Relativity aiR vs Everlaw vs Disco: which AI e-discovery platform actually fits your matter in 2026

**Relativity aiR** is not a separate product — it is a generative AI layer that runs on top of RelativityOne, the cloud version of the document review platform that has been the de facto standard in large-matter e-discovery for fifteen years. The aiR family in 2026 includes aiR for Review (first-pass responsiveness and issue coding), aiR for Privilege (privilege identification with reasoning), and aiR for Case Strategy (matter-level insight generation). Critically, aiR sits on top of the existing per-GB pricing stack — you still pay to host, still pay to process, and now you pay again to let the AI look at the documents. Source: https://www.relativity.com/data-solutions/air/.

**Everlaw** built its AI features natively into the platform rather than charging separately, which is the most consequential pricing decision in this category. EverlawAI Assistant — launched in 2023, materially upgraded through 2025 — handles document summarization, deposition outline generation, document-level Q&A, and a writing-review feature that flags weak passages in motions. Because seats are the unit of pricing, you can run AI across an unlimited document volume without watching a meter spin. The tradeoff: seats are not cheap, and Everlaw caps your scaling differently than the per-GB vendors. Source: https://www.everlaw.com/pricing/.

**Disco** rebuilt its review platform on a modern stack in 2018-2020 and added Cecilia AI as a first-class feature in 2023, with substantial generative additions in 2024-2025. Cecilia handles prompt-based review (write a natural-language prompt, code the entire population), document summaries, and timeline extraction. The platform sells per-matter, which is a category-defining choice — if your firm runs 30 matters a year of similar size, your costs are highly predictable. Source: https://www.csdisco.com/pricing.

The three platforms converge on capability — by mid-2026, all three can do generative responsiveness review, generative privilege, document summaries, and depo prep — but diverge sharply on how they charge and where they win. **Relativity aiR** wins when the matter is huge, the data is messy, and the litigation strategy demands every tool the ecosystem can offer. **Everlaw** wins when you have a stable team and want to stop budgeting by gigabyte. **Disco** wins when you run a steady book of mid-market matters and want a fixed per-matter line item.

A note on terminology: 'AI features' in 2024 mostly meant predictive coding (TAR 2.0) and CAL workflows. In 2026, the entire category has moved to GPT-4-class generative models with retrieval-augmented grounding. All three vendors run their AI in tenant-isolated environments with vendor-attested no-training agreements — none of them train on customer data. Verify that contractually before signing. The capability gap between the three on generative review accuracy is small and closing.

**Relativity aiR** lives inside RelativityOne, which runs on Microsoft Azure with regional data residency in the US, EU, UK, Canada, Germany, Australia, and select APAC regions (https://www.relativity.com/data-solutions/relativityone/). The integration story is unmatched in the category: hundreds of partner apps in the Relativity App Hub for everything from short message collection (Slack, Teams, mobile) to translation, transcription, and production. If your matter touches a niche data type, there is probably an app for it. aiR itself uses Azure OpenAI Service with prompt and response logging inside the workspace, which is what enterprise security teams want to see.

**Everlaw** runs on Google Cloud Platform and integrates natively with Microsoft 365, Google Workspace, Slack, Teams, Box, and Zoom — including a depo product that records, transcribes, and links to documents in real time (https://www.everlaw.com/product/depositions/). The architecture choice (GCP) is meaningful for in-house teams already on Google. Everlaw also pushed early into mobile collection and short-message data and has one of the more usable Slack/Teams review experiences in 2026. The platform is fully SaaS — no on-prem option, ever — which is non-negotiable for firms with regulatory or sovereignty constraints.

**Disco** runs on AWS with a self-built review stack designed for speed — it consistently benchmarks faster on ingest and search than the competition, especially on large unstructured datasets. Native integrations cover Microsoft 365, Box, Slack, and mobile/short-message collection, and Disco's Cecilia AI is built directly into the review interface rather than as a separate tool you switch to. The workflow difference is real: in Disco, you write a prompt in the search bar and the AI codes the result; in Relativity aiR, you submit a job and wait for results to populate a separate review queue.

The architectural decision that matters most to in-house legal and outside counsel CTOs in 2026 is what happens to your data after the matter closes. **Relativity** lets you export to native format and walk away — the data was always yours and lives in your tenant. **Everlaw** offers full export but the platform-native productions are tied to seat licenses, so you need to budget for either continued seat coverage or a one-time export workflow. **Disco** offers matter archival pricing that is materially cheaper than active hosting, which is the right answer for matters that close but might reopen.

Integration depth matters more than feature count. **Relativity** wins on breadth — if a niche tool exists in legal tech, there is probably a Relativity integration. **Everlaw** and **Disco** win on integration depth with the core M365 stack and on usability of the integrations they do have. Pick on what you actually use, not on what the integration grid shows.

Let us model a realistic matter: 2 TB of source data, 1.4 TB after deNIST and dedupe, six attorneys reviewing for six months. The pricing model differences are not subtle. **Relativity aiR** (https://www.relativity.com/data-solutions/air/) at typical partner-quoted rates lands at approximately $100/GB processing one-time ($140K), $15/GB/month hosting for six months ($126K), and $50/GB aiR for Review one-time ($70K) — call it ~$336K for the matter on the platform alone, before partner consulting fees. That is consistent with what we hear from AmLaw firms running similar matters in 2026, and it is sourced from vendor pricing pages, June 2026.

**Everlaw** (https://www.everlaw.com/pricing/) at six seats × ~$9K/year (mid-tier with AI) = $54K annualized. If the matter runs six months, you are paying $27K for the seat coverage during the active matter, with unlimited data volume included. That is more than a 10x difference vs the Relativity stack on this hypothetical. The catch: those seats also cover all your other matters running concurrently, so the per-matter math gets murky when you allocate. The clean comparison is: Everlaw rewards firms that run many matters per seat per year.

**Disco** (https://www.csdisco.com/pricing) at ~$20/GB on 1.4 TB processed ($28K) plus a platform fee of roughly $5-15K/month for six months ($30-90K) lands at ~$58-118K depending on platform tier and hosting volume. Cecilia AI is included. Disco's pricing intentionally lands between the two extremes — more predictable than Relativity, more matter-scaled than Everlaw. The model rewards firms that want a clean per-matter line item to bill back to clients without explaining seat allocations.

The math flips at scale. On a 20 TB regulatory matter with 30+ reviewers, **Everlaw**'s seat model would land at ~$180K-270K for thirty seats, but the unlimited data volume becomes overwhelming value. **Relativity aiR** on that same matter could exceed $1M in platform fees alone. **Disco** would land in the $500-700K range. On a 200 GB plaintiff matter with two attorneys, **Everlaw** is overkill (you still pay for seats), **Disco** lands at ~$15-25K total, and **Relativity** at ~$25-40K — making Disco the obvious budget pick for small matters.

One critical pricing nuance: all three vendors offer enterprise master agreements that flatten the per-matter math, and all three negotiate. The published per-GB rates above are starting points — firms that commit to multi-year contracts or volume floors typically land 30-50% below list. As of June 2026 — verify at relativity.com/pricing, everlaw.com/pricing, and csdisco.com/pricing — because all three vendors have raised list prices twice in the past 18 months and the negotiation room is narrowing as AI demand consolidates onto these three platforms.

**Pick Relativity (with aiR)** if you are an AmLaw 100 firm or Fortune 500 in-house legal team running multi-TB matters with multiple data sources, complex production workflows, and a partner ecosystem that already knows Relativity. The platform's ceiling is the highest in the category — it can handle 50+ TB matters, dozens of custodians, and the most exotic production requirements without breaking. The cost is real, but on bet-the-company litigation, paying for the platform that everyone else is also using means your discovery vendor and your opposing counsel speak the same language. Source: https://www.relativity.com/customers/.

**Pick Everlaw** if you are a plaintiff firm, an investigations boutique, or a corporate legal department where matter volume is high but per-matter data size is moderate (sub-2TB typical). The seat-based pricing means you can run unlimited matters concurrently without budget surprises, which is the right model when you do not know in advance which case is going to explode. Everlaw's depo product and writing-review tool are genuinely differentiated — if your team spends serious hours on depositions or motion practice, those bundled features replace separate point tools. Source: https://www.everlaw.com/customers/.

**Pick Disco** if you are a mid-market litigation firm with a steady book of similar matters and you want clean per-matter pricing you can bill back to clients without explaining a SaaS seat model. Disco's per-matter line item shows up on a client invoice the way clients expect — a number per case — and the platform speed advantage on ingest is meaningful when you are running a high cadence of mid-size matters. The Cecilia AI prompt-based workflow is the most accessible AI experience of the three for non-power-users. Source: https://www.csdisco.com/customers.

The wrong-fit signal is loud once you know what to look for. Running **Relativity aiR** on a 100GB matter is paying for capability you do not need and will not use. Running **Everlaw** on a 50 TB regulatory production is leaving money on the table because seat pricing does not scale down per-document. Running **Disco** on a multi-party class action with 40 custodians and exotic data types is asking the platform to do something it was not architected for. Match the platform to the matter shape, not to the brand familiarity.

A category note: in-house legal teams are increasingly going dual-platform — one of the three above for active litigation, plus a separate AI contract review tool (Harvey, Spellbook, Robin) for transactional work. The cost stacks are different, and the workflows are different. Do not assume your e-discovery platform should also handle your contract review queue.

All three platforms hit the baseline: SOC 2 Type II, ISO 27001, HIPAA, GDPR-ready data processing agreements, and tenant isolation. **Relativity** (https://www.relativity.com/trust/) additionally carries FedRAMP Moderate authorization for RelativityOne and is the only platform of the three with a US Government Community Cloud offering — non-negotiable for federal agencies and DOJ-facing matters. If your firm represents government clients or works on national security-adjacent matters, this single fact may end the comparison.

**Everlaw** (https://www.everlaw.com/security/) runs on Google Cloud with SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliance, with EU and UK data residency. Everlaw's security posture is strong and the audit trail and chain-of-custody tooling is well-regarded. There is no FedRAMP authorization, which matters if your matter touches federal data. The platform's no-training AI agreement is contractually explicit and covers all EverlawAI features — verify the current contract language before signing.

**Disco** (https://www.csdisco.com/security) runs on AWS with SOC 2 Type II, ISO 27001, HIPAA, and GDPR compliance. Data residency covers US, EU, UK, and Canada. Disco's Cecilia AI runs in tenant-isolated environments with no-training guarantees, and the platform offers customer-managed encryption keys on enterprise tiers. The security story is solid but, like Everlaw, lacks the federal certifications that Relativity carries.

Data residency is where the platforms actually diverge in 2026. **Relativity** offers the broadest regional footprint — Germany sovereignty, Australia sovereignty, multiple APAC regions — which matters if you are a multinational corporate legal team operating under data localization rules. **Everlaw** and **Disco** cover the major Western jurisdictions but lack the breadth in EMEA and APAC. If you are running cross-border matters with mandatory in-country processing, this is a screening criterion, not a nice-to-have.

One under-discussed issue: AI model governance. All three vendors will tell you their AI does not train on your data. What they will not always tell you upfront is which model they use (OpenAI GPT-4 class, Anthropic Claude, or their own fine-tunes), where the inference runs (the same region as your data, or a different one), and what the prompt-and-response retention policy is for AI features. Get those answers in writing during procurement — they will matter in a year when AI provenance regulations land.

**Relativity aiR** implementation runs through a partner — Lighthouse, Consilio, KLDiscovery, Epiq, and dozens of smaller shops. Direct procurement from Relativity is unusual; the partner model is how almost every firm buys. That adds 15-30% to landed cost vs raw platform fees and adds 4-8 weeks to setup for a complex matter, but partners bring matter-specific workflows, search templates, and reviewer training that meaningfully accelerate first-pass review. If you are new to Relativity, do not try to self-implement. Source: https://www.relativity.com/partners/.

**Everlaw** sells direct and implementation is typically 1-2 weeks for a standard matter — load data, set up users, configure review batches. The platform is materially easier to learn than Relativity for first-time users; junior associates are productive in days, not weeks. Everlaw's customer success team is included in seat pricing and is genuinely available — that is not always the case in this category. The cost of implementation effectively rounds to zero on the published seat pricing, which is one of the platform's quiet advantages.

**Disco** also sells direct and implementation runs 1-3 weeks depending on matter complexity. Disco's reviewer interface is the most modern of the three — clean, fast, and intuitive — and the platform's prompt-based AI workflow does not require the same level of training as Relativity's job-based aiR workflow. Disco's customer success is solid but thinner than Everlaw's, which matters if you are a firm without a dedicated litigation support team.

Time-to-first-production-set is the metric that actually matters and the three platforms diverge here. On a clean 500GB matter, **Everlaw** and **Disco** both get to a first-pass-coded review set in 2-4 days with AI assistance. **Relativity aiR** on the same matter, with a partner doing implementation, is typically 5-10 days because of the additional configuration and workflow setup. That gap compresses on larger matters where the configuration overhead becomes a smaller percentage of total project time.

Training cost is real and frequently underbudgeted. **Relativity** requires certified users for advanced workflows — RCA, RCAC certifications run hundreds of dollars per person and take weeks. **Everlaw** and **Disco** both rely on platform usability rather than certification, which is cheaper but means you have less formal validation of reviewer skill. If your matter is high-stakes and your reviewers are contract attorneys, the certification model has real value — you know what skills you are getting.

On generative responsiveness review accuracy in mid-2026, the three platforms are roughly comparable on standard benchmarks (~85-92% recall at meaningful precision on Enron-style test sets), but their workflow differences matter more than the headline numbers. **Relativity aiR** uses a job-based workflow where you configure a prompt and review criteria, submit, and review the results in a separate queue — auditable, repeatable, and well-suited to large matters with multiple reviewers and validation cycles. The downside: the loop time between prompt iteration and results is longer than the interactive alternatives.

**Everlaw** EverlawAI runs interactively — you can ask a question of the document corpus and get an answer with citations in seconds, which is the right workflow for case investigation and depo prep. EverlawAI is particularly strong on document-level Q&A and summary generation, and the writing-review feature is the only one of its kind in the category that meaningfully helps with motion drafting. The weakness: at very large scale (10TB+), the interactive model produces less rigorous audit trails than Relativity's job-based approach.

**Disco** Cecilia AI is the most accessible — write a prompt in the search bar, get coded documents back, iterate. The workflow is closer to how users actually think about review than the alternatives, and for mid-size matters Cecilia produces results comparable to aiR with less configuration overhead. Cecilia's timeline generation feature, which extracts dated events across the document set into a navigable chronology, is genuinely differentiated and useful in narrative-heavy litigation.

On privilege review specifically, **Relativity aiR for Privilege** is the most mature offering with explicit reasoning outputs that map to traditional privilege log requirements. **Everlaw** and **Disco** both handle privilege identification well but produce less court-defensible reasoning chains as of June 2026. If your matter is going to involve extensive privilege log practice, this is a real differentiator for Relativity. Source: https://www.relativity.com/data-solutions/air/.

The honest take on AI quality: all three platforms are good enough that the choice should be made on pricing model, workflow fit, and integration depth, not on a 2-3% accuracy delta. If a vendor is pitching you on AI accuracy as the primary differentiator in 2026, push them on the workflow questions instead — that is where the day-to-day reviewer experience actually diverges.

On **Relativity**, watch out for partner markups, data egress fees, and processing rate creep. Partner-quoted per-GB rates often bundle platform fees with partner consulting in a way that obscures the platform cost. Ask for the platform-only rate explicitly and negotiate partner consulting separately. Data egress at matter close is sometimes free, sometimes charged — get it in writing. And processing rates are quoted as a range — push for the bottom of the range or a volume tier that drops you into it. Source: https://www.relativity.com/pricing/.

On **Everlaw**, the gotcha is seat overage. Seats are typically a hard count and adding mid-year is sold at non-discounted prorate, which means urgent reviewer adds at the start of an unexpected matter are expensive. Negotiate a buffer pool or burst-seat pricing into the master agreement. The other watch-out: storage overage — the seat-based model includes data volume, but extreme volume (50TB+) may trigger overage discussions. Get the volume ceiling defined in writing.

On **Disco**, the gotcha is matter close-out. The per-matter pricing model is clean during the matter but the platform's archival tier pricing is not always negotiated up front, and firms get surprised by reactivation fees if a matter reopens. Negotiate archival pricing and reactivation terms in the master agreement, not at the time you need them. Also watch the platform tier fee — Disco's lower tiers cap AI feature access, and the differential between tiers matters more than the headline per-GB rate.

All three vendors increased list prices materially in 2025 and again in early 2026 — typically 8-15% per cycle. Multi-year contracts are the standard hedge, but they lock you to a vendor for 24-36 months. Three-year deals with annual escalators capped at 5% are achievable on volume; one-year deals get standard escalators. The negotiation lever that works best is competitive POCs — running the same matter through two vendors and using the results to drive price. Vendors hate it; it works.

One under-negotiated point: AI feature pricing. **Relativity aiR** is priced separately on top of the platform stack, and that pricing is moving — aiR rates have come down 20-30% from the 2024 launch as the market matured. **Everlaw** and **Disco** include AI in platform pricing, but the included AI features map to specific tiers — make sure the tier you sign covers the AI features you actually need. The wrong tier on a multi-year deal is an expensive mistake. As of June 2026 — verify at relativity.com/pricing, everlaw.com/pricing, and csdisco.com/pricing.