Hebbia vs Glean: Enterprise AI Research Compared (2026)
Hebbia excels at deep document analysis. Glean excels at enterprise search. Here's an honest head-to-head, where both fall short, and what enterprises actually need beyond both.
Hebbia ($3K–15K/seat/year, deep multi-document analysis, financial services and legal focus) and Glean ($260M raised, $7.2B valuation, enterprise search across 100+ connectors, ~$15–25/user/month) target fundamentally different knowledge workflows. Hebbia is built for the analyst spending days reasoning through thousands of pages to reach a decision. Glean is built for the employee who can't find the document they need across a dozen enterprise tools. They solve different problems — which is exactly why buying the wrong one is so easy to do.
Here's an honest breakdown: what each does well, where each falls short, and what to do when your enterprise needs something neither one provides.
Hebbia vs Glean: Deep Research vs Broad Search
Putting these two tools side by side is a bit like comparing a microscope and a telescope. Both help you see things you couldn't see before. One looks deep. The other looks wide. Choosing between them requires knowing what you're actually trying to see.
Hebbia's signature product, Matrix, is a multi-agent grid interface that reasons across thousands of documents in parallel — extracting structured answers from credit agreements, 10-Ks, fund documents, or any large corpus. It was designed for the analyst who needs to compare acquisition terms across 30 deals in a data room, not the engineer who needs to find the team's architecture decision record.
Glean indexes the entirety of your enterprise knowledge stack — Confluence, Slack, Salesforce, SharePoint, Jira, GitHub, and 100+ more connectors — and surfaces permission-aware answers through a single search interface. It was designed for the whole organization, not just a specialized analytical function.
Head-to-head comparison
| Dimension | Hebbia | Glean | Nexus |
|---|---|---|---|
| Core function | Deep document analysis and reasoning across thousands of pages | Enterprise search across 100+ data sources | Autonomous agents that complete entire business workflows end-to-end |
| What the AI does | Analyzes documents, extracts structured insights, answers complex analytical questions | Finds information across enterprise tools, generates answers from company knowledge | Collects data, validates it, makes decisions, handles exceptions, executes actions across systems |
| Best for | Financial analysts, lawyers, consultants with heavy document workloads | Knowledge workers who can't find information across enterprise tools | Enterprises where the bottleneck is completing work, not finding or analyzing information |
| Vertical focus | Finance, legal, consulting, defense | Horizontal (any enterprise) | Horizontal (any enterprise) |
| Document depth | Very deep: multi-agent swarm reasons across millions of pages via Matrix | Surface-level: finds and summarizes, doesn't deeply analyze | Documents analyzed as one step in larger workflow execution |
| Search breadth | Narrow: connects to document repositories | Very broad: 100+ enterprise data sources indexed | 4,000+ integrations, bidirectional (reads and writes) |
| Workflow completion | No. Produces insights for humans to act on | No. Finds information for humans to act on | Yes. Agents execute end-to-end across systems |
| Who uses it | Analysts, associates, researchers | All employees | Business teams (build agents), entire org (uses them) |
| Architecture | Proprietary ISD (Inference, Search, Decomposition), model-agnostic | Enterprise search with RAG, connector-based indexing | Agent-first with autonomous decision-making, 4,000+ integrations |
| Pricing | Per-seat: $3K–15K/seat/year | Per-user: ~$15–25/user/month | Per-agent: tied to value delivered |
| Named clients | BlackRock, KKR, Carlyle, Centerview, U.S. Air Force | Databricks, Duolingo, Grammarly, Reddit | Orange, European telecoms, consulting firms |
| Funding | $130M Series B (July 2024, a16z-led) | $260M Series F (February 2025, $7.2B valuation) | — |
| Support model | Enterprise onboarding and account management | Enterprise support and customer success | Forward Deployed Engineers embedded in your team |
Where Hebbia wins
Hebbia's strength is analytical depth on document-heavy work. When the job is "reason across 500 pages of credit agreements and extract every relevant provision," Hebbia's Matrix product was built for exactly that.
Matrix: the multi-agent research grid. Hebbia's Matrix doesn't just search documents — it reasons across them. You define the rows (documents or entities) and columns (the analytical questions you want answered), and a multi-agent swarm processes each cell in parallel. A credit analyst reviewing 40 loan agreements can populate a structured comparison in minutes rather than days. This goes far beyond what keyword search or basic RAG can do.
Financial services domain depth. The platform understands financial document types — 10-Ks, credit agreements, fund documents, prospectuses — at a level that horizontal tools don't reach. Partnerships with FactSet and Preqin reflect that domain specialization. Reported clients include BlackRock, KKR, Carlyle Group, and Centerview Partners. According to Hebbia, 40%+ of the largest asset managers are customers.
Million-page context. Hebbia's "infinite effective context window" chains agent context windows to reason across document sets that would overwhelm most AI tools. For compliance reviews, regulatory filings, or due diligence across massive data rooms, this matters.
Purpose-built UX for analysts. Hebbia's interface is designed for people whose job IS reading and analyzing documents. It's not a general-purpose AI tool. It's a specialized instrument — and that specificity is a genuine advantage for the users it serves.
Strong security posture for sensitive work. Hebbia offers SOC 2 Type II compliance, private cloud deployment, and data isolation. For financial services and legal work — where documents contain highly sensitive information — this matters as much as the analytical capability itself.
Where Glean wins
Glean's strength is breadth. When the job is "find the answer across our enterprise tools, fast," Glean does that better than almost anything else at this scale.
Enterprise-wide search. Glean indexes Confluence, Slack, Drive, SharePoint, Salesforce, Jira, GitHub, and dozens more. One search bar, all your company's knowledge. For organizations where information is scattered across tools and people waste hours looking for things, Glean provides genuine, measurable time savings. Glean publicly cites productivity improvements across its customer base including Databricks, Duolingo, and Reddit.
Everyone can use it. Hebbia serves a specific user: the analyst doing document-heavy research. Glean serves every employee in the company — from the new hire looking for the PTO policy to the engineer looking for a design doc to the sales rep looking for competitive positioning. The addressable user base is the entire organization.
Permission-aware answers. Glean respects your existing access controls. When it surfaces an answer, it only shows what you're authorized to see. Getting permissions right across 100+ data sources is genuinely hard to do well, and Glean handles it at scale.
Glean Agents. Glean has moved beyond pure search with its Agents product, allowing organizations to build AI agents that automate workflows on top of the Glean knowledge layer. This extends the platform from retrieval into action — though the depth of execution remains narrower than dedicated agent platforms.
Horizontal coverage. Glean works for every department and every industry. It's not optimized for a specific vertical, which means it works whether you're in finance, technology, healthcare, or retail.
Hebbia vs Glean: Shared Limitations
This is where the comparison gets more useful. For many enterprises, the real question isn't "Hebbia or Glean." It's "why didn't either one deliver the business outcomes we expected?"
Neither one completes workflows
Hebbia tells you what's in the documents. Glean tells you where the information is. Neither one does the work that comes next.
Consider a real business process: qualifying enterprise accounts for sales outreach. The work isn't just "find information about this company" (Glean) or "analyze these documents about this company" (Hebbia). It's:
- Collect data from the CRM, LinkedIn, news sources, financial filings, and internal notes
- Synthesize it into a coherent account profile
- Score the opportunity against your ICP
- Identify buying signals and competitive movements
- Route the qualified accounts to the right reps
- Deliver structured intelligence in the format the team actually uses
Steps 1–2 might involve document analysis (Hebbia territory) and information search (Glean territory). Steps 3–6 require judgment, decision-making, cross-system execution, and follow-through. Neither Hebbia nor Glean reaches there. The analysis and search are complete. The work isn't.
Neither one connects analysis to action
Hebbia produces insights. Those insights live in Hebbia. Someone has to take them, move to another system, validate the findings, make a decision, and take action. Glean finds information. That information appears in the Glean interface. Someone has to take it, open the relevant system, and do the work.
This might seem obvious, but it's the reason most enterprise AI deployments don't move the business metrics leadership expected. The productivity gain on finding and analyzing information is real. But if the entire process takes 10 hours and the finding/analyzing part was 2 hours, you've improved 20% of the workflow. The other 80% is untouched.
Per-seat and per-user pricing limits scale
Hebbia charges $3,000–15,000 per seat per year. Glean charges approximately $15–25 per user per month. Both are reasonable for what they deliver. But both tie cost to headcount, which means the economics scale linearly with the number of people using the tool.
For a defined group of analysts (Hebbia) or a whole organization (Glean), this model works. But when the goal shifts from "make people faster" to "complete work autonomously," headcount-based pricing breaks down. An AI agent that onboards customers or researches accounts doesn't need a seat. It needs to exist. The cost model should reflect work completed, not humans augmented.
What Both Platforms Don't Do
Here's the pattern we see consistently. Enterprises evaluate Hebbia because they want faster, deeper document analysis. They evaluate Glean because they want better enterprise search. Both tools deliver on their promises. But neither one delivers the business process transformation that leadership actually wanted.
The reason is structural. Hebbia and Glean are both information tools. They help humans find and understand information better. They don't complete the work that information supports.
The enterprises that move past this pattern are the ones that stop asking "how do we read faster?" and start asking "how do we complete this process end-to-end, autonomously, at scale?"
That's a different category entirely. It requires AI that doesn't just analyze or search, but collects data from multiple systems, validates it, makes decisions within guardrails, handles exceptions, escalates when uncertain, and executes actions. It requires agents, not assistants or analytical tools.
What Nexus delivers that neither Hebbia nor Glean can
Nexus is an autonomous agent platform paired with Forward Deployed Engineers. Agents complete entire business workflows end-to-end. Document analysis and information retrieval are built into the workflow, not the entire workflow.
Orange Group (multi-billion euro telecom) didn't need to find information faster. They needed agents that complete customer onboarding end-to-end. The agent collects customer data in real-time, validates it against backend systems, checks compatibility, routes exceptions, and escalates complex issues with context. Deployed in 4 weeks. 50% conversion improvement. ~$6M+ yearly revenue. 90% autonomous resolution. 100% team adoption.
A European telecom (13,000+ employees) deployed a dozen agents across their organization. 40% of support volume freed across millions of interactions. Business teams own the agents.
In each case, the enterprise needed AI that goes beyond finding and analyzing information to actually completing the work. Document analysis isn't absent from these workflows — it's embedded within them as one step among many.
How Nexus differs from both
| Capability | Hebbia | Glean | Nexus |
|---|---|---|---|
| Analyze documents deeply | Yes (core strength, via Matrix) | Surface-level | Yes (as part of workflow) |
| Search across enterprise tools | Limited (document repos) | Yes (core strength) | Yes (4,000+ integrations) |
| Make decisions within guardrails | No | No | Yes |
| Handle exceptions and escalate | No | No | Yes |
| Execute actions across systems | No | No | Yes |
| Complete workflows end-to-end | No | No | Yes |
| Business teams build and own it | No (analysts use it) | No (employees use it) | Yes |
| Forward Deployed Engineers | No | No | Yes |
How to decide
Choose Hebbia if your bottleneck is analytical depth on document-heavy work. Your analysts spend days reading documents that AI could analyze in minutes. You're in financial services, legal, or consulting. The output you need is structured insights from documents — a completed Matrix analysis — and your team handles everything after that.
Choose Glean if your bottleneck is finding information across enterprise tools. Your employees waste hours searching for things scattered across Confluence, Slack, Salesforce, and Drive. The output you need is fast, accurate answers from your company's knowledge, and your team handles everything after that.
Choose Nexus if your bottleneck isn't finding or analyzing information but completing the work at scale. Your processes span multiple systems, require judgment at each step, involve exceptions and edge cases, and consume hours of human effort that compound across hundreds or thousands of instances. You need AI that executes, not just AI that reads.
Here's the cleanest test: think about the process you're trying to improve. How much time is spent reading and finding information? How much time is spent on everything else — validating, deciding, routing, executing, following up? If the reading is 80% of the effort, Hebbia or Glean can make a real dent. If the reading is 20% and the other 80% is execution, you need a different kind of tool.
Frequently asked questions
What is Hebbia Matrix and how does it work? Hebbia Matrix is Hebbia's core product — a multi-agent research grid where users define rows (documents or entities) and columns (analytical questions). A swarm of AI agents processes each cell in parallel, extracting structured answers across large document sets simultaneously. It's designed for analysts who need to compare, synthesize, or audit information across dozens or hundreds of documents at once — for example, reviewing acquisition terms across 40 loan agreements side by side.
How much does Hebbia cost? Hebbia pricing is not publicly listed, but reported figures from enterprise customers and press coverage indicate a range of approximately $3,000 to $15,000 per seat per year. This makes Hebbia one of the most expensive per-seat AI tools on the market — a reflection of the specialized, high-value analytical work it performs. Most customers are financial services firms, law firms, and consulting firms where analyst time costs far more than the tool.
What industries use Hebbia most? Hebbia is primarily used in financial services (investment banks, private equity, hedge funds, asset managers), legal (law firms and in-house legal teams), management consulting, and defense. Reported clients include BlackRock, KKR, Carlyle Group, Centerview Partners, and the U.S. Air Force. Hebbia reports that 40%+ of the largest asset managers are customers.
Can Hebbia and Glean be used together? Yes — and this is one of the more logical enterprise AI stack combinations. Glean handles day-to-day information retrieval across enterprise tools (Slack, Confluence, Salesforce). Hebbia handles deep analytical research on specific document-heavy tasks (deal documents, regulatory filings, litigation records). They address different user populations and different use cases, so there is minimal overlap and real complementarity. Many enterprises in financial services use both.
What are Glean Agents and how do they differ from Hebbia? Glean Agents is Glean's agentic layer, allowing organizations to build automated workflows on top of Glean's enterprise knowledge graph. Glean Agents can trigger actions, summarize information, and route tasks based on content retrieved from Glean's indexed data sources. The key difference from Hebbia: Glean's agent capabilities extend from a search-first architecture (broad retrieval, action on top), while Hebbia's Matrix is a research-first architecture (deep document analysis, synthesis as the output). Neither is a full workflow execution platform in the way that dedicated agent platforms are.
Is Hebbia available for companies outside financial services? Yes. While Hebbia's deepest domain expertise and most of its named clients are in financial services and legal, the platform works with any large document corpus. Use cases in consulting, government, healthcare compliance, and research-intensive industries are viable. The ISD architecture (Inference, Search, Decomposition) is model-agnostic and document-type-agnostic — it works with PDFs, contracts, research reports, and regulatory filings across sectors.
Worth exploring?
If your team's bottleneck has shifted from "we can't find or analyze information fast enough" to "we can't complete the work fast enough," it might be worth seeing how enterprises are solving that.
Every Nexus engagement starts with a 3-month proof of concept tied to measurable outcomes. Forward Deployed Engineers embed with your team from day one. You see the results before committing. You can exit anytime.
100% of clients who started a POC converted to an annual contract.
