Own the models, data, and code behind your legal AI on your own infrastructure — vs. a per-seat assistant running in Google's cloud
Legal organizations adopting AI face one hard constraint before any feature: privileged client matters must stay protected under attorney-client privilege and ABA ethics duties. Where the AI runs — and who controls it — matters as much as what it does.
Gemini is a managed assistant from Google, billed at about $30 per user per month and running in Google's cloud on Google's Gemini models. Its strength is tight Google Workspace integration with little setup, but it is tied to Google Cloud and Gemini models and your data is processed in the vendor's cloud.
Self-hosted AI runs on infrastructure you control — on-premise, in your private cloud, or fully air-gapped. You own the code, the data, and the models, run any LLM, and keep privileged client matters inside your perimeter, integrated with Clio, iManage, NetDocuments, Westlaw, and LexisNexis. This comparison covers case research, contract review, discovery, and knowledge management for legal — and when each option is the right call.
by ibl.ai
Owned agentic AI platformby Google
Per-seat AI assistant| Criteria | Self-Hosted AI | Gemini |
|---|---|---|
| Out-of-the-Box Productivity | Strong agent capability once deployed; you configure the workflows your teams need. | Polished assistance from day one with tight Google Workspace integration. |
| Legal System Integration | Deep integration with Clio, iManage, NetDocuments, Westlaw, and LexisNexis via APIs and MCP, built around your data. | Connects to common tools, but integration with sector systems is limited. |
| Custom Agents & Workflows | Build and own production agents for case research, contract review, discovery, and knowledge management. | A few prebuilt agents; customization is bounded by the platform. |
| Any-LLM & Model Control | Run any open or commercial model, route by cost/latency/capability, and switch anytime. | Runs on Google's Gemini models; tied to Google Cloud and Gemini models. |
| Criteria | Self-Hosted AI | Gemini |
|---|---|---|
| Self-Hosting / On-Prem / Air-Gapped | Run on your servers, private cloud, or fully air-gapped with zero external calls. | Runs in Google's cloud; cannot be self-hosted or air-gapped. |
| Data Stays in Your Perimeter | privileged client matters never leaves your environment; every interaction is logged for audit. | Vendor controls help, but data is processed in the provider's cloud. |
| Model Choice | Any LLM — open-source or commercial — under your control. | Locked to Google's Gemini models. |
| Source Code & Platform Ownership | Own the full platform code; no lock-in to a vendor's roadmap. | You rent access; the platform and roadmap belong to the vendor. |
| Criteria | Self-Hosted AI | Gemini |
|---|---|---|
| Cost at Scale | Flat, usage-based cost on owned compute — no per-seat fees. | about $30 per user per month; cost rises with every seat. |
| Compliance & Audit Fit | Data stays in your perimeter, supporting attorney-client privilege and ABA ethics duties with full audit logging. | Vendor compliance coverage under shared-responsibility cloud terms. |
| Time-to-Value | Requires infrastructure and setup, or a partner to deploy it for you. | Turn it on for your users with minimal setup. |
| Support & Maintenance | Self-managed, or fully supported with forward-deployed engineers. | Fully managed by Google with enterprise support. |
Self-hosted AI keeps privileged client matters inside your perimeter and can run fully air-gapped — the strongest posture for attorney-client privilege and ABA ethics duties.
Gemini adds capable assistance quickly, but processes data in Google's cloud under shared-responsibility terms.
For legal workloads bound by attorney-client privilege and ABA ethics duties, owning the stack is the safer default; Gemini fits lower-sensitivity productivity.
Self-hosting replaces per-seat licensing with flat cost on compute you own, so broad rollouts don't scale with headcount.
Gemini is about $30 per user per month, predictable per user but growing with every license.
For organization-wide deployment, owned infrastructure is often far cheaper at scale.
A model-agnostic platform runs any model — including the vendor's own — and switches as the frontier moves.
Gemini is tied to Google Cloud and Gemini models.
If avoiding model lock-in matters, the owned, model-agnostic platform wins.
Self-hosting keeps privileged client matters in your environment, supporting attorney-client privilege and ABA ethics duties and air-gap requirements a managed cloud assistant cannot meet.
Gemini delivers immediate value with tight Google Workspace integration and minimal setup.
Flat, usage-based cost on owned compute avoids per-seat fees that scale with headcount.
Owning the platform lets you build and tune production agents for case research, contract review, discovery, and knowledge management across any model.
Timeline: A few weeks, depending on infrastructure and MLOps maturity
Timeline: Days to a couple of weeks
See how ibl.ai deploys AI agents you own and control—on your infrastructure, integrated with your systems.