Which deployment model is right for your enterprise?
Private deployment, hybrid model deployment, seamless ERP and CRM integration and support for complex permission systems: we fit AI into your enterprise environment, with more flexible implementation at a lower cost.
Start with your business requirements, then choose a deployment model
Weigh your rollout timeline, data residency, network boundaries and operations capacity. Each model suits different conditions: choose one that supports your business today and that you can sustain.
Want to start fast? Choose SaaS
For teams that want to trial and roll out quickly with less infrastructure investment. Once you have confirmed that cloud data processing and network access meet your company's requirements, you can start with real business tasks.
SaaS: get started fast — Use the cloud platform to launch team pilots and business applications quickly, with less infrastructure preparation and day-to-day operations effort.
Need your own cloud environment? Choose dedicated cloud
For companies that need dedicated resources, network isolation or a specified cloud environment. Confirm the region, capacity and how systems will connect, then agree on resource investment and operations responsibilities.
Dedicated cloud: an isolated environment — Plan dedicated cloud resources, network isolation and data residency to your requirements, and set operating and management boundaries with the delivery team.
Must stay on your intranet? Choose on-premises
For companies with firm requirements for data residency, intranet access or infrastructure control. You need suitable hardware and operations capacity, and should confirm the data boundaries for model calls and external tools.
On-premises: full control — Run on your own infrastructure, connect internal systems and private models, and keep control of your data, network and runtime environment.
Key capabilities
From selection to go-live
Assess the deployment model against data sensitivity, network requirements, concurrency and operations capacity, then complete capacity planning, environment validation and go-live handover.
Communicate with our experts to clarify AI application needs and business scenarios.
02
Environment Preparation
Set up the necessary hardware and software environment to ensure the system supports AI model deployment.
03
Product Deployment
Configure models, permissions and system connections for the chosen SaaS, dedicated cloud or on-premises option.
04
Testing
Perform comprehensive testing on the deployed AI model and system to ensure stability and performance.
05
Training & Delivery
Provide training on the system and AI model usage, completing the final delivery.
06
Maintenance & Support
Offer ongoing maintenance services and technical support to ensure the long-term stable operation of the AI system.
Three service models: choose the one that fits your needs
Compare the runtime environment, resource investment and management responsibility. The deployment model alone does not set the level of security; configuration, processes and ongoing operations do.
SaaS
Best for
Fast start, team pilots and everyday business applications
Where it runs
A cloud service environment provided by the platform
Preparation before launch
Confirm accounts, network, data policy and subscription plan
Data and system connections
Check cloud data residency and the authorization requirements for connecting systems
Operations and updates
The provider maintains the platform; you manage accounts, content and usage policies
How to assess the investment
Subscription, usage and business integration costs
Dedicated cloud
Best for
Dedicated cloud resources, network isolation or a specified cloud environment
Where it runs
A dedicated cloud environment configured as agreed
Preparation before launch
Confirm cloud resources, region, network connectivity and capacity
Data and system connections
Plan the dedicated network and the connections between the cloud and enterprise systems
Operations and updates
Responsibilities for cloud resources, platform maintenance and updates are set by agreement
How to assess the investment
Also weigh dedicated cloud resources, implementation and operations costs
On-premises
Best for
Firm requirements for intranet access, data residency or your own infrastructure
Where it runs
On-premises infrastructure owned or designated by your company
Preparation before launch
Prepare hardware, network, model services and operations staff
Data and system connections
Plan intranet connections and check how data flows to external models and tools
Operations and updates
You run the environment; platform support and upgrades are arranged by agreement
How to assess the investment
Also weigh hardware, model compute, implementation and ongoing operations
Management requirements to settle, however you deploy
Data Privacy and Security
Agree on data residency, isolation, backup and key management together, and check how data flows to models and external tools.
Enterprise Autonomy
Define how members, authentication and access permissions are managed, and agree with the provider on who is responsible for what in each service model.
High Availability and Performance
Supports a high-availability architecture with horizontal scalability, allowing you to autonomously adjust capacity based on your business needs.
Customization
Configure your AI services with a dedicated domain, company logo, brand colors, and more to showcase your unique brand identity.
Explore what's possible with GPTBots.
Contact us today to get a tailored deployment plan that fits your business needs.
