The installation and operation of legal AI software on a law firm's own servers or private data center — required by some clients and regulators for data sovereignty, but involving substantially higher costs and technical complexity than cloud SaaS.
Last reviewed: 2026/05/25
A contract required by GDPR between a data controller and processor, governing how personal data may be handled, secured, and returned or deleted.
SecurityUsing AI tools to identify, manage, and document compliance obligations under the EU General Data Protection Regulation across organizational data practices.
SecurityAI models deployed on infrastructure owned or controlled by the law firm or legal department, keeping all data and computation within the organization's own environment.
SecurityAn AI vendor commitment that customer inputs and outputs are not stored beyond the immediate processing session — the strongest available privacy assurance for sensitive legal queries.
Enterprise AI for portfolio-level contract analysis and institutional memory.
AI clause extraction and due diligence trusted by AmLaw 100 firms.
Generative AI for eDiscovery review and privilege at enterprise scale.
Move from this definition to role-based legal AI shortlists and the selection criteria that matter for each type of legal team.
Am Law 200 and global firm workflows: accuracy at scale, security compliance, and matter-level auditability.
Legal department workflows: contract lifecycle, regulatory tracking, outside counsel management, and risk.
Last reviewed: 2026/05/25. Definitions are written by the LawyerAI Editorial team. Commercial relationships are disclosed and do not determine editorial scores or conclusions. See our Sponsorship & Affiliate Disclosure.
The installation and operation of legal AI software on a law firm's own servers or private data center — required by some clients and regulators for data sovereignty, but involving substantially higher costs and technical complexity than cloud SaaS.
On-premise deployment sits at the intersection of legal AI capability and data security requirements. For most law firms and legal departments, cloud SaaS is the right deployment model: the vendor manages the infrastructure, security, and updates; the firm pays a subscription and focuses on using the tool rather than operating it. But for certain segments of the legal market, cloud SaaS is not permissible.
BigLaw firms serving major financial services clients face the strictest vendor security requirements in any industry. A global bank retained by a law firm may require that the bank's documents never leave the firm's own infrastructure — that no processing occur on third-party cloud servers, regardless of the security controls those servers employ. Government legal departments may face sovereignty requirements: legal documents processed by AI must remain within national borders, or within the government's own IT infrastructure. Defense and intelligence-adjacent legal work may require classified or sensitive but unclassified (SBU) network deployment that precludes any commercial cloud processing.
For law firms serving these clients, on-premise legal AI capability is not a preference — it is a prerequisite for AI adoption. A firm that cannot deploy legal AI on-premise cannot use legal AI on matters for those clients, creating competitive disadvantage relative to firms that can.
The practical challenge is that on-premise deployment of modern AI tools is technically and financially demanding. The AI models powering legal AI tools are large, computationally intensive, and require specialized hardware. The vendors who build these tools have optimized for cloud SaaS delivery and treat on-premise deployment as a non-standard, premium offering. Understanding what on-premise legal AI actually requires — and what it realistically provides — helps firms make informed decisions about when to require it and when to accept alternative security arrangements.
What on-premise means technically:
On-premise deployment means that the software components of the legal AI system run on hardware that the law firm or legal department owns or leases and physically controls. This includes:
In a cloud SaaS deployment, all of these components are provided and managed by the vendor in their cloud infrastructure. In an on-premise deployment, the law firm provides all of these components within its own data center.
The AI model infrastructure challenge:
The most demanding component of on-premise legal AI deployment is the AI model itself. Modern large language models require significant GPU (Graphics Processing Unit) resources to run efficiently. Running GPT-4-class models on-premise requires dedicated GPU server hardware — NVIDIA A100 or H100 GPU servers — that cost tens of thousands of dollars per server and require specialized power, cooling, and maintenance.
This hardware requirement creates a significant cost barrier for on-premise legal AI. A law firm deploying a GPU-accelerated AI model on-premise may need to invest $100,000-$500,000 or more in hardware infrastructure alone, plus ongoing maintenance, power, and cooling costs. This is in addition to the software licensing costs, which are typically higher for on-premise licenses than for equivalent SaaS subscriptions.
Alternative architectures — using smaller, more efficient open-source models that can run on less demanding hardware — may enable lower-cost on-premise deployment but typically at some cost to AI capability and output quality.
On-premise vs. private cloud — the distinction:
On-premise deployment (hardware the firm owns and controls) is distinct from private cloud deployment (dedicated cloud infrastructure leased from a cloud provider, accessible only to the specific firm). Private cloud provides physical isolation of compute resources without the firm needing to own and operate the hardware. This is an important distinction because some clients who require "on-premise" processing may accept private cloud deployment as an equivalent control, while others specifically require firm-owned hardware.
On-premise legal AI from leading vendors:
Luminance offers on-premise and private cloud deployment options for enterprise clients with regulatory requirements or client mandates that preclude standard cloud processing. Luminance's on-premise deployment maintains the full functionality of its contract AI in the client's controlled environment. Kira Systems has offered on-premise deployment for law firms with strict data handling requirements, allowing firms to run Kira's machine learning contract review within their own infrastructure. Relativity AI offers on-premise deployment through its Relativity Server product — distinct from its RelativityOne cloud SaaS offering — enabling law firms and corporations to run eDiscovery AI workflows within their own data centers.
The regulatory drivers for on-premise legal AI:
Several regulatory and contractual frameworks specifically drive on-premise legal AI requirements:
Data sovereignty regulations: Some jurisdictions require that certain categories of data be processed within national borders. GDPR's data transfer restrictions, China's Data Security Law, Russia's data localization law, and similar regulations may require that legal documents not be processed on servers in other jurisdictions.
Client security mandates: Major financial services clients (banks, hedge funds, private equity firms), defense contractors, and government entities frequently impose vendor security requirements — ITAR compliance, FedRAMP authorization, specific network isolation requirements — that cloud SaaS legal AI tools may not satisfy, requiring on-premise deployment by the law firm.
Government legal department requirements: Government legal departments working with classified or sensitive information may be required by policy to use only government-operated IT infrastructure, precluding commercial cloud legal AI.
Assess the actual requirement before committing to on-premise: Client or regulatory requirements for data localization are sometimes broader than actually required, or may be satisfiable through alternative means (private cloud, contractual DPA commitments, specific cloud regions). Before committing to the significantly higher cost and complexity of on-premise deployment, assess whether the client's actual security requirement requires firm-owned hardware or can be satisfied with alternative controls.
Evaluate the total cost of ownership: On-premise legal AI is substantially more expensive than cloud SaaS when all costs are counted: hardware capital expenditure, software licensing (typically higher for on-premise), IT labor for deployment and ongoing maintenance, hardware refresh cycles, power and cooling, and the opportunity cost of IT resources diverted from other priorities. Build a realistic 5-year total cost of ownership model before comparing on-premise to cloud SaaS on subscription price alone.
IT capability requirement is significant: Running AI model infrastructure on-premise requires IT expertise — specifically, expertise in GPU server management, AI model deployment, and the specific software stack the vendor uses — that most law firm IT departments do not currently have. Assess whether your IT team has the capability to deploy and maintain on-premise legal AI, or whether engaging specialist third parties is required.
Vendor support for on-premise is limited: Most legal AI vendors have optimized for cloud SaaS delivery. Their support organizations, update processes, and product roadmaps are designed around cloud deployment. On-premise deployments may receive slower support response times, delayed access to new features, and more complex update processes. Negotiate specific support SLAs for on-premise deployments.
Update and model versioning complexity: Cloud SaaS legal AI vendors update their models and software continuously. On-premise deployments require coordinating with the vendor to receive updates, testing updates in the firm's environment, and managing the update process without disrupting production workflows. This creates a maintenance overhead absent from cloud SaaS.
Hardware obsolescence: AI model requirements evolve rapidly. Hardware that is sufficient for today's AI models may be inadequate for improved future models. On-premise deployments face hardware refresh costs and refresh cycles that cloud SaaS users do not.
Security quality depends on firm IT capability: The security assurance of on-premise deployment is bounded by the security capability of the firm's IT organization. A well-managed cloud deployment by a major vendor with a dedicated security team and continuous security monitoring may provide stronger practical security than an on-premise deployment maintained by a firm without equivalent security resources. On-premise is not inherently more secure than cloud — it shifts where the security investment must be made.
Capability limitations from local model constraints: Some advanced legal AI capabilities require model scales or computational resources that are not economically feasible for on-premise deployment. Firms requiring on-premise deployment may have access to less capable AI models than firms using cloud SaaS, particularly for capabilities requiring frontier model scale.
Vendor availability risk: If a vendor exits the market, significantly changes their product, or discontinues on-premise support, on-premise customers may face higher switching costs and continuity risk than cloud SaaS customers.