What does the legal tech landscape look like in Europe?

Most identified players monetise their technology through SaaS subscriptions, traditionally priced per user, while some offer database subscriptions (e.g. Jus Mundi, Karnov Group) or online legal services themselves (e.g. LegalPlace, Legálitas). One-off implementation and professional services typically account for only a small share of revenue (e.g. ~2% of Oneflow's Q1 2025 sales; Oneflow, May 2025). Revenue is therefore largely recurring, as legal tools become embedded in customers' daily workflows. For instance, Karnov Group generates ~90% of net sales from subscriptions, with ~97–98% of customers in its Nordic Region North renewing annually and all 20 largest customers by licences renewing for >20 years (Karnov Group, March 2026). Customer acquisition can nevertheless be challenging, with ~33% of users reporting no dedicated LegalTech budget. At the same time, larger law firms and corporate legal departments typically require direct sales, complex tenders and long sales cycles. More broadly, buyers rely heavily on internet research, personal networks and peer recommendations, making references and reputation important to winning new customers (Legal Tech Verband, March 2025). Implementation further increases acquisition costs, as customisation work is often not paid separately and AI-native LegalTech players need to deploy dedicated implementation engineers to support deployments (Contrary Research, October 2026; Legal Tech Verband, March 2025). In addition, competition for large enterprise contracts can also pressure pricing, with some vendors reportedly offering steep discounts or even free access to win multi-year contracts with large law firms (Contrary Research, October 2026). By contrast, smaller customers are increasingly acquired through freemium and low-cost self-service models (e.g. Genie AI, Oneflow). AI changes the underlying economics, with vendors shifting toward hybrid models combining fixed licences with usage-based charges. This reflects AI's variable inference costs, which vendors have to absorb in fixed per-seat contracts, creating material margin pressure. To illustrate, Harvey (US)'s gross margin fell from positive ~50% to negative ~50% in H1 2026. as its token consumption rose ~20x vs. 2025 following upgrades to its AI agents (Gartner, September 2026; Contrary Research, October 2026).