How Much Does eDiscovery Software Cost in 2026?

What Does eDiscovery Software Cost in 2026?

eDiscovery software costs in 2026 range from $3 to $30+ per gigabyte for volume-based platforms, $150 to $250 per user per month for per-seat models, or $499 to $999+ per month for all-inclusive AI-native platforms.

The total cost of ownership depends on the pricing model, data volume, user count, and whether the vendor charges hidden fees for processing, hosting, or AI inference. If you are evaluating new eDiscovery platforms, the sticker price rarely tells the full story.

Research from the RAND Institute on litigation costs has shown that discovery -- particularly document review -- accounts for the majority of civil litigation expenses.

The eDiscovery market has changed. Legacy platforms built in the 2010s still dominate market share, but their pricing structures were designed for keyword search and manual review. AI-native platforms run on different economics: the AI does the heavy lifting, costs shift from labor hours to compute cycles, and firms can analyze ten times the documents at a fraction of the price.

How Does Per-GB eDiscovery Pricing Work?

Per-gigabyte pricing is the most common model among legacy eDiscovery platforms. Vendors charge a rate for each gigabyte of data processed, hosted, and reviewed.

Typical rates in 2026 range from $3 to $10 per GB for processing, $5 to $15 per GB for hosting (charged monthly), and $15 to $30 per GB for review and production. A mid-size litigation matter with 50 GB of data can cost $7,500 to $25,000 per month in hosting fees alone -- before anyone reviews a single document.

The per-GB model creates a perverse incentive: the more data your case involves, the more you pay, regardless of how much of that data is actually relevant. Firms that need to cast a wide collection net -- common in regulatory investigations and multi-party disputes -- face costs that can exceed six figures per month.

Platforms like Relativity and its hosted service RelativityOne are heavily associated with this model. Costs scale linearly with data volume, and additional charges apply for analytics features.

What Are the Pros and Cons of Per-Seat eDiscovery Pricing?

Per-seat pricing charges a fixed monthly rate for each user who accesses the platform. Costs are tied to headcount rather than data volume, which makes budgeting easier. Typical rates fall between $150 and $250 per user per month, though enterprise tiers with advanced analytics can exceed $400 per user. Platforms like Everlaw use variations of this approach.

The downside is that per-seat models penalize collaboration. Adding a partner, a paralegal, or co-counsel to the platform means another license fee. In multi-party litigation where six or more attorneys need access, per-seat costs can reach $1,500 or more per month before data processing fees.

Many per-seat vendors also charge separate fees for processing, ingestion, or production. The per-user price is rarely the complete cost.

What Hidden Fees Should You Watch For?

Hidden fees in eDiscovery platforms can double or triple the advertised price. The most common ones are processing fees (charged to de-duplicate, de-NIST, and index incoming data), hosting fees (monthly charges for storing data on the platform), production fees (charges per page or per document for producing files), and overage penalties for exceeding storage or processing limits.

• Processing surcharges: Some vendors charge $3 to $8 per GB just to ingest and index data, separate from the monthly hosting fee
• Analytics add-ons: Advanced features like TAR (Technology Assisted Review), clustering, or email threading are often sold as premium modules at $5 to $15 per GB
• Export and production fees: Charges of $0.02 to $0.10 per page for producing documents can add thousands of dollars on large cases
• Training and onboarding: Enterprise platforms may charge $5,000 to $20,000 for initial setup and training
• Minimum commitments: Multi-year contracts with minimum spend requirements that lock firms into paying for capacity they may not use

These fees add up fast. A firm budgeting $5,000 per month for an eDiscovery platform may find their actual spend is $12,000 to $18,000 after processing, hosting, analytics add-ons, and production fees.

Always request a total cost of ownership estimate based on your specific case parameters before committing to a platform.

How Do AI Inference Costs Affect eDiscovery Pricing?

AI inference costs are a newer pricing component from AI-native eDiscovery platforms. AI inference is the compute cost of running AI models to analyze, classify, and extract information from documents.

Unlike traditional per-GB charges, inference costs are tied to how much analytical work the AI performs -- not how much data you store. You pay for work done, not disk space occupied.

The key differentiator among AI-native platforms is how they mark up inference costs. Some vendors bundle inference into opaque per-document or per-page fees, making it impossible to know the actual compute cost. Others apply markups of 50% to 200% on top of base inference costs.

DiscoverLex charges AI inference at cost plus 10%, with full transparency into actual compute costs and detailed usage reports. Firms never subsidize hidden margins on AI processing.

eDiscovery Pricing Models Compared

What Is the Total Cost of Manual Review vs. an AI Platform?

The total cost gap between manual review and an AI platform is wide. Manual review for a mid-size commercial dispute with 500,000 documents typically requires 200+ associate hours per month at $300 to $600 per hour. That translates to $60,000 to $120,000 in monthly labor costs alone.

Add contract attorney fees, quality control overhead, and the eDiscovery hosting platform, and total monthly spend can exceed $150,000.

By contrast, an AI-native platform like DiscoverLex handles the same document set for a flat monthly subscription plus transparent AI inference costs. A firm on the Professional plan at $999 per month with typical inference usage for 500,000 documents will spend a fraction of the manual review cost.

That price includes faster results, citation trails, contradiction detection, and relationship mapping. The AI vs manual review comparison shows that AI-assisted workflows consistently deliver 10x to 20x ROI compared to fully manual approaches.

The economics get better for ongoing matters. Manual review costs recur every month regardless of how much new data arrives. AI-powered platforms incur inference costs only when new analysis is performed, so re-querying existing data costs almost nothing.

Teams that run repeated searches, generate multiple work products, or need to respond quickly to new discovery requests see the biggest gains from the AI model.

How Does DiscoverLex Pricing Work?

DiscoverLex uses a transparent flat-rate pricing model with three tiers designed for different firm sizes. Every plan includes unlimited AI-powered search, entity relationship mapping, and full citation trails. AI inference -- the compute cost of running document analysis -- is billed at cost plus 10% with detailed usage reports so firms know exactly what they are paying for.

• Essential ($499/month): Up to 5 lawyer seats, unlimited search, entity mapping, basic pattern detection, and email support. Designed for small firms getting started with AI-powered analysis.
• Professional ($999/month): Up to 15 lawyer seats with everything in Essential plus advanced deep-dive QA, contradiction detection, custom alert rules, and priority support. The most popular tier for growing litigation practices.
• Enterprise (custom pricing): Unlimited seats with dedicated account management, custom integrations, on-premise deployment options, and AI inference at cost plus 10%. For firms with 15 or more lawyers.

Document ingestion -- the one-time cost of processing, OCR, and indexing your document set -- is priced separately based on volume: $2,500 for up to 50,000 pages, $3,500 for up to 100,000 pages, and $4,500 for up to 250,000 pages. This one-time fee covers production-grade multi-engine OCR processing, metadata extraction, and full indexing. Visit the pricing page for full details and a cost calculator.

How Should Firms Evaluate eDiscovery Costs?

Evaluating eDiscovery costs means looking beyond the advertised price to calculate true total cost of ownership. Gartner's legal technology research recommends requesting detailed pricing breakdowns that include processing, hosting, analytics, production, and training fees.

Build a side-by-side comparison across at least three vendors using the same case parameters -- document volume, user count, production volume, and duration. That reveals the actual cost differences that marketing pages obscure.

Key Questions to Ask Every Vendor

1. What is the all-in monthly cost for a case with 100 GB of data and 10 users?
2. Are analytics features (TAR, clustering, AI search) included or charged separately?
3. What are the processing and ingestion fees for new data?
4. Is there a minimum contract commitment or early termination fee?
5. How are AI inference or compute costs billed, and what markup is applied?
6. What are the production and export fees per page or per document?
7. Can the platform scale to handle 10x the initial data volume without renegotiating the contract?

The firms that negotiate the best eDiscovery contracts understand all the cost components and compare them apples-to-apples. A platform that looks more expensive per-seat may deliver lower total cost of ownership when AI features eliminate the need for contract attorneys and reduce review time by 90%.

Compare DiscoverLex vs Relativity and DiscoverLex vs Everlaw for detailed platform-level comparisons.

The Bottom Line on eDiscovery Costs in 2026

eDiscovery pricing in 2026 is shifting. Legacy per-GB and per-seat models still dominate, but AI-native platforms are showing that flat-rate pricing with transparent inference costs delivers better value.

Firms that audit their current eDiscovery spend -- accounting for every hidden fee, add-on, and overage charge -- consistently find that switching to an AI-first platform reduces total cost of ownership while improving speed and accuracy.

The most important step is to calculate your current true cost -- not just your platform subscription. Include associate time, contract attorney fees, quality control overhead, and the opportunity cost of slow turnaround.

That total figure is what AI-native eDiscovery replaces. For most litigation practices, the math is not close. Request a free demo to see how the numbers work for your specific caseload.