Engineering Traffic Management for DeepSeek: Cloud‑Native Deployment Strategies

Background

DeepSeek‑related demands fall into two categories: (1) the official APP/Web service often fails to return results, prompting cloud providers and hardware/software vendors to offer full‑version or distilled API + compute services, as well as local deployment options; (2) enterprises begin to call DeepSeek APIs to build large‑model applications, focusing on construction efficiency and stability.

We have previously published many cloud and local deployment solutions for the first demand; this article addresses the second demand by discussing engineering solutions at the traffic‑management layer.

DeepSeek Deployment Options

Since DeepSeek released the full DeepSeek‑R1 model weights (671 B parameters), companies can deploy the model within their own network, keeping the entire AI data pipeline under control.

Model weight download : available via ModelScope (https://modelscope.cn/). The full model requires substantial GPU resources; quantization (int8/int4) or distilled models can reduce requirements.

Deployment methods (Alibaba Cloud): PAI, GPU + ACK, ModelScope + FC, Spring AI Alibaba + Ollama. Links are provided for each method.

PAI deployment – example using DeepSeek‑R1‑Distill‑Qwen‑7B via Model Gallery (no code required).

Bailei deployment – API with free token quota for DeepSeek‑R1 and DeepSeek‑V3.

GPU server deployment – install vLLM and Open WebUI to host the model.

Serverless deployment – use CAP to deploy Ollama and Open WebUI as FC functions.

Local deployment – add spring-ai-ollama-spring-boot-starter and inject ChatClientBean to interact with the model.

Engineering Challenges in Large‑Model Applications

Similar to web‑app deployment, large‑model services face traffic bursts, overload, network latency, security/compliance, quota and cost control, and release‑induced failures. The architecture differs from traditional web apps, requiring tailored solutions.

AI Gateway as the Standard Solution

The AI gateway registers deployed models as services, exposing APIs with built‑in rate limiting, authentication, and statistics. It addresses the following needs:

Fallback for limited concurrency : route failed DeepSeek‑R1 requests to smaller distilled models (e.g., DeepSeek‑R1‑Distill‑Qwen‑32B) or online APIs like DeepSeek‑V3 or Qwen‑max.

Content safety : integrate Alibaba Cloud content‑security service to block unsafe responses; example JSON response shown.

API authorization and quota control : issue API keys per consumer, enforce permissions and usage limits, and monitor token consumption.

Gray‑release traffic shifting : proportionally route traffic (e.g., 90 % to OpenAI, 10 % to DeepSeek) and adjust without code changes.

Caching of frequent requests : enable cache for common prompts (greetings, product queries) to reduce inference cost.

Observability and Advanced Features

The gateway provides rich metrics for content safety, rate limiting, and caching. Combined with SLS, it offers semantic vector indexing, topic clustering, intent and sentiment recognition, and quality evaluation to continuously improve model performance.

References

[1] DeepSeek‑R1‑Distill‑Qwen‑32B: https://modelscope.cn/models/deepseek-ai/DeepSeek-R1-Distill-Qwen-32B