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DeepSeek API Service on Vertex AI Model Garden

June 19, 2025 · 1 min read · Sampath Kumar

DeepSeek Vertex AI Model Garden Hero

💡 Originally published on Google Cloud Community on Medium.

TL;DR

Google Cloud Vertex AI Model Garden offers fully managed, one-click deployments and serverless endpoints for open-weight models including DeepSeek-V3 and DeepSeek-R1. By serving DeepSeek through Vertex AI, enterprise engineering teams get Google Cloud IAM access control, private VPC endpoints, automatic scaling, and SLA-backed infrastructure without managing underlying GPU clusters manually.

Why Serve DeepSeek via Vertex AI Model Garden?

While self-hosting DeepSeek on raw compute instances requires substantial infrastructure orchestration (vLLM configuration, GPU quota management, multi-node networking), Vertex AI Model Garden provides a managed abstraction:

graph LR
    subgraph ClientLayer["Developer & Application Layer"]
        App["Client Application"]
        SDK["Google Cloud Python SDK"]
    end

    subgraph VertexCloud["Vertex AI Managed Control Plane"]
        IAM["Cloud IAM & VPC Service Controls"]
        Endpoint["Vertex AI Online Prediction Endpoint"]
        Model["DeepSeek-V3 / R1 (Optimized Serving)"]
    end

    App --> SDK
    SDK --> IAM
    IAM --> Endpoint
    Endpoint --> Model
  • Enterprise Governance: Enforce Cloud IAM permissions and audit logging on every inference call.
  • Zero GPU Scarcity Headaches: Leverage Google Cloud dynamic provisioning and reserved capacity.
  • OpenAI-Compatible & Native SDKs: Seamlessly integrate using either standard Vertex AI prediction APIs or OpenAI-compatible endpoint bridges.

Deploying DeepSeek from Model Garden

You can deploy DeepSeek models via the Google Cloud Console or programmatically with the google-cloud-aiplatform SDK:

from google.cloud import aiplatform

aiplatform.init(project="your-gcp-project-id", location="us-central1")

# Reference DeepSeek model from Vertex AI Model Garden
model = aiplatform.Model.from_pretrained(
    "publishers/deepseek-ai/models/deepseek-v3"
)

# Deploy to an autoscaling GPU-backed endpoint
endpoint = model.deploy(
    machine_type="g2-standard-96",
    accelerator_type="NVIDIA_L4",
    accelerator_count=8,
    min_replica_count=1,
    max_replica_count=4,
)

Performing Inference

Once deployed, query the endpoint with standard payload schemas:

from google.cloud import aiplatform

endpoint = aiplatform.Endpoint("projects/PROJECT_ID/locations/LOCATION/endpoints/ENDPOINT_ID")

payload = {
    "prompt": "Explain the advantages of Mixture of Experts (MoE) architectures in 3 bullet points.",
    "temperature": 0.2,
    "max_tokens": 512,
}

response = endpoint.predict(instances=[payload])
print(response.predictions[0])

Key Enterprise Best Practices

  1. VPC Service Controls: Place your Model Garden endpoints inside private service connect boundaries to prevent data egress.
  2. Dynamic Autoscaling: Configure scale-to-zero or min/max replica boundaries to optimize GPU spend during off-peak hours.
  3. Observability: Monitor request latency, token throughput, and compute utilization directly through Cloud Monitoring dashboards.