Within the context of electronic discovery (eDiscovery), the task of review refers to the examination of electronically stored information (ESI) to identify content that is relevant, responsive, privileged, or otherwise significant to a legal matter. As one of the three core eDiscovery tasks—alongside collection and processing—review has historically been the most resource-intensive stage of the discovery process.
Market Evolution and Relative Task Expenditures
In 2012, review accounted for approximately 73% of total eDiscovery task expenditures, with processing at 19% and collection at just 8%. Fast-forward to 2024, and the cost distribution had shifted: review comprised 64% of expenditures, collection rose to 16%, and processing stabilized at 20%.
Projections for 2029 signal a continued trend of cost redistribution. Review, while remaining the largest task category, is expected to constitute just 52% of total eDiscovery spending—down from 64% in 2024—reflecting the growing adoption of AI-driven efficiencies and strategic investment in upstream tasks such as collection and processing.
Market Opportunity for Review: The Spend
Based on the most recent ComplexDiscovery eDiscovery Market Size Mashup, global spending on review-related software and services was estimated at $10.81 billion in 2024. Despite its declining share, review expenditures are projected to grow to approximately $13.05 billion by 2029.
This modest growth in dollar value, coupled with a shrinking percentage share, highlights the effects of predictive analytics, technology-assisted review (TAR), and emerging Generative AI (GenAI) models in streamlining manual review efforts. These advancements are reshaping resource allocation strategies across eDiscovery workflows.
Review Pricing Data Points: The Cost
The Winter 2025 eDiscovery Pricing Survey, conducted by ComplexDiscovery OÜ and the Electronic Discovery Reference Model (EDRM), offers detailed insight into evolving review cost structures. Respondent data highlights growing divergence in pricing models and methods of delivery—particularly in the face of remote work, specialized review needs, and AI-enhanced services.
Traditional Review Rates
- Remote Review (Attorney-per-hour): A majority of hourly rates clustered between $25 and $40, with some exceeding $40.
- Onsite Review (Attorney-per-hour): Nearly half of reported rates exceeded $40.
- Remote Review (Per-document): Balanced between $0.50 to $1.00 and > $1.00.
- Onsite Review (Per-document): Most responses indicated > $1.00.
GenAI-Assisted Review Rates
Although still in the early adoption stages, GenAI-assisted review pricing models are emerging, albeit with uncertainty:
- Per-document (GenAI): Most frequently cited at $0.26 to $0.50.
- Per-GB pricing: Still largely undefined.
- Outcome-based Models: Few respondents offered defined structures; custom project-specific pricing agreements were the most common.
- Special processing/exception handling: About 50% of respondents were uncertain about how these are priced.
GenAI: A Disruptive Force or Assistive Add-On?
While adoption remains in the early stages, Generative AI is poised to fundamentally reshape the economics and operations of eDiscovery review. More than just an incremental improvement to traditional technology-assisted review (TAR), GenAI introduces capabilities that could compress timelines, reduce manual workloads, and change how service providers and buyers structure review engagements.
Unlike traditional technology-assisted review (TAR), which relies on iterative human-in-the-loop training to learn document relevance, GenAI can classify, summarize, and interpret content with minimal supervision. This zero-shot or few-shot learning capacity introduces significant potential for both cost savings and scalability—particularly for small to mid-sized matters where full-scale TAR deployment may have been cost-prohibitive.
However, the market is still catching up to the technology. According to the Winter 2025 eDiscovery Pricing Survey, nearly one-third of respondents reported no familiarity with or use of GenAI-assisted review. Among those who have begun adopting it, structured pricing models remain rare, with most engagements involving custom agreements or exploratory pilots rather than standardized per-document or per-GB pricing.
Where pricing does exist, most GenAI-assisted document reviews fall between $0.26 and $0.50 per document, offering a glimpse of potential cost efficiencies—but also raising questions about defensibility, validation, and risk. Legal teams remain cautious, particularly around issues like hallucinated outputs, auditability, and privilege errors that may arise from fully automated reviews.
Still, the strategic impact of GenAI is already being felt. Early indicators suggest that GenAI is contributing to the projected shift in how review is resourced within the broader eDiscovery process—even as total spend on review continues to grow. Early adopters who can successfully integrate GenAI into their workflows stand to benefit from increased throughput, more predictable costs, and competitive differentiation.
In this context, GenAI is not merely an assistive add-on. It is a paradigm-shifting catalyst, and its trajectory will shape the future of review in eDiscovery far more than any single pricing model can currently predict.
Key Market Drivers: Technology, Regulation, and Data Complexity
Several structural forces are accelerating the transformation of eDiscovery review, shaping how services are delivered, priced, and consumed.
Regulatory Complexity and Globalization
The continued expansion of global privacy laws—such as GDPR, CCPA, and region-specific mandates—has significantly raised the bar for defensible document review. Organizations are increasingly managing cross-border discovery that requires multi-jurisdictional expertise, language localization, and regionally compliant workflows. This requirement not only introduces legal risk but increases the demand for review teams with both technical agility and subject-matter awareness.
Data Diversity and Volume Growth
The explosion in collaboration tools (e.g., Slack, Microsoft Teams), mobile content, and ephemeral messaging apps has transformed the scope of review. Instead of focusing solely on traditional email and documents, review teams must now interpret complex, contextual, and multimedia-rich datasets. The need to normalize, interpret, and evaluate this data increases the time, cost, and technical demands associated with review.
Operational Pressure for Speed and Accuracy
Corporate legal teams face mounting pressure to resolve legal issues faster and with greater precision. This pressure has catalyzed the adoption of predictive coding, continuous active learning (CAL), and AI-powered prioritization tools. These technologies enable legal professionals to surface the most relevant content early, reduce data volumes requiring human review, and streamline workflows without compromising accuracy.
Delivery Models and Strategic Outsourcing
The expansion of remote and hybrid review models, as reported in the Winter 2025 Pricing Survey, reflects a broader shift toward flexible service delivery in eDiscovery. Legal teams and service providers are increasingly adapting to a distributed workforce by leveraging remote talent while still maintaining the option for onsite engagement when necessary. These evolving models, combined with structured project management support, allow for scalable, cost-conscious review strategies tailored to matter-specific requirements.
Quantifying the Transition: Charts and Benchmarks
To better understand this shift, the following data highlights key trends in task-level expenditures and pricing benchmarks for eDiscovery review services, offering quantifiable insight into how priorities, budgets, and service models are evolving.
Task Expenditures for eDiscovery
eDiscovery Market Overview by Task
Relative Task Expenditures for Core Discovery Tasks
Review Pricing Data Points: The Cost
Review Pricing
What is the per GB cost to conduct predictive coding as part of a technology-assisted review?
What is the cost per hour for document review attorneys to review documents for an onsite managed review?
What is the cost per hour for document review attorneys to review documents for a remotely managed review?
What is the cost per document for document review attorneys to review documents for an onsite managed review?
What is the cost per document for document review attorneys to review documents for a remote managed review?
Review Pricing – Generative AI
What is your primary model for GenAI-assisted review in eDiscovery?
If using a per-document model, what is the average cost per document for GenAI-assisted review?
If using a per GB model, what is the average cost range for GenAI-assisted review?
If using an outcome-based pricing model, how is the cost typically structured?
How do pricing models typically account for documents that fail to process or require special handling in GenAI-assisted review?
Charting a Course Toward Smarter Review
As the review task continues to evolve, legal teams, service providers, and technology developers must remain agile in adapting to market demands and cost pressures. While review will likely remain the largest area of eDiscovery spending through 2029, its declining proportional share signals a broader industry transformation.
Investment in GenAI and TAR technologies offers promising returns—especially when paired with strategic review planning and defensible cost modeling. The future of review lies not in how many documents are reviewed but in how efficiently relevance and risk are identified at scale.
Assisted by GAI and LLM Technologies
Source: HaystackID published with permission from ComplexDiscovery OÜ
