How to Choose eDiscovery Software in 2026

To choose eDiscovery software, start with the size and frequency of your matters, the data sources you collect from, who will run the platform day to day, and which pricing model fits your data. Then test AI review on a closed matter and confirm production formats and security. Those decisions matter more than feature lists, because most platforms can process and review email; far fewer fit your caseload at a sensible cost.

Size up your caseload before you shop

eDiscovery needs vary more by matter profile than by firm size. A ten-lawyer commercial litigation firm with two large cases a year needs something very different from a fifty-lawyer insurance defense firm with dozens of small productions. Before you take a demo, gather a few numbers from the last two years:

• How many matters involved document review, and how many gigabytes each one collected
• How long each matter stayed hosted, from collection to final disposition
• How many reviewers worked on the largest matter
• How often you received productions from opposing parties, and in what format
• Whether any matter involved regulators or government agencies

Those answers point you toward a tier. Occasional small matters fit self-service tools built for lawyers and paralegals. A steady flow of mid-size matters fits a cloud platform with strong review and analytics. Very large, complex matters, multi-party litigation, and second requests usually call for an enterprise platform or a service provider running one for you.

Match the platform to the data you collect

Email and loose files are the easy part. Modern discovery increasingly involves collaboration and mobile data, and each source raises its own processing questions.

• Microsoft 365 and Google Workspace. Ask whether the platform can collect directly from these sources or only ingest exports, and how it handles linked attachments that point to cloud documents instead of embedding them.
• Slack and Microsoft Teams. Chat data needs to be grouped into reviewable conversations, not thousands of single-line documents. Ask to see how a day of channel messages appears in review.
• Mobile devices. Text messages and messaging apps often arrive as forensic exports. Confirm which export formats the platform loads and how threads display.
• Audio and video. Some platforms transcribe recordings so they can be searched. Logikcull offers audio and video transcription on its annual plans.
• Incoming productions. You will receive load files with images, text, and metadata from opposing parties. Make sure the platform imports standard load files cleanly.

Federal Rule of Civil Procedure 34(b) lets the requesting party specify the form of production, and Rule 26(f) expects the parties to discuss ESI early. If your platform cannot handle a data type, you will learn it at the worst possible time. Test your most common sources before you sign.

Decide who will run it

This decision shapes everything else. Enterprise platforms reward trained administrators. RelativityOne handles very large data sets, deep analytics, and custom workflows, but it takes trained staff to run well, which is why many smaller firms access it through a litigation support provider. Everlaw is known for an interface new reviewers learn quickly, and it builds case strategy tools into the same platform.

Self-service tools are designed for lawyers and paralegals who run discovery themselves a few times a year. Logikcull processes, OCRs, and indexes data automatically after you drag it in. GoldFynch targets solo lawyers and small firms with modest document sets, with no user, processing, or production fees.

Be honest about your team. A powerful platform no one on staff can administer leads to vendor services charges or mistakes in productions. Choose for the team you have today, and revisit the choice if you hire litigation support staff.

Evaluate AI review with your own documents

Courts have accepted technology-assisted review for more than a decade. Da Silva Moore v. Publicis Groupe (S.D.N.Y. 2012) was the first opinion to approve predictive coding, and Rio Tinto v. Vale (S.D.N.Y. 2015) noted that courts permit its use when the process is transparent and reasonable. Generative AI review is newer, and vendors now sell tools that summarize documents, answer questions about the record, and suggest relevance and privilege calls.

Billing for AI varies widely. RelativityOne’s aiR tools come with no separate AI license or per-user fees, while other vendors charge for AI by usage or reserve it for higher plans. Logikcull includes its AI fact-finding tools on annual plans rather than pay-as-you-go. Get the AI cost in writing.

Then test it properly:

1. Pick a closed matter where you already know the key documents and privilege calls.
2. Load a representative sample into each finalist.
3. Run the AI review or prioritization and compare its picks with your known answers.
4. Check how the tool explains its reasoning and whether reviewers can audit it.
5. Note how many documents a human still needs to review to reach the same result.

If you plan to rely on TAR or AI review in a contested matter, also confirm the platform can report the validation metrics you would disclose to opposing counsel or the court.

Check security, privilege protection, and compliance

Discovery data often contains trade secrets, health records, and personal financial information. ABA Model Rule 1.6(c) requires reasonable efforts to protect it, and clients increasingly audit their firms’ vendors. Ask for:

• Current security attestations such as SOC 2 Type II or ISO 27001
• Single sign-on and two-factor authentication for every user
• Granular permissions so contract reviewers see only their assigned documents
• PII and PHI detection and redaction tools, and a business associate agreement if you handle HIPAA-covered records
• Data residency options for matters involving data from outside the US
• FedRAMP authorization if you work with federal agencies; Everlaw and RelativityOne’s government offering both hold FedRAMP authorization

Privilege protection is a workflow question as much as a security one. Look for privilege logging tools, redaction that burns into produced images, and production checks that flag privileged-tagged documents before they leave. Pair those tools with a Federal Rule of Evidence 502(d) order in federal cases, which protects against waiver if something slips through.

Compare pricing on the same sample matter

eDiscovery pricing models differ so much that headline rates mislead. Per-GB pricing charges for data hosted or processed, per-user pricing charges for reviewers, and enterprise platforms often offer pay-as-you-go or one-year and three-year commitments. Only some vendors publish rates. GoldFynch is one, with case plans from $27 a month for 3 GB and $179 a month for 25 GB, billed prorated. Everlaw, RelativityOne, and Logikcull quote prices, though Logikcull’s pay-as-you-go plan bills monthly per GB with a 10 GB minimum.

Build one sample matter from your caseload, such as 50 GB collected, 20 GB after culling, eight months of hosting, five reviewers, and two productions. Ask every vendor to quote that exact scenario, including:

• Ingest and processing charges, and whether deduplicated data is billed
• Monthly hosting, and whether rates drop after a matter closes
• User fees, including for outside counsel and experts
• Production, OCR, and redaction charges
• AI review costs
• Fees for archiving or deleting a matter

The lowest per-GB rate is not always the lowest bill once hosting months and add-ons are counted. For pricing models and our picks by firm type, see our ranked list of the best eDiscovery software.

Run a pilot before you commit

A demo shows the vendor’s best data. A pilot shows yours. Ask each finalist for a trial workspace or a sample matter, and treat it like a small real case. GoldFynch lets you test with a free case without a credit card, and Logikcull offers a sample matter, while enterprise vendors usually set up a guided proof of concept.

1. Load a mix of data from a closed matter: a PST, a chat export, some scanned PDFs, and an incoming production.
2. Have a paralegal run searches and build a review batch without vendor help.
3. Tag privilege, apply redactions, and run a test production with Bates numbering.
4. Check the production against the format your ESI protocols usually require.
5. Ask a partner to use the case-building or summary tools for twenty minutes.

Pick the platform that got through the pilot with the fewest support tickets and the clearest bill, not the one with the most impressive demo.

Questions to ask in every demo

• Can you quote our sample matter, including hosting, users, AI, and productions?
• Which data sources can you collect directly, including Slack, Teams, and mobile?
• How is generative AI review billed, and can we test it on a closed matter?
• What validation reports can we share with opposing counsel for TAR?
• Which security attestations do you hold, and can we see the report?
• How do you prevent privileged documents from being produced?
• What does it cost to archive or delete a closed matter?
• What training is included for attorneys and paralegals?

Frequently asked questions