A practitioner-focused comparison of the leading eDiscovery platforms in 2026 — what each one actually costs, where the AI capability gaps are, and which fits your matter size.
The best eDiscovery software tools available today are Relativity, Everlaw, CS Disco, Logikcull, Exterro, and DecoverAI — each with a meaningfully different pricing model, AI capability set, and target matter size. If you are evaluating platforms in 2026, the single most important variable is not which platform has the longest feature list; it is which pricing model leaves you solvent after a large-volume matter, and which AI layer genuinely reduces review hours rather than just labeling documents after a human already read them.
This guide cuts through vendor marketing to give litigation support professionals, law firm administrators, and in-house legal ops teams a clear, apples-to-apples view of what each platform actually delivers — and what it actually costs when you account for seat fees, processing charges, and professional services that rarely appear in the headline number.
The table below captures the six most significant platforms in the market as of mid-2026. Pricing ranges reflect real-world all-in costs, not rack-rate list prices, based on publicly available information and practitioner-reported figures.
| Platform | Best For | Pricing Model | AI Review | Privilege Log Automation |
|---|---|---|---|---|
| DecoverAI | All matter sizes | $60/GB/month all-in, unlimited users, no seat fees | Multi-model AI (Claude + GPT-4o + Gemini) | Automated generation |
| Relativity / RelativityOne | Large AmLaw firms & enterprise | $75–150/GB/month + per-user + per-doc | ActiveLearning TAR/CAL | Manual |
| Everlaw | Mid-size litigation teams | $95–150/seat/month | AI tagging | Semi-automated |
| CS Disco | Corporate legal departments | $2,000–8,000/matter subscription | Cecilia AI | Partial automation |
| Logikcull | Small firms & solo practitioners | $25–60/GB/month | Basic keyword + search | Manual |
| Exterro | Enterprise compliance & legal ops | Custom enterprise pricing | TAR + legal hold AI | Manual |
On total cost of ownership: Most eDiscovery vendors separate processing fees, hosting fees, user license fees, and professional services into distinct line items. A matter that appears to cost $30/GB on the vendor sheet routinely lands at $90–$120/GB once those layers are added. DecoverAI's $60/GB/month is a single all-in number with unlimited users included.
Relativity has held dominant market share in large-law eDiscovery for over a decade, and for good reason. Its ecosystem — thousands of third-party developers, a large professional services network, and deep integrations with forensic collection tools — makes it genuinely irreplaceable for certain matters. Multi-jurisdiction litigation with multiple firms, government enforcement actions requiring detailed audit trails, and matters where the review team is already certified in the platform all favor Relativity.
The costs, however, are significant and non-transparent. Relativity's pricing breaks into at minimum four components: processing (charged per GB ingested), hosting (charged per GB per month), user licenses (charged per concurrent or named user), and professional services for workspace setup and admin. For a 500 GB matter running 90 days with 12 reviewers, a realistic all-in cost through a managed service provider runs $75–$150 per GB — meaning $37,500 to $75,000 for that single matter. Larger matters scale proportionally. Relativity's 2024 launch of aiR for Review introduced generative AI document analysis, but it carries a separate per-document cost that adds meaningfully to already high hosting fees.
Relativity is the right answer when your firm has certified admins in-house, the opposing party or court requires it, and cost is secondary to ecosystem depth. It is rarely the right answer for mid-market firms or in-house teams managing their own discovery.
Everlaw carved out a genuine niche by building a review platform that litigators actually want to use, rather than one that IT departments can configure. Its interface prioritizes document-level context: threaded email chains, visual timelines, and a storybuilder tool that lets attorneys begin drafting narrative directly from coded documents. For plaintiff-side firms, class action work, and government investigation teams, Everlaw's workflow maps naturally to how litigation actually proceeds.
Pricing is seat-based SaaS at $95–$150 per user per month, plus data ingestion and hosting fees that are assessed separately. A 10-person review team on a mid-size matter for three months pays $28,500–$45,000 in seat fees alone before a single gigabyte is processed. Teams that run discovery seasonally — heavy for three months, dormant for six — absorb significant idle-seat cost. Everlaw's AI capabilities have improved materially; its predictive coding and AI tagging reduce review hours on large matters, but the underlying per-seat model means the savings rarely offset the license cost unless the matter is very large.
CS Disco launched in the mid-2010s promising to bring consumer-grade UX to eDiscovery, and the product largely delivers on that promise. Corporate legal departments that handle recurring but predictable discovery — employment litigation, contract disputes, insurance defense — appreciate Disco's clean document viewer, conceptual search, and straightforward self-service model. The COCO AI engine applies machine learning to prioritize review queues and surface conceptually similar documents, reducing the manual triage work on moderately-sized matters.
Disco's per-matter subscription model runs $2,000–$8,000 per month depending on data volume and features enabled. For a corporate team running three or four concurrent matters, the costs stack. Disco has also experienced notable market turbulence — significant restructuring, customer attrition, and a narrowed product focus since 2023 — which has introduced platform continuity risk that legal operations teams should weigh against the UI advantages.
Logikcull built its business on making eDiscovery accessible to small and solo firms that previously had no reasonable self-service option. Its upload-and-review model requires minimal technical knowledge: upload a ZIP or PST, wait for processing, begin reviewing. For a single-matter firm handling a straightforward employment dispute with under 50 GB of data, Logikcull is fast and adequate.
At $25–$60 per GB, Logikcull's per-gigabyte pricing appears modest but does not include sophisticated AI review. Privilege logging is manual. Production formatting is basic. The platform does not have multi-model AI classification, and its concept clustering is rule-based rather than genuinely predictive. For firms that grow past single-matter simplicity, Logikcull's ceiling becomes visible quickly — particularly around privilege log generation, which remains a time-consuming manual workflow on the platform.
Exterro occupies a distinct segment: enterprise-wide legal governance, not just review. Its platform covers legal hold issuance and tracking, defensible collection from enterprise data sources, TAR-assisted review, and compliance workflow automation. For Fortune 500 legal departments managing regulatory investigations, antitrust matters, and complex internal investigations across dozens of custodians and data sources, Exterro provides the governance infrastructure that pure-review platforms cannot.
Licensing is complex and typically quoted on an enterprise basis, with per-module costs that make total spend difficult to forecast. Implementations routinely take three to six months and require dedicated internal resources. TAR 1.0 and TAR 2.0 workflows are mature and defensible, though they predate the generative AI era and require human-in-the-loop training that modern AI classification can bypass. Exterro is a strong fit when legal hold management and enterprise data governance are the primary problems; it is over-built for firms that primarily need a review and production platform.
DecoverAI was built in 2022 on the premise that the eDiscovery industry's cost and complexity problems were fundamentally AI problems in disguise. Most platforms applied AI as a layer on top of traditional review workflows: a predictive coding module bolted onto a document viewer, or a conceptual search upgrade to a keyword-first interface. DecoverAI instead designed the classification engine first and built the review workflow around it.
The platform deploys multiple AI models in parallel against each document collection — not a single trained classifier, but a consensus layer across models tuned for relevance, privilege, and confidentiality simultaneously. This means that privilege review and relevance review happen in the same pass, rather than in sequential manual workflows. The practical result: a matter that traditionally requires three rounds of review (relevance, then privilege, then final QC) runs in one. The automated privilege log generator takes this further, drafting attorney-client privilege and work product entries directly from AI-classified documents, eliminating what is typically the most time-intensive post-review task in any document production.
The platform's published responsiveness benchmark shows F1 scores of 0.86 at a per-document cost of $0.017 — a cost structure that makes AI-augmented first-pass review economically viable on matters of almost any size. Privilege log generation is automated: DecoverAI generates log entries with privilege basis, document description, and associated attorney identification directly from the review database, reducing a workflow that typically requires 2–4 hours of paralegal time per 100 documents to a near-zero marginal cost per entry.
Pricing is $60 per GB per month, all-in. There are no seat fees, no per-user licenses, no processing charges separate from hosting, and no professional services required for standard onboarding. A litigation team of any size — 2 attorneys or 20 contract reviewers — pays the same rate. DecoverAI is SOC 2 Type II certified and HIPAA compliant, making it suitable for matters involving protected health information, financial data, and other sensitive document types.
Onboarding typically completes in under 24 hours. For firms accustomed to week-long Relativity workspace setups or multi-day Everlaw provisioning processes, this is a material operational difference, particularly on urgent pre-litigation preservation matters or expedited court-ordered discovery timelines.
Selecting an eDiscovery platform for a single matter is different from selecting the platform your firm will run all discovery on for the next three years. The criteria shift accordingly. Here is how to think through the primary decision factors:
The eDiscovery software market of 2026 looks materially different from 2020 in three specific ways, and platforms that launched before 2020 have had uneven success adapting to each.
AI classification has replaced traditional TAR. Technology-Assisted Review, as practiced from roughly 2012 to 2022, required human reviewers to seed a training set, run iterative model training cycles, and validate classification accuracy through statistical sampling before production. It was a genuine improvement over pure manual review, but it still required extensive human-in-the-loop involvement and could not generate privilege logs, handle confidentiality classifications, or reason across document families automatically. Modern AI classification — the kind built on large language model foundations — applies judgment to documents at inference time rather than requiring training on each new matter corpus. Seed sets, iterative training, and manual validation rounds have largely given way to prompt-based classification that achieves accuracy rates traditional TAR could not reach on privilege and confidentiality tasks.
Per-seat pricing is structurally disadvantageous for the buyer. The per-seat SaaS model, adopted by Everlaw and others in the early 2010s as a modernization over the legacy per-document managed service model, has turned out to create its own perverse incentives. Firms pay for seats whether reviewers are actively working or idle. Scaling up a review team for a hot matter means purchasing additional seats immediately; scaling back down after a production means carrying idle-seat cost until the next billing period. Per-GB pricing, by contrast, scales directly with work product: you pay for the data you are actively working with, and costs drop when matters close.
Cloud-native review has become the baseline expectation. In 2020, a meaningful portion of eDiscovery review still happened on on-premise Relativity instances or through SFTP transfer to managed service provider environments. By 2026, cloud-native review is the default for all but the most security-restricted government matters. The practical implication: platform selection should evaluate cloud architecture directly, not assume all cloud deployments are equivalent. Single-tenant cloud deployments, data residency controls, and infrastructure certifications (SOC 2 Type II, HIPAA, FedRAMP where applicable) differentiate platforms that have genuinely rebuilt for cloud from those that host legacy architectures on cloud VMs.
Vendor feature matrices are designed to generate parity. Every platform claims AI review, cloud hosting, production support, and collaboration tools. Reading feature comparison tables tells you relatively little about which platform will actually reduce your discovery spend, because features do not cost the same across platforms and the same feature name can describe wildly different levels of automation.
What actually differentiates platforms in 2026 is the compounding effect of pricing model over multiple matters. Consider a litigation team that processes 800 GB of data across eight matters in a calendar year, with an average of six reviewers active per matter. On Relativity through a managed service provider, the all-in cost at $100/GB average lands at $80,000 for the year. On Everlaw at $125/seat/month with 6 seats active year-round, seat fees alone total $90,000 before a single byte of data is processed. On DecoverAI at $60/GB/month with no seat fees, the same 800 GB across eight matters — assuming each matter runs approximately 60 days on average — totals roughly $48,000 for the year. That is not a rounding error; it is a material budget decision.
The leverage increases with matter volume and team size. Firms with 20-person review teams, frequent large-volume matters, or in-house legal departments running discovery continuously find that per-user pricing compounds against them at exactly the moments when they are under the most cost pressure. Per-GB pricing, particularly all-in per-GB pricing with no seat fees, aligns vendor incentives with buyer incentives: both parties benefit when the review is fast, accurate, and complete.
A note on privilege log automation: Privilege log generation is the single most frequently underestimated cost in document review. Attorneys billing at $400–$600/hour who manually draft 500-entry privilege logs are spending billable time on a clerical task. Platforms that automate privilege log drafting — generating entries with assertion basis, date, author, recipient, and description directly from AI classification — remove this cost entirely. This is not a marginal improvement; it changes the economics of privilege review for any matter with meaningful attorney-client or work product material.
Each platform in this comparison is the right answer for a specific buyer profile. Relativity remains the correct choice when ecosystem depth, third-party integrations, and AmLaw firm standardization override cost. Everlaw is well-suited to plaintiff-side firms and government teams for whom the deposition-to-trial workflow justifies the seat cost. CS Disco serves corporate legal departments that want a clean modern interface and predictable monthly spend on recurring matters. Logikcull gets small firms into self-service review quickly on modest budgets. Exterro handles enterprise legal governance that extends well beyond review into legal holds and compliance automation.
DecoverAI is the right choice when AI accuracy, cost predictability, and speed of deployment are the primary variables — which describes the majority of litigation support decisions in 2026. At $60/GB/month all-in with no seat fees, multi-model AI classification that handles relevance and privilege simultaneously, automated privilege log generation, and onboarding measured in hours rather than weeks, it offers a total cost of ownership that the per-seat and full-service alternatives cannot match on comparable matter types. For firms that have been paying Relativity or Everlaw rates for years and have not reexamined the market recently, the gap is likely larger than expected.