Harvey AI Review: Pricing and In-House Fit [2026]
Caitlin PricePublished Updated
Harvey is a legal platform for research, document analysis, contract review, and collaboration. It initially focused on law firms and now also serves corporate counsel.
Harvey AI pricing requires a quote. The company does not publish a standard subscription rate. In a September 17, 2026, interview, COO Katie Burke described seat-based pricing with an optional consumption model for customers requesting it. Ask for a written quote covering your team’s users, tools, usage, and support.
For in-house teams weighing that investment, we recommend GC AI, a legal AI platform with a practical toolkit for solo GCs through Fortune 500 legal teams.
Our Playbooks apply company positions in GC AI for Word, Research helps investigate the law behind an issue, and Exact Quote opens quoted source passages for your review. GC AI serves 2,200+ legal teams, including teams at News Corp, Nestlé, and TIME.
How Harvey AI Pricing Works
Harvey sets fees in the order form. Custom workflows, third-party integrations, and certain research modules sit outside its Basic Service and need separate written coverage.
Confirm that the workflow shown in a demonstration is included in your offer.
What the 2024 and 2025 Price Estimates Tell You
In April, Artificial Lawyer cited approximately $1,200 per seat per year, equivalent to $100 per month. Anonymous market sources supplied the estimate and reported minimum; the article did not establish universal terms.
In 2025, Artificial Lawyer published another pricing estimate that Harvey disputed, including assumptions about costs, seat requirements, and existing Lexis subscriptions. Neither report establishes a current price or minimum commitment. Budget from your written offer.
Get One Complete Written Quote
- Users and commitment: Included users, any minimum commitment, seat reassignment, expansion, and treatment of occasional users.
- Products and availability: The specific research, Vault, contract review, Contract Intelligence, agent, and integration capabilities included, with any preview or early-access status identified.
- Research entitlements: Databases, jurisdictions, content access, and any separate subscription or commercial requirement.
- Usage: The billing model, any metered unit, included allowance, rates, overages, and treatment of reruns or automated work.
- Implementation and support: Configuration, playbook assistance, training, connector setup, ongoing support, and their costs.
- Evaluation and renewal: Evaluation scope and charges, initial term, renewal provisions, and options to change or end the subscription.
For a consumption component, also request usage reporting, alerts, and any available spending controls. Ask how model selection affects charges, if at all.
Harvey meters and bills preview features as normal usage unless the applicable terms indicate otherwise. Confirm any evaluation or early-access charges in the written offer.
Model an Ordinary Month and a Peak Month
Model your usual workload and a peak such as a transaction or quarter-end contract queue, using the same commercial terms.
| Budget Input | Ordinary Month | Peak Month | Written Term to Confirm |
|---|---|---|---|
| Users | Expected active reviewers and supporting users. | Additional reviewers or temporary collaboration needs. | Which users require paid access and how additions are charged. |
| Document work | Typical contract reviews, collections, and follow-up revisions. | Larger collections, more review turns, or a higher contract volume. | Applicable capacity limits and which operations incur usage charges. |
| Research and agents | Expected research questions and recurring tasks. | More complex questions, reruns, or additional automated work. | Included sources, metered units, model-dependent charges, and allowances. |
| Services and administration | Routine support, playbook maintenance, and user administration. | Additional setup, training, or project assistance. | Included services, separately charged work, and internal ownership. |
| Total | Quoted fixed charges plus applicable variable charges and services. | The same calculation using peak assumptions. | Overages, limits, alerts, spending controls, and renewal effects. |
If the offer is fixed-price within defined limits, record the included capacity and limits. Separate one-time implementation costs from recurring costs, and avoid counting a service twice when it is already included.
What Harvey Does for Legal Teams
Research and Document Collections
Harvey works with LexisNexis and other legal-data providers through Knowledge. Ask LexisNexis is separate from Harvey's Basic Service. If Lexis research is part of your evaluation, confirm that the quote includes it and the sources your lawyers need.
Ask the demonstration to show a lawyer opening the underlying authority, checking its relevance and currency, and resolving a conflicting result.
Vault supports document organization, bulk analysis, and review tables. This is relevant to diligence, recurring-term analysis, and finding provisions across a collection. Ask the demonstration to move from a table entry back to the relevant document, including amendments and exceptions.
Also distinguish repository capacity from the scope of a particular analysis. A file-storage limit does not establish how many documents a given task can analyze together or how it handles incomplete material.
Contract Playbooks and Review Agents
Harvey’s Playbook Builder creates review instructions from existing playbooks, templates, marked-up contracts, or a guided conversation.
Test whether the resulting playbook preserves your preferred position, permitted fallback, and approval conditions. An old contract's concession should remain distinguishable from approved policy.
Contract Review Agents can use executed agreements and an attached precedent bank during review and suggest updates to stale positions. Harvey currently directs buyers to an early-access waitlist. Confirm access before making precedent-based review a requirement for your rollout.
Harvey’s Contract Intelligence covers intake, review, and executed-contract insights. Evaluate the stage you need to improve. A review queue, a first-pass redline, and a portfolio answer are different deliverables, even when they sit within the same offering.
Word and Connected Sources
Custom agents created in Harvey’s Agent Builder can run in its Word add-in using the open document and supporting Vault material. Lawyers can inspect proposed changes and apply or discard them. Applied suggestions become tracked changes.
Custom Workflows do not yet run in Harvey's Word add-in. If your team depends on one, test where counsel must leave Word to complete it.
Harvey can search SharePoint, OneDrive, Google Drive, and Gmail, and import Ironclad contracts into Vault or Assistant. Ask to see the connection your team needs, using the intended user’s permissions and the proposed package.
Permissions and Source Evidence
Harvey’s personal-source connections use the requesting user’s credentials and the source system’s access controls. Administrator restrictions, user permissions, and policy checks govern whether a connected action can run.
Native connector results include inline citations, while Model Context Protocol (MCP) connector output enters the model context as plain text. Inspect how the integration your team would use exposes its sources to the reviewer.
Harvey’s MCP policy engine can allow, deny, or pause partner-tool calls for explicit approval. Failed checks deny the call; some finer-grained controls remain under development. These controls reduce risk, but prompt injection remains an open problem.
Confirm who can connect a source, what an agent can retrieve or change, when approval is required, and what evidence remains afterward.
Is Harvey a Good Fit for an In-House Department?
For an in-house department, Harvey’s fit depends on the package you buy. Check which research sources, agents, and Word workflows it includes and what your team must configure to use them.
Ask who will configure the sources, maintain the playbooks, administer access, and help lawyers learn the workflow. Include that work in the purchase decision. A successful demonstration does not establish that the team can repeat the result with its ordinary staffing.
Harvey and its subprocessors commit not to train AI models on your data or content. Some optional features have different processing locations, retention conditions, or human-review provisions. Review the terms for the features you will enable, including whether your workspace retention setting applies.
GC AI vs. Harvey: How Do They Compare?
GC AI was built for in-house lawyers by our co-founder and CEO, Cecilia Ziniti, a three-time general counsel at Anki, BloomTech, and Replit who also worked in-house at Amazon and Cruise. She founded GC AI to build the platform she wished she had as a GC: one that helps counsel apply company positions, negotiate agreements, and advise business leaders.
Harvey initially focused on law firms and now also serves in-house departments. Compare how each platform supports the work your team needs to finish:
| What You Need to Do | GC AI | Harvey |
|---|---|---|
| Apply Your Company’s Positions in Word | GC AI’s Playbooks check the agreement against your preferred terms and fallback positions in GC AI for Word. Review the proposed redlines where you negotiate. | Harvey’s Word add-in supports Playbooks and custom agents. Custom workflows are not yet supported in the add-in. |
| Reuse Context and Review Instructions | Select a Company Profile with your business’s jurisdictions, risk tolerance, and preferred clauses. Projects keep the matter’s files and instructions together, so you can reuse relevant context. | Memory carries user preferences across Harvey. Playbook Builder creates review instructions from existing playbooks, templates, and marked-up contracts. |
| Check the Passage Behind the Advice | GC AI’s Exact Quote opens the original document with the cited passage highlighted. Check the wording and surrounding context before advising the business. | Agents and Knowledge return cited output. Review the underlying sources and confirm that they support the recommendation. |
| Budget for Everyday Use | The Individual plan is $500 per month. Seat plans include unlimited chats and skills, so follow-up questions and revisions have no per-chat charge. API usage is billed separately. | Pricing requires a quote. Seat-based pricing can include an optional consumption model for customers requesting it; confirm usage charges in your offer. |
| Build Your Team’s AI Fluency | Live, attorney-led Academy classes let you practice on legal tasks and ask questions. Team plans also include Solutions Attorney support for putting GC AI to work. | Harvey Academy provides on-demand training and workflow guidance. Confirm any live training and implementation services in the offer. |
Data note: Capabilities and pricing reflect public product pages and documentation checked in October 2026. Availability depends on the plan, integration, and rollout. This is a capability comparison, not a scored product test.
When comparing GC AI vs. Harvey, use the supplier review below to inspect how each platform carries the company’s position from the contract into the advice.
Compare the Confirmed Packages
GC AI Team and Enterprise pricing require a quote. Team and Enterprise plans include one chosen legal database, US Case Law or International Law; access to the other database can be added. Individual database access is available through add-ons.
Confirm the document capacity, included services, and price for Contract Intelligence in your GC AI offer.
For a Harvey offer with a consumption component, document volume and repeated runs may affect the bill even if the seat count stays constant. Compare the same users, research access, document capacity, and services; the written offers determine the total cost difference.
GC AI Team includes Solutions Attorney support, while Enterprise offers onboarding, change management, and dedicated support options.
GC AI Academy offers live, attorney-led classes that help users:
- Build Playbooks around the company’s preferred terms and fallback positions.
- Draft, review, and redline agreements directly in Microsoft Word.
- Give clearer AI instructions, check source material, and evaluate answers before using them.
These AI skills for lawyers help users work better and faster. For confidential matters, pair that fluency with clear safeguards for company data.
We keep your data isolated from other customers and encrypt it in storage and transit. GC AI works with leading AI providers such as OpenAI and Anthropic. None of our providers train on your data.
Zero-data-retention agreements with LLM providers apply wherever feasible; the subprocessor list records specific conditions and exceptions. Check these alongside application retention, access controls, and the models enabled for your team.
Compare Both Platforms on a Complete Matter
Use the same source documents, company instructions, and required deliverables for each finalist. The following hypothetical supplier review shows a GC AI sequence that can serve as a comparison task.
Assume a proposed vendor will process customer information. The supplied master services agreement (MSA) applies one liability cap to all claims and refers to a data-processing addendum that is missing. For this example, the company's approved playbook calls for a separate liability cap for specified confidentiality and data-security breaches. Any exception requires general counsel approval after the business confirms the intended data use.
Use your company’s approved positions and approval rules for an actual transaction.
Assemble the Matter and Identify Gaps
Bring together the proposed MSA, available schedules, approved playbook, and the business owner's description of the service and data involved. Select the relevant Company Profile so GC AI has the company's jurisdictions and risk preferences. Distinguish approved precedent from one-off concessions.
Where permitted, Agent Connectors can help retrieve the request or prior correspondence from connected work accounts. Check the actual thread, attachments, and versions. Access and available actions depend on the organization’s configuration and the connected account.
Ask the review to identify the missing addendum. Counsel can analyze the available MSA while keeping conclusions dependent on the addendum open. Assign procurement to obtain it and the business owner to confirm the planned data use.
Apply the Company’s Position in Word
Run the approved Playbook against the agreement in GC AI for Word. Request an explanation of how the liability cap interacts with confidentiality, data-security duties, indemnities, and remedies.
For this hypothetical matter, the proposed edit should introduce the separate cap described in the playbook for the specified breaches. It should not silently remove other exclusions, change unrelated claims, or treat a prior exception as approval for this vendor.
Inspect the full agreement after the edit. A clause-level improvement can still conflict with a definition or another remedy elsewhere in the document.
Research the Question the Playbook Cannot Resolve
Suppose counsel needs to assess the proposed damages limitation under Delaware law. Provide GC AI Research with the exact wording, governing jurisdiction, relevant facts, and the question that affects the negotiation.
Use US Case Law to locate relevant opinions and open the full text in Opinion Reader. Counsel checks the holding, procedural context, subsequent treatment, and application to the clause. Treatment indicators are not available for every case, so critical authorities require independent verification.
The lawyer then decides whether the research changes the proposed language or escalation. Keep the reasoning with the matter so a reviewer can understand why the advice differs from the starting playbook.
Ritesh Patel, Chief Legal Officer at Viant Technology, uses GC AI for initial analysis before deciding whether to involve outside counsel:
Now, I can analyze it myself, see where it gets me, and call outside counsel if I'm truly out of depth.
Check the Sources and Prepare Two Outputs
Click an Exact Quote citation in chat to open the source document with the quoted text highlighted. In this example, check the cap and its exceptions against the explanation, then inspect the related confidentiality and data-security provisions. Counsel must still decide whether the proposed clause implements the company’s position.
The finished review should produce both a redline and a short decision record.
| Output | What It Should Contain in This Example | What Counsel Checks |
|---|---|---|
| Counterparty redline | The proposed separate cap, related conforming changes, and comments appropriate for the vendor. | Consistency with the approved position, connected provisions, research, and the intended allocation of risk. |
| Internal recommendation | The unresolved source gap, the requested negotiation position, the decision required if the vendor refuses, and named next actions. | That it matches the redline and does not present an unapproved exception as acceptable. |
An illustrative internal message could read:
I recommend the playbook’s separate liability cap for the specified confidentiality and data-security breaches, as shown in the redline. Procurement must obtain the missing data-processing addendum, and the business owner must confirm the planned data use. If the vendor refuses the separate cap, we need general counsel approval for an exception. Final approval remains open pending those steps.
Measure What the Lawyer Still Has to Do
For each platform, record whether it found the material issue, identified the missing addendum, respected the approval condition, and produced consistent outputs. Count unnecessary flags and edits as well as missed issues. A faster first answer has limited value if counsel spends the saved time correcting it.
Inspect the exchanged Word file for tracked changes, numbering, definitions, and comments. Check that internal notes have not moved into the counterparty version. Measure the time counsel spends verifying sources, researching, repairing the redline, and preparing the business response.
After approval, save the sources and decision in GC AI Projects, which group matter files, chats, and instructions. Add the relevant files explicitly and confirm access. Before reusing the record, check whether the prior position still applies; the playbook owner must approve policy changes, and vendor-specific exceptions should remain identifiable.
Connect the Review to the Department’s ROI
Record whether the completed work frees counsel for additional matters or replaces work you would otherwise send to a firm. Report lawyer capacity separately from outside counsel fees avoided, and count each benefit once. Include configuration, training, playbook maintenance, and review time alongside subscription and usage charges.
Use the GC AI ROI calculator for an initial estimate, then replace assumptions with your own invoices and matter records. Our legal AI ROI guide shows how to build the finance case; the AI contract review guide covers the broader software shortlist.
Inspect the GC AI Work Your Team Would Send
Bring an agreement your team is authorized to use, the company’s approved positions, and the business owner’s question to a GC AI demonstration. Request the counterparty redline and internal recommendation separately. Ask to see how counsel verifies and repairs those outputs before they leave the legal team, then confirm which quoted features produced them.
