis or encoding your firm’s specific methodology, no. The consistent complaint from Wall Street Oasis users is that the output cannot be submitted directly to clients or partners without significant rework.
How much does it cost to build custom AI research infrastructure?
The full Balyasny model costs $6-10 million per year with a dedicated 20-person team. But most funds do not need that. Building custom infrastructure for the 3-5 workflows where proprietary methodology matters most costs a fraction of that, especially with the MCP connector ecosystem handling data integration.
What is the 80/20 rule in AI research tools?
Off-the-shelf tools cover roughly 80% of general research use cases (the commodity work). The remaining 20%, where a fund’s specific investment methodology, risk scoring, and sector logic live, requires custom infrastructure. That 20% is where competitive advantage concentrates.
Should funds build or buy AI research tools?
Most funds should do both. Use SaaS tools for the 80% of work that is generic across the industry. Build custom for the 20% that encodes proprietary methodology. The hybrid approach delivers better results than either extreme at a fraction of the cost of a full custom build.
What are the main limitations of current AI research SaaS tools?
Three structural issues: the output is too generic to be deliverable without rework, the tools cannot encode firm-specific methodology, and data integration across platforms remains manual. These limitations are inherent to the SaaS model serving thousands of firms simultaneously.
Sources: Rogo Series C (Sequoia, January 2026, $750M valuation), Hebbia Series B (a16z, July 2024, $700M valuation), Wall Street Oasis analyst reviews (2025-2026), Gartner Peer Insights (AlphaSense), Resonanz Capital “How Hedge Funds Are Really Using Generative AI,” AIMA September 2025 survey (95% AI tool adoption), Sacra Rogo analysis (context window limitations), Anthropic Financial Services Plugins (11 MCP connectors)
Last updated: April 14, 2026
By BetterAI | We build custom AI research infrastructure for European investment firms.