Knowledge | 6 Main Enterprise Deployment Modes for DeepSeek

International Data Corporation (IDC) recently published an article titled Behind DeepSeek’s Surge: Potential Impacts on the Large-Scale Model/Generative AI Market Ecosystem Attract Attention, stating:

“The deployment process of large-scale models must simultaneously meet stringent requirements for high concurrency and low latency, while comprehensively considering multiple factors such as data security, privacy protection, resource scalability, and system maintenance. DeepSeek has introduced various deployment mode licenses, challenging the primary commercialization approaches of global large-scale model technology providers. The current offerings include cloud deployment, local/intranet deployment, edge deployment, hybrid deployment, containerized/microservices deployment, and federated deployment modes.”

From this, it’s clear that for enterprise users, DeepSeek’s large-scale model deployment options primarily consist of the above six types. So, what are the characteristics of these six modes, and which scenarios are they best suited for?

1. Cloud Deployment:

The DeepSeek large-scale model is deployed on public or private clouds, leveraging the infrastructure and resources of cloud providers. Applicable scenarios:

1. Elastic Demand: Resources need to be dynamically adjusted based on load.

2. Rapid Scaling: Business growth is fast, requiring quick system expansion.

3. Cost Optimization: Aims to reduce IT costs through a pay-as-you-go model.

2. Local/Intranet Deployment:

The DeepSeek large-scale model is deployed on internal enterprise servers or data centers, with data and applications running entirely within the enterprise intranet. Applicable scenarios:

1. Data Sensitivity: High data security requirements demand full control over data.

2. Compliance Requirements: Must meet specific industry or regional regulatory standards.

3. Network Restrictions: Intranet environments cannot connect to external networks.

3. Edge Deployment:

The DeepSeek large-scale model is deployed on edge nodes close to the data source, reducing data transmission latency. Applicable scenarios:

1. Low Latency Needs: Scenarios like IoT or real-time monitoring requiring rapid responses.

2. Limited Bandwidth: High data transmission costs or constrained bandwidth make edge computing ideal to reduce uploads.

3. Offline Operation: Must function normally in unstable or offline network conditions.

4. Hybrid Deployment:

Combines cloud and local deployment, with parts of the DeepSeek large-scale model system on the cloud and parts on-premises. Applicable scenarios:

1.Flexible Needs: Some data requires local processing, while other parts need cloud processing.

2. Transition Phase: Serves as a transitional solution when migrating from local to cloud setups.

3. Disaster Recovery: Local and cloud setups act as mutual backups to enhance system reliability.

5. Containerized/Microservices Deployment:

The DeepSeek large-scale model system is split into multiple microservices, deployed and managed using container technologies (e.g., Docker). Applicable scenarios:

1. Agile Development: Requires rapid iteration and release of new features.

2. Resource Isolation: Different services need independent runtime environments to avoid interference.

3. Elastic Scaling: Allows independent scaling of specific services based on demand.

6. Federated Deployment:

Multiple independent DeepSeek large-scale model systems collaborate via a federated protocol, sharing data and resources while remaining autonomous. Applicable scenarios:

1. Cross-Organizational Collaboration: Multiple organizations need to share data while maintaining independent management.

2. Data Privacy: Requires data sharing while protecting privacy.

3. Distributed Computing: Needs distributed data processing across multiple nodes, such as federated learning.

From this, it can be generally observed:

Cloud Deployment: Achieves elastic scaling and cost optimization via cloud providers.

Local/Intranet Deployment: Ensures full data control through on-premises data centers.

Edge Deployment: Delivers low latency and real-time processing via edge nodes.

Hybrid Deployment: Combines local and cloud setups for flexibility and disaster recovery needs.

Containerized/Microservices Deployment: Enables agile development and resource isolation through container technology and microservices architecture.

Federated Deployment: Facilitates cross-organizational collaboration and data privacy protection via federated protocols and distributed architecture.

Enterprise users can select the appropriate deployment mode based on specific needs to optimize system performance and cost.

On February 2, ZStack Cloud Computing announced that its AI Infra platform, ZStack AIOS, fully supports the privatized deployment of DeepSeek V3, R1, and Janus Pro models. It is compatible with a variety of domestic and international CPUs/GPUs, including Hygon, Ascend, NVIDIA, and Intel.

As a DeepSeek enterprise-grade expert, ZStack AIOS not only fully supports the above six DeepSeek enterprise deployment modes but also, in the fifth mode, extends beyond containerized/microservices deployment to support virtual machine and bare-metal deployments.

As a next-generation AI Infra platform, ZStack AIOS was featured in the report for its all-in-one advantages, including computing resource scheduling, training and inference for various large models like DeepSeek, and AI application service development. It helps enterprise users improve heterogeneous hardware utilization and reduce AI costs; accelerate multi-model collaboration to optimize AI performance; and implement full-domain metering and billing for self-service AI, thereby speeding up privatized enterprise-grade AI applications.