eDiscovery Buyers Report 2026
Moving every matter to a public cloud-only eDiscovery model compounds risk across four areas: cost, security, data residency and AI model access.
Consolidating onto a single cloud-only model means compounding risk across four
dimensions: cost, security, data governance, and AI model access.
SaaS consumption-based pricing, typically tied closely to data volume, leaves teams exposed to the cost volatility AI accelerates.
Organizations report that their data doubles on average every 21 months, and the mass adoption of AI tools — ChatGPT and Claude among them — promises to steepen that curve: every AI-assisted chat, transcript, and generated draft becomes potentially discoverable material. Under consumption-based pricing, that volume converts directly into spend: each additional terabyte carries hosting fees and reviewer hours, and neither falls away when budgets tighten.
20% yearly increase in eDiscovery costs over the last three years, according to respondents
68% of respondents are concerned that AI-driven data growth will materially raise costs over the coming three years
This cost volatility can be seen as driving the reemergence of private and on-premises models, where AI workloads can run at a lower cost on infrastructure the organization controls. Buyers doubt that consumption-based pricing models will scale gracefully as AI-driven data volumes grow.
Multi-tenant environments can complicate how teams govern model training and data
exposure. In a public cloud-only (multi-tenant) model, a team’s matter data shares
infrastructure with other customers, giving them less direct control over how AI interacts with their data and who can access it. This leaves some teams uncertain about whether privileged or confidential data could be used to train or fine-tune shared AI models or could be retained in ways they can’t audit. These concerns push organizations toward private deployment for sensitive matters.
35% of buyers named cybersecurity and CISO governance policies as top
factors in deployment decisions
2 in 5 respondents who reported a rising share of private-deployment matters named data exposure to AI training models in shared environments as a top driver
Buyers view private deployment as more controlled: buyers govern their own data and contain the exposure a shared environment creates.
Most of the surveyed law firms, legal service providers, and corporations operate on a global scale, and their eDiscovery teams must satisfy regulatory and data sovereignty obligations that may differ from one jurisdiction to the next.
77.5% of respondents rate regulatory or data sovereignty requirements
(e.g., GDPR, sector regulators, cross-border data transfer rules) as
an important decision-making factor in deployment choice
Respondents say that getting boxed into a public cloud environment, especially where that cloud is chosen by their technology vendor, limits AI model flexibility—and they unanimously reject that limit.
100% call the ability to run their own proprietary or fine-tuned AI models important.
Buyer requirements and vendor offerings diverge most sharply on this dimension.
Taken together, these dimensions describe a single trade. A one-size deployment model asks buyers to give up cost predictability, data governance, jurisdictional control, and AI model choice in exchange for the vendor’s operational simplicity. Many buyers have weighed that trade and rejected the compromises it involves.
Chapter 5
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All the data from 200 senior buyers on AI adoption, private deployment, cost and portability, plus the ACEDS industry pulse survey.