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What are the deployment options for AI application platforms?

Created on 2025-07-29 18:35:25
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AI application platforms can be deployed in several ways, depending on factors like scalability, cost, control, and latency requirements. Here are the main deployment options with explanations and examples:

  1. On-Premises Deployment

    • The AI platform is installed and run on a company’s own servers and infrastructure.
    • Pros: Full control over data, security, and customization.
    • Cons: High upfront costs, maintenance burden, and limited scalability.
    • Example: A financial institution deploying an AI fraud detection system on its own data center for compliance reasons.

  2. Public Cloud Deployment

    • The AI platform runs on a third-party cloud provider’s infrastructure (e.g., compute, storage, and AI services).
    • Pros: Scalable, cost-efficient (pay-as-you-go), and fast to deploy with built-in AI tools.
    • Cons: Dependency on the cloud provider’s security and potential latency issues.
    • Example: A startup using Tencent Cloud TI Platform to train and deploy machine learning models without managing hardware.

  3. Hybrid Deployment

    • Combines on-premises and cloud resources, allowing sensitive data to stay on-site while leveraging cloud scalability.
    • Pros: Balances security and flexibility.
    • Cons: Complex to manage and integrate.
    • Example: A healthcare provider processing patient data locally but using Tencent Cloud AI services for model training in the cloud.

  4. Edge Deployment

    • AI models are deployed on edge devices (e.g., IoT devices, local servers) to process data locally with low latency.
    • Pros: Real-time processing, reduced bandwidth use.
    • Cons: Limited compute power compared to the cloud.
    • Example: A smart factory using edge AI for real-time quality control with Tencent Cloud IoT Edge for lightweight model inference.

  5. Serverless Deployment

    • AI applications run on demand without managing servers, using functions-as-a-service (FaaS).
    • Pros: Auto-scaling, cost-effective for sporadic workloads.
    • Cons: Cold starts and limited runtime control.
    • Example: A chatbot service using Tencent Cloud SCF (Serverless Cloud Function) to handle user queries dynamically.

For AI workloads, Tencent Cloud offers services like TI Platform (AI model training & deployment), TKE (Kubernetes for AI workloads), and Cloud GPU (for accelerated computing) to support flexible deployment.