Harvey vs Legora
A vendor-disclosed comparison. Every cell below is sourced to the vendor's own page; cells marked "not publicly disclosed" reflect the absence of a first-party statement and are intentionally not back-filled from PitchBook, Crunchbase, or trade-press aggregators.
| Dimension | Harvey | Legora |
|---|---|---|
| Latest funding round (publicly disclosed) | $200M Series, March 2026; total raised over $1B ↗ | Not publicly disclosed |
| Valuation (publicly disclosed) | $11 billion ↗ | Not publicly disclosed |
| Customers (vendor-stated) | 1,300+ customers across 60+ countries ↗ | 980+ customers across 30+ markets ↗ |
| Headcount (vendor-stated) | Not publicly disclosed | 375+ coworkers ↗ |
| Stated audience | Global law firms and Fortune 500 enterprises ↗ | Law firms and in-house teams; practice areas including M&A, litigation, banking, tax, insurance ↗ |
| Featured customer logos (sample of 5, vendor-displayed) | A&O Shearman, Reed Smith, PwC UK, KKR, Bridgewater ↗ | Not publicly disclosed |
| Stated capability set | Legal research; document analysis and bulk processing; contract analysis and due diligence; customizable workflow agents; mobile access ↗ | Tabular review; Word add-in for drafting; legal research; Outlook integration; portal-based workflow automation ↗ |
| Pricing | Undisclosed; "Request a Demo" ↗ | Undisclosed ↗ |
| Public hallucination / accuracy statement | Not publicly disclosed | Not publicly disclosed |
| Year founded | Not publicly disclosed | Not publicly disclosed |
| Headquarters | Not publicly disclosed | Not publicly disclosed |
Legal AI Governance does not sell, resell, take referral fees, or accept advertising from any vendor on this site. Every cell above is sourced to the vendor's own page; cells marked "not publicly disclosed" reflect the absence of a first-party statement and are intentionally not back-filled from third-party aggregators.
Where the two products overlap
Both Harvey and Legora compete for the same buyer: a law firm or in-house legal team that wants AI integrated into the practice stack rather than a standalone consumer chat tool. Both ship legal research, document analysis, and workflow capabilities. Both operate at meaningful scale by the customers-stated metric: Harvey reports 1,300+ customers across 60+ countries; Legora reports 980+ customers across 30+ markets. Both decline to disclose pricing publicly and route prospects through demo requests rather than self-serve onboarding. Neither carries a published hallucination or accuracy claim on the homepage we reviewed.
The natural reader question, "if both ship the same general capabilities, what differs?", has to be answered through the details each vendor publishes about itself, not through ranked comparison. The next two sections walk that.
Where their public positioning diverges
Harvey leads on funding and global enterprise reach. The most recent disclosure (March 2026) names Harvey's $200M round led by GIC and Sequoia, total raised over $1 billion, and an $11 billion valuation. Harvey's customer marketing emphasizes AmLaw firms (A&O Shearman, Reed Smith), the Big Four (PwC UK), and Fortune 500 enterprise (KKR, Bridgewater).
Legora leads on European-vertical specificity and product surface area for the daily lawyer workflow. Legora's about page foregrounds practice-area depth (M&A, litigation, banking, tax, insurance) and integration with the tools a lawyer already uses (Word add-in, Outlook integration). The 375-coworker headcount disclosure suggests a denser product team relative to customer-count parity with Harvey, and the Stockholm origin shows in the European customer mix that the rest of the legaltech market knows Legora for, even if the about page itself does not enumerate logos.
Neither vendor publicly states founding year, headquarters, or a hallucination-rate accuracy claim on the pages we reviewed. The Stanford RegLab study summarized on the AI hallucination explainer tested legal-AI tools generally and found that products marketed as "hallucination-free" still hallucinated on 17% to 33% of legal-research queries. Buyers should treat any vendor's accuracy claims as marketing language until tested on their own corpus.
Which fits which firm
The defensible buyer-side read, given the public record:
- AmLaw 100 firms and Fortune 500 in-house legal: Harvey is the safer brand pick on customer-marketing-fit alone. The named-customer roster overlaps the AmLaw / Fortune 500 audience the firm is already procurement-aligned with.
- Mid-market firms with European or cross-border practice: Legora's European customer concentration and practice-area-depth framing matches the kind of practice areas (banking, tax, insurance, M&A) where vertical specificity matters more than brand recognition. The Word and Outlook integrations also reduce the change-management lift relative to a firm that has not adopted a separate practice management surface.
- Solo and small firms (1-10 attorneys): Neither vendor publicly markets toward this segment. Both decline to disclose pricing, which typically signals enterprise procurement. Solo and small-firm buyers will likely find the consumer-tier alternatives (Microsoft Copilot for legal, ChatGPT Team or Pro, Claude Pro or Team) more accessible. The Best AI for Law Firms 2026 hub covers the wider option set.
Cross-cutting governance considerations apply to both vendors and to their alternatives. The verification protocol, vendor due diligence file, and supervisory documentation that ABA Formal Opinion 512 expects do not depend on which tool the firm chooses. The Opinion 512 compliance guide walks the rule-by-rule documentation; the vendor due-diligence checklist is the 11-item file the policy template assumes.
How to evaluate either on your own corpus
A meaningful evaluation in 2026 looks past the demo. Three practices reduce the risk of buying on marketing language:
- Run your own hallucination test. Take 20 to 50 of your firm's recent legal-research queries (the actual ones, not synthetic) and run them through the candidate tool. Compare against the verified answers your associates produced. The Stanford methodology summarized at how often AI hallucination happens is the model.
- Get the Terms of Use and DPA before binding. Confirm the data-retention posture, training-on-input behavior, and any sub-processor list relevant to client-confidentiality obligations under Rule 1.6. The vendor checklist maps the 11 due-diligence items.
- Document the supervisory protocol. Whichever vendor wins, the firm needs a written record of who reviews AI-assisted work product before it leaves the firm. This is the supervisory duty Rules 5.1 and 5.3 reach. The policy template includes the section structure.
Buyers who skip these because the vendor demoed well are the buyers most exposed at the next renewal cycle, when the malpractice carrier's underwriter asks how AI use is policed. See AI Liability Insurance for Law Firms for the current carrier landscape and the seven questions to put to the underwriter.
How this page is built
Every cell in the comparison table is sourced to a URL on the vendor's own site, dated 2026-05-05. Cells marked "not publicly disclosed" reflect the absence of a first-party statement on the pages reviewed; they are intentionally not back-filled from PitchBook, Crunchbase, or trade-press aggregators, both because those secondary sources rot quickly and because Lanham Act considerations make first-party sourcing the safer foundation for any direct comparison. The page will be re-verified quarterly. Last verified: May 5, 2026.