23 Chinese IT Vendors Adopt DeepSeek: Survival Guide for Engineers in the AI Era

23 Domestic IT Vendors Integrate DeepSeek

Security Domain

360 : Feb 1, founder Zhou Hongyi offered free cybersecurity protection for DeepSeek; Feb 2, 360 Digital Security announced its security LLM integrated DeepSeek as base model, leveraging 360's security big data and reinforcement learning to train a "DeepSeek version" security LLM for "autonomous driving" security.

安恒信息 : Feb 4, its 恒脑 security vertical LLM integrated DeepSeek, completed training based on DeepSeek-R1, launched first "DeepSeek" security intelligent agent; using 恒脑 agent framework, quickly migrated existing agents; excels in phishing email recognition, data classification, alert analysis.

奇安信 : Feb 5, QAX security LLM fully integrated DeepSeek; applied to threat analysis, security operations, penetration testing, vulnerability management, IAM, anti-phishing, anti-malware/ransomware, DLP, security training, supply chain security; security Q&A performance score improved ~16%.

亚信安全 : Feb 5, built agents on DeepSeek-V3/R1 to correlate massive alerts into event chains; accurately identifies 10+ complex events like continuous HTTP weak-password login, multi-mode scanning, crypto-mining, social engineering.

安胜 : Feb 5, 星盾 platform integrated DeepSeek; customized DeepSeek security LLM gives multi-modal analysis and intelligent reasoning hub; enables closed-loop threat hunting, incident analysis, automated response; upgrades defense to cognitive intelligence with evolving dynamic immunity.

启明星辰 : Feb 6, 安星 agent fully connected with DeepSeek; applied across security operations, threat detection, intelligence, data security; users can select base LLM version, upper-layer apps auto-adjust for optimal effect.

天融信 : Feb 6, 天问 LLM completed DeepSeek integration; empowers security event detection, threat intelligence fusion; deep fusion and co-evolution, not simple stacking; after training, achieved "qualitative change" in multiple scenarios.

安博通 : Feb 7, next-gen AI firewall combined with DeepSeek-R1-Distill-Qwen-32B; accuracy of parameter extraction and inference responses increased, eliminated hallucinations; system efficiency improved 300% on same hardware.

Cloud Computing Domain

华为云 : Feb 1, SiliconFlow and 华为云 jointly launched DeepSeek R1/V3 inference service on 昇腾 cloud; self-developed acceleration engine delivers performance on par with high-end GPUs; Feb 4, DeepSeek-R1/V3/V2/Janus-Pro on 昇腾 community, one-click deployment.

腾讯云 : Feb 2, HAI high-performance app service supports one-click DeepSeek-R1 deployment; developers access in 3 minutes, skipping GPU purchase, driver install, network/storage/env/framework setup, model download.

移动云 : Feb 3, full DeepSeek version coverage, all sizes, full features; integrated with 智算 platform, code LLM, elastic compute, AI-native storage, COCA compute-native platform; AI cloud PC upgraded with DeepSeek kernel; customized compute for DeepSeek-R1 for internet innovation and academic research.

联通云 : Feb 3, 星罗 platform adapted multi-spec DeepSeek-R1 on domestic/mainstream compute; supports private/public cloud; 270+ backbone cloud pools pre-deployed; integrated with cloud desktop, coding assistant; security via anti-DDoS and 元景 security LLM.

京东云 : Feb 4, launched DeepSeek-R1/V3; supports public cloud online deployment and private/hybrid instances; one-click deployment via 言犀 AI development platform's AI asset marketplace.

天翼云 : Feb 6, 息壤 smart compute platform first to achieve full-stack domestic inference for DeepSeek-R1/V3; marks new phase for domestic AI ecosystem, providing high-performance, secure, controllable intelligent compute base.

Chip Domain

沐曦 : Feb 2, Gitee AI and 沐曦 launched full DeepSeek R1 Qwen-distilled models on 国产 沐曦曦云 GPU; smaller distilled models lower enterprise deployment cost; full stack from chip to platform, compute to model, all domestic R&D and manufacturing.

摩尔线程 : Feb 4, based on self-developed full-function GPU, open-source + self-developed dual-engine, quickly deployed DeepSeek distilled model inference; also supports MTT S80 and S4000 for DeepSeek-R1 distilled deployment.

天数智芯 : Feb 4, with Gitee AI completed DeepSeek R1 adaptation in one day; launched services including DeepSeek R1-Distill-Qwen-1.5B/7B/14B; demonstrates mature software ecosystem and general GPU advantages: easy, out-of-the-box, zero-cost migration.

海光信息 : Feb 5, technical team successfully adapted DeepSeek V3 and R1.

AI's Core Impact on IT Industry

Technical Innovation

Intelligent Development and Operations AI is reshaping software development and IT operations. Tools like GitHub Copilot and Amazon CodeWhisperer auto-complete code, generate unit tests, even design architectures, greatly boosting dev efficiency. In operations, AI analyzes logs to predict server failures; Netflix's fault prediction system reduced downtime by 30%.

Automated Testing and Security AI-driven testing tools auto-generate test cases and identify boundary conditions, raising coverage. In security, Darktrace's Antigena system monitors network anomalies in real time and automatically blocks attacks.

Career Transformation

Skill Reconstruction IT practitioners must shift from traditional coding to "AI collaborative development", mastering prompt engineering, model fine-tuning. New roles emerge: AI ethics consultant, MLOps engineer, data annotation specialist; traditional developers need ability to debug AI-generated code.

Low-Code/No-Code Proliferation Platforms like Microsoft Power Apps (visual builder, data connectors, templates), Zoho Creator (form builder, data views, workflow automation, drag-and-drop), and FineBI (drag-and-drop analytics, self-service modeling, real-time anomaly analysis) shorten development cycles and empower business users.

Industry Ecology

Cloud and AI Infrastructure Competition AWS, Azure, Google Cloud compete for AI training market; 2023 global AI cloud market reached $250B. Edge computing drives local AI deployment, e.g., NVIDIA Jetson series.

How IT Professionals Should Respond

1. Data Engineer

Technical Upskilling : Enhance data cleaning, labeling, analysis for high-quality AI training data; master AI data pipeline design for efficient training/inference.

Career Transition : Move from traditional data processing to AI data processing; apply ML to optimize data flows; become data scientist by learning ML/DL algorithms.

Collaboration : Work closely with data scientists to ensure data quality; participate in data governance for compliance and security.

2. Software Developer

Skill Upgrade : Learn AI frameworks (TensorFlow, PyTorch) to integrate models (e.g., recommendation, image recognition); master AI-assisted tools (Copilot, CodeWhisperer) for code generation/debugging.

Career Transition : Shift to AI application development (NLP, CV); become AI ethics expert for compliance/privacy review.

Innovation Mindset : Explore AI-driven solutions (ML for algorithm optimization); contribute to AI open-source projects.

3. System Architect

Technical Upskilling : Design AI-enabled architectures (cloud/edge/AI fusion, distributed agent collaboration); optimize compute resources (FP8 mixed precision, low-level communication optimization).

Strategic Planning : Track emerging tech (quantum computing, dedicated AI chips) for future-proof architectures; align AI with business needs for strategic systems.

Team Collaboration : Partner with AI engineers and data scientists to optimize architecture for model efficiency; mentor team on AI awareness and code optimization.

4. Data Scientist

Technical Deepening : Master advanced ML/DL algorithms (MoE, MLA) for model performance; strengthen data cleaning, labeling, analysis.

Cross-Domain Knowledge : Understand business scenarios (e.g., financial risk prediction); grasp AI ethics for fairness/transparency.

Innovation & Research : Join AI research projects, publish papers; explore new domains (multimodal like GPT-4V, embodied intelligence).

5. Cybersecurity Expert

Technical Upskilling : Apply AI for anomaly/malicious attack detection; optimize security models for better detection/response.

Cross-Domain Collaboration : Integrate security into AI systems with dev teams; participate in AI security standard-setting.

6. IT Operations Personnel

Technical Upskilling : Adopt AI-driven ops tools (ML for failure prediction); learn automation (AI for config/monitoring).

Career Transition : Move to intelligent ops (ML for performance optimization); become ops data analyst for reliability/performance gains.

Collaboration : Work with dev teams for efficient AI system operation; engage in continuous improvement via AI.

7. Test Engineer

Technical Upskilling : Use AI-driven test tools (auto test case generation, boundary detection); learn to test AI models for accuracy/reliability.

Career Transition : Shift to AI testing (ML for test process optimization); become test data analyst for efficiency gains.