Your First LLM API on Kubernetes: From Model to Curl Request

📰 Dev.to · Pawan Kumar

Learn to deploy and expose a large language model API on Kubernetes, enabling scalable and secure AI services

intermediate Published 25 Jun 2026
Action Steps
  1. Deploy Qwen2.5-1.5B-Instruct on a Kubernetes GPU node using vLLM
  2. Expose the model as an OpenAI-compatible API
  3. Configure the API for secure access
  4. Test the API using a curl request
  5. Monitor and optimize the API performance on the Kubernetes cluster
Who Needs to Know This

DevOps and AI engineers benefit from this knowledge to deploy and manage LLM APIs, while product managers can leverage this to integrate AI capabilities into their products

Key Insight

💡 Kubernetes enables scalable and secure deployment of LLM APIs, making it easier to integrate AI into products and services

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🚀 Deploy your first LLM API on Kubernetes! 🤖

Key Takeaways

Learn to deploy and expose a large language model API on Kubernetes, enabling scalable and secure AI services

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