Scrivly vs. Harvey
Harvey is one of the most well-funded legal AI companies in the world. Here is an honest look at how Scrivly compares on the things that matter to your firm: deployment, data residency, pricing, and architecture.
Side-by-side comparison
Based on publicly available information. Updated as products evolve.
| Feature | Scrivly | Harvey |
|---|---|---|
| Deployment | On-premise, cloud, or air-gapped | Cloud-only |
| Data residency | Your office, your cloud, or your data center | Vendor cloud infrastructure |
| Pricing transparency | Published pricing ($199/mo Local) | Enterprise quotes only |
| Seat minimums | None | Reported minimums |
| Hardware option | Dedicated on-premise appliance | Not available |
| AI architecture | Proprietary dual-model inference | Built on third-party models |
| Third-party dependency | None for core inference | OpenAI partnership |
| Target market | All firm sizes, no minimums | Enterprise and Am Law focus |
| Funding | Bootstrapped | $150M+ from Sequoia, others |
Deep dive: Data residency
Your data stays where you put it
Scrivly Local runs on a hardware appliance inside your office. Documents are indexed and queried on-device. No data leaves the premises. There is no cloud sync, no telemetry, and no background data transmission. With Scrivly Pro, data is encrypted and isolated in a dedicated cloud environment. With Scrivly Secure, the entire system is air-gapped.
This is not a software toggle. It is an architectural constraint enforced at the hardware level.
Cloud-native infrastructure
Harvey operates on cloud infrastructure with data processing handled by vendor systems. For firms that are comfortable with cloud-based data residency and trust Harvey\u2019s security practices, this approach offers scalability and rapid deployment.
For firms handling matters where opposing counsel could subpoena cloud providers, or where privilege concerns require physical data control, cloud-only deployment may present challenges.
Deep dive: Pricing
Published, transparent pricing
Scrivly Local costs $199/month plus a one-time \$3,500 hardware purchase. No seat minimums. No per-query fees. No annual commitment traps. Scrivly Pro offers per-seat cloud pricing, also with no seat minimums. Pricing is published because we believe attorneys should know what they are paying before they schedule a demo.
Enterprise-negotiated pricing
Harvey\u2019s pricing is not publicly available and is negotiated on an enterprise basis. Reports suggest that pricing is structured around firm size and usage, with potential seat minimums. This model works well for large firms with procurement teams. It may be less accessible for smaller firms or solo practitioners.
Deep dive: Architecture
Proprietary inference from the ground up
Scrivly\u2019s core inference engine is built internally with multiple patent filings. A dual-model system separates retrieval from composition, enabling citation traceability by design. No client data is sent to third-party APIs. The system can run entirely offline on local hardware.
Built on top of leading foundation models
Harvey has a well-publicized partnership with OpenAI and builds on top of leading foundation models. This gives Harvey access to state-of-the-art language capabilities and allows rapid iteration. The trade-off is a dependency on third-party infrastructure for core AI functionality.
Who should choose Harvey
- Large firms that want an enterprise AI partner with significant funding and resources behind it.
- Firms that are comfortable with cloud-based data residency and do not require on-premise deployment.
- Organizations that value the breadth of a platform built on leading foundation models.
- Firms with dedicated procurement teams that can negotiate enterprise pricing.
Who should choose Scrivly
- Firms that need on-premise deployment for privilege, compliance, or risk management reasons.
- Solo practitioners and small firms that need transparent pricing with no seat minimums.
- Firms that require zero third-party data exposure for AI-assisted work product.
- Organizations handling defense, government, or classified matters that need air-gapped deployment.
Frequently asked questions
Harvey has more funding, a larger team, and deeper penetration into the Am Law 100. For firms that want an enterprise AI partner with significant resources and do not require on-premise deployment, Harvey is a strong choice. Scrivly is better suited for firms that need deployment flexibility, transparent pricing, no seat minimums, or on-premise data residency.
As of the most recent publicly available information, Harvey operates as a cloud-based platform. On-premise deployment is not a standard offering. Scrivly offers on-premise deployment through Scrivly Local, a dedicated hardware appliance that runs inside your office with zero internet dependency.
Harvey and Scrivly take fundamentally different architectural approaches. Harvey builds on top of large third-party language models and focuses on breadth across enterprise legal workflows. Scrivly uses a proprietary dual-model inference engine focused on citation traceability and source verification. For firms where hallucination prevention and data sovereignty are primary concerns, Scrivly’s architecture may be more appropriate.
Yes. Scrivly has no seat minimums. Scrivly Local costs $199/month plus a one-time $3,500 hardware purchase. A solo practitioner gets the full platform. Harvey’s pricing has not been publicly detailed at the per-seat level, and reports suggest enterprise-focused minimums.
No. Scrivly does not use OpenAI, Anthropic, or any other third-party model provider for its core inference. The entire retrieval and response generation pipeline is proprietary. This means no client data is sent to third-party APIs, ever.
Yes. Scrivly offers consultations and demonstrations. We encourage firms to evaluate multiple platforms, including Harvey, before making a decision. Different tools serve different needs, and the best way to evaluate is to see each platform work with your own documents.
Your clients' confidentiality is not negotiable. Your AI shouldn't be either.
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