Choose on-premises deployment when regulations, data classification, or internal policies require data and models to remain in an enterprise-controlled environment. Choose SaaS when approved cloud services meet the organization’s requirements and managed operations are a priority. Consider hybrid deployment when sensitive and general workloads can be separated clearly.

• On-premises: Greater control over data, models, networks, and system access
• SaaS: More infrastructure and platform operations managed by the provider
• Hybrid: Sensitive and general workloads run in different environments
• Decision principle: Compliance defines the minimum requirements; architecture and operational capacity determine how to meet them

Deployment should not be selected by company size or industry alone. Financial services, government, energy, and healthcare organizations often face stricter requirements, but the appropriate model still depends on the applicable regulations, data classification, internal policies, and specific workflow.

Comparing the three deployment models

Organizations can choose different deployment models based on security requirements, data sensitivity, and operational preferences.

SaaS

Cloud-based deployment managed by the provider

Runtime environmentProvider-managed cloud infrastructure

Data boundaryDefined by cloud architecture and contractual agreements

Model optionsUsually based on cloud-hosted models

System integrationSuitable for standard APIs and cloud-approved systems

Identity & accessShared between SaaS platform and enterprise identity systems

Updates & operationsManaged primarily by the provider

Best fitOrganizations with standardized workflows and approved cloud usage

On-premises

Enterprise-controlled deployment for maximum control

Runtime environmentEnterprise data center or private cloud

Data boundaryStorage, networks, and access remain under enterprise control

Model optionsSupports local, proprietary, or approved models

System integrationBetter suited for restricted internal systems

Identity & accessDirect integration with enterprise identity and access controls

Updates & operationsManaged jointly by enterprise teams and providers

Best fitOrganizations requiring strict control over data, models, or systems

Dedicated Cloud / Hybrid

Flexible deployment combining cloud and enterprise environments

Runtime environmentDedicated resources or hybrid infrastructure

Data boundaryDefined based on workload and data sensitivity

Model optionsDifferent models can support sensitive and general tasks

System integrationConnections can be separated according to sensitivity

Identity & accessRequires consistent identity design across environments

Updates & operationsComponents follow separate update and operation processes

Best fitEnterprises that need workload separation and flexible deployment

SaaS is not automatically insecure, and on-premises deployment does not automatically guarantee compliance. Organizations must inspect the actual data flows, model calls, encryption, tenant isolation, access controls, audit records, backups, deletion procedures, and contractual responsibilities.

What does an on-premises deployment involve?

On-premises deployment is more than installing software. It requires decisions about data, models, system connections, security, and ongoing operations. A typical program contains five stages.

Stage 1: Requirements and compliance assessment

• Classify data and identify what may be processed in the cloud
• Map applicable laws, industry requirements, and internal policies
• Determine whether external model services are permitted
• List the intranet systems, files, and databases involved
• Define audit, retention, deletion, and human-approval requirements

The output should be a specific map of data and system boundaries, not a general statement that “data cannot leave the intranet.”

Stage 2: Architecture design

• Select a data center, private cloud, or dedicated environment
• Decide between cloud, local, and hybrid model deployment
• Plan SSO, role-based access, and service accounts
• Design API, UI automation, and other system connections
• Separate development, testing, and production environments
• Define logging, monitoring, backup, and recovery

For an AI employee platform, the control plane, execution layer, skills, models, and data stores should each have a defined location.

Stage 3: Environment and system deployment

• Prepare compute, storage, network, and security resources
• Install the platform and required components
• Configure models and routing policies
• Connect identity, permission, key, and credential services
• Integrate business systems and data sources
• Configure logs, monitoring, and alerts

Running a large model locally requires separate planning for GPUs, model capacity, concurrency, latency, and model operations.

Stage 4: Business and security validation

• Select a representative task with clear boundaries
• Test both normal and exception data
• Validate system operations, document processing, and write-back
• Test unauthorized access, sensitive actions, and human approvals
• Confirm that logs can reconstruct the task and its actions
• Test failures such as model unavailability and system timeouts

Acceptance criteria should cover business results, operational stability, and governance.

Stage 5: Production operations

• Move into production through the organization’s change process
• Assign responsibilities across business, IT, security, and the provider
• Establish access reviews, audit checks, and exception handling
• Manage model, skill, and platform versions
• Monitor task quality, human intervention, and resource usage
• Expand only after the first workload is stable

There is no universal deployment timeline. The schedule depends on system count, data boundaries, model architecture, security reviews, and organizational coordination.

When does SaaS make sense?

SaaS is a practical option when:

• Corporate policy permits the relevant data to be processed through approved cloud services
• The workload does not contain data restricted from cloud processing
• Business systems can be connected through controlled APIs or connectors
• The organization wants the provider to manage more infrastructure and updates
• Distributed teams need access to a shared service
• The immediate goal is to validate a workflow rather than build a private environment

When evaluating SaaS, verify:

• Where data is stored and processed
• Whether data is encrypted in transit and at rest
• How tenants are isolated
• Whether provider personnel can access customer data
• Which third-party models and tools are involved
• How long logs, backups, and customer data are retained
• Whether data can be exported and deleted
• What happens to data when the service ends
• How security incidents are reported and handled

These requirements should appear in technical documentation, data processing agreements, and contracts.

How should an organization choose?

Prioritize on-premises deployment when:

• Regulations, contracts, or internal policies require an enterprise-controlled environment
• The platform must connect to systems that cannot be exposed externally
• Model inputs contain sensitive personal or commercial information
• The organization requires local or proprietary models
• The enterprise must control upgrades, logs, and security policies
• IT, security, and operations teams can support the environment

Prioritize SaaS when:

• The organization permits approved cloud services
• The data can be processed in the proposed cloud environment
• Required systems support secure, standardized integration
• The provider can assume more infrastructure and update responsibilities
• The SaaS edition satisfies the required governance controls

Consider dedicated cloud or hybrid deployment when:

• Workloads have different levels of sensitivity
• Some models or systems must remain within the enterprise environment
• General workloads can use cloud services
• The organization can manage identities, logs, and data flows across environments
• The provider supports the required hybrid architecture

Hybrid deployment is not inherently simpler. It can resolve some data-boundary issues while creating additional complexity across identities, networks, logs, and troubleshooting.

Which deployment options does Laiye support?

Laiye’s digital workforce platform supports cloud and on-premises deployment. Laiye Worker is a desktop-native enterprise AI employee that can work with authorized local files and intranet systems. Laiye Shifu governs agents, permissions, policies, audits, and usage. The skill layer, including Laiye APA, Laiye ADP, Laiye ACX, and third-party skills, provides system operation, document processing, and other specialized functions.

A deployment design should define each layer separately:

• Control plane: Where governance, identities, policies, and audits run
• Execution layer: Which devices, files, and business systems Laiye Worker can access
• Skill layer: Which skills run in the enterprise environment and which use cloud services
• Model layer: Whether models are cloud-hosted, local, or proprietary
• Data layer: Where inputs, outputs, logs, and intermediate data are stored

Laiye cloud services provide encryption in transit and at rest, tenant isolation, access controls, and audit capabilities. For organizations requiring full data sovereignty, Laiye also supports environments where models and data run within the customer-controlled infrastructure. See Laiye security and compliance.

The Laiye Work Execution Platform supports both cloud and on-premises deployment, along with open-source and commercial models, enterprise skills, and existing business systems.

Laiye is the only vendor from China with recognition across the Gartner Magic Quadrants for Robotic Process Automation, Intelligent Document Processing, and Enterprise Conversational AI Platforms. It has also been included in the Gartner Magic Quadrant for Robotic Process Automation for six consecutive years as the only vendor from China.

In 2026, Laiye was named a Visionary in the Gartner® Magic Quadrant™ for Intelligent Document Processing. This marked its second consecutive year in the report, where it was again the only vendor from China. Read the official 2026 Gartner IDP announcement.

Laiye serves more than 3,000 enterprise customers, including over 300 Fortune Global 500 and China Top 500 companies. Laiye has also been recognized as a national-level Specialized and Innovative “Key Little Giant” enterprise.

FAQ

Q: How long does an on-premises deployment take?

A: There is no standard timeline that applies to every project. The schedule depends on system count, network approvals, model deployment, security testing, and business acceptance. Ask providers to define deliverables, dependencies, owners, and schedules for each implementation stage.

Q: Does on-premises deployment require physical servers?

A: Not necessarily. The platform may run in an enterprise data center, private cloud, or approved dedicated environment. GPU requirements depend on whether models run locally, their size, and concurrency. External models reduce local compute needs but change the data and network boundary.

Q: Is SaaS secure enough for enterprise use?

A: The SaaS label alone does not answer that question. Review encryption, tenant isolation, access controls, audit logs, retention, third-party processing, backup and recovery, and exit procedures. Confirm the controls through documentation, contracts, and testing where necessary.

Q: Can a SaaS deployment later move on-premises?

A: Portability depends on the product architecture. Evaluate data export, skill and configuration migration, log retention, interface compatibility, and retesting requirements before selection. Do not assume that a SaaS configuration can move to an on-premises environment without changes.

Q: What is the main risk in deploying an enterprise AI employee platform?

A: A common risk is scaling before data boundaries, permissions, responsibilities, and acceptance criteria are defined. Start with one bounded workload and test normal execution, exceptions, human takeover, and auditability before expanding.

Q: Is hybrid deployment always preferable to on-premises?

A: No. Hybrid deployment works when sensitive and general workloads can be separated clearly, but it increases complexity across identity, networking, logging, and operations. A single deployment model may be easier to govern when data flows and responsibilities cannot be divided cleanly.