Glean vs Harvey
Detailed side-by-side comparison to help you choose the right tool
Glean
🟢No CodeBusiness AI Solutions
AI-powered enterprise search and knowledge assistant. Connects all company information to provide instant, intelligent answers for employees.
Was this helpful?
Starting Price
ContactHarvey
🟢No CodeBusiness AI Solutions
Enterprise-grade AI legal assistant built for law firms and corporate legal departments, offering contract analysis, legal research, litigation support, document drafting, and compliance automation with enterprise-grade security.
Was this helpful?
Starting Price
~$1,000/lawyer/monthFeature Comparison
Scroll horizontally to compare details.
💡 Our Take
Choose Glean if you need a horizontal enterprise AI platform spanning Slack, Salesforce, Jira, Confluence, and 100+ other systems across all departments. Choose Harvey AI if you're a law firm or in-house legal team needing purpose-built legal workflows like contract analysis, case research, and litigation drafting — Harvey's vertical depth in legal beats Glean's generalist approach for that specific use case.
Glean - Pros & Cons
Pros
- ✓Integrates with 100+ enterprise applications including Slack, Salesforce, Jira, Confluence, GitHub, Google Workspace, Microsoft 365, ServiceNow, and Zendesk — broader native connector coverage than most competitors in our directory
- ✓Permission-aware retrieval inherits source-system ACLs in real time, so users only see results they're already authorized to access — critical for regulated industries and confidential data
- ✓Three integrated products in one platform: enterprise Search, an AI Assistant, and a no-code/low-code Agents builder, reducing the need to stitch together separate vendors
- ✓Strong customer base of 100+ enterprises including Reddit, Sony Electronics, Pixar, Databricks, Duolingo, and Confluent, providing social proof and a mature product roadmap
- ✓Founded in 2019 by ex-Google search engineers, with deep retrieval expertise reflected in ranking quality — a meaningful advantage over generic RAG implementations
- ✓Vendor-neutral on LLMs: customers can route to OpenAI, Anthropic, Google, or self-hosted models, avoiding lock-in to any single foundation model provider
Cons
- ✗Enterprise-only pricing with no public tiers, free trial, or self-serve option — small teams and individuals are effectively excluded
- ✗Implementation typically takes 3-6 months and requires dedicated IT, security, and change-management resources, making it a heavier lift than chatbot-style alternatives
- ✗Search and answer quality is bottlenecked by the cleanliness of underlying source data; organizations with poor document hygiene see weaker results
- ✗Agent-building tools, while no-code, still require thoughtful prompt engineering and workflow design — not truly turnkey for non-technical users
- ✗Total cost of ownership (license + implementation + ongoing tuning) can exceed $500K annually for large deployments, putting it out of reach for many mid-market firms
Harvey - Pros & Cons
Pros
- ✓Legal-specific AI models trained on millions of legal documents deliver higher accuracy and domain understanding than general-purpose AI tools, with proprietary fine-tuning that minimizes hallucinated citations
- ✓Partnership with Intapp provides industry-leading privilege protection and ethical wall enforcement, ensuring AI-assisted workflows respect attorney-client privilege boundaries and conflict-of-interest requirements
- ✓Proven enterprise adoption with 60+ AmLaw 200 firms and marquee clients including A&O Shearman and PwC, demonstrating reliability and trust at the highest levels of the legal profession
- ✓Comprehensive integration with existing legal technology infrastructure including iManage, NetDocuments, Microsoft 365, and enterprise SSO providers like Okta for seamless deployment into firm workflows
- ✓Enterprise-grade security architecture with SOC 2 Type II certification, ISO 27001 compliance, end-to-end encryption, and a contractual guarantee that no client data is used for model training
Cons
- ✗Enterprise-only pricing with annual commitments starting at approximately $1,000–$1,200 per lawyer per month makes Harvey prohibitively expensive for small and mid-sized firms, solo practitioners, and legal aid organizations
- ✗No public pricing, free tier, or self-serve signup option means prospective users cannot evaluate the platform without engaging in a multi-week sales and pilot process
- ✗Heavily oriented toward large law firm and corporate legal department workflows, with less focus on niche practice areas such as patent prosecution, immigration, or family law
- ✗Output still requires attorney review and professional judgment — Harvey is explicitly an assistant rather than a replacement, and AI-generated legal analysis can still contain errors requiring validation
- ✗Deep value depends on integrating firm proprietary data and workflows, requiring significant implementation effort over 3–6 months including SSO configuration, DMS integration, and user training
Not sure which to pick?
🎯 Take our quiz →🔒 Security & Compliance Comparison
Scroll horizontally to compare details.
Price Drop Alerts
Get notified when AI tools lower their prices
Get weekly AI agent tool insights
Comparisons, new tool launches, and expert recommendations delivered to your inbox.