Deploying even a small AI application today in a production environment is no small feat. It requires not only knowledge of traditional application deployment practices but also expertise in the complexities of GPU management, model training, model serving, and ensuring scalability. Each step in the process demands significant time, expertise, and resources. Let’s break down the journey:
1. Deploy Everything to a Secure, Cost-Effective Public Cloud
The application, model serving layer, and database must be deployed to the public cloud. This involves:
- Selecting the right cloud provider.
- Ensuring compliance with security best practices (e.g., encryption, role-based access control).
- Optimizing for cost to avoid unexpected bills.
2. Train Your Models (Optional)
Training a model is not required if you plan to use existing model. If you have domain specific data and you plan to train the model, it will involve below step:
- Data Preparation: Gathering and cleaning large datasets.
- Model Selection: Deciding whether to use pre-trained models or fine-tune them for your specific needs.
- Infrastructure Setup: You’ll need access to high-performance GPUs or TPUs to train these models effectively.
Challenges:
Deploying GPU nodes in the cloud can be tricky.
- GPU Selection: Choosing the right GPU depends on workload requirements and cloud offerings. High-performance GPUs like NVIDIA A100 are ideal for intensive training, while T4 GPUs are more cost-effective for lighter tasks. Cloud providers like AWS, GCP, and Azure offer different GPU models, with varying regional availability and pricing structures, making research critical.
- Spot Instances: While cost-effective, spot instances can be interrupted unexpectedly, requiring automation to fallback to on-demand instances. Tools like AWS Auto Scaling Groups or Kubernetes node pools can help manage this but demand setup and fine-tuning expertise.
- Base Machine Setup: Instances need proper images preloaded with GPU drivers and frameworks. AWS offers Deep Learning AMIs, GCP has Deep Learning VM Images, and Azure provides Data Science VMs. Even with these, further customization may be needed for your specific needs.
- GPU Observability: Unlike CPU monitoring, GPU metrics like utilisation and memory usage require additional setup. Tools like NVIDIA DCGM or custom Prometheus exporters can help, but they add complexity and potential cost.
3. Serve Your Model
Either you have model from your training from (1) or you are begin with existing model.
The model must be made accessible to applications through a serving layer. For instance, Ray Serve is a popular framework for serving AI models.
Challenges:
- Scalability: Ensuring the serving layer can handle fluctuating traffic in a cost-effective manner.
- Security: Protecting the endpoints from unauthorized access and ensuring secure communication.
4. Deploy a Vector Database for the AI / LLM App
For RAG or other applications involving embeddings, you’ll need a vector database to store and query high-dimensional data effectively.
Challenges:
- Choosing and Configuring vector database for low-latency, high-throughput scenarios.
5. Custom Backend Integrations
Ray Applications may need databases, caching layers for storing state for stateful models, or additional data for Retrieval-Augmented Generation usecases.
Challenges:
- Setting up secure storage for documents and other session data.
- Ensuring fast and secure retrieval of relevant data during inference.
6. Deploy Logs and Metrics for Monitoring and Troubleshooting for your AL / LLM Apps
To ensure smooth operation and fast debugging, you need to:
- Integrate logging and monitoring tools.
- Set up dashboards and alerts for critical metrics.
- Build a troubleshooting pipeline for issues in production.
Does the World Have to Be So Complex?
Building an AI/LLM app from scratch is undeniably complex. The process requires expertise in multiple domains: cloud infrastructure, machine learning, application development, security, and cost optimization. The sheer effort can be daunting, especially for teams focused on delivering value rather than solving technical challenges. Your team is adding highest value when spending time building features for your cusotmer. Rest is all Heavy lifting that doesn’t move the needle.
Is There a 2025 Way of Doing Things?
Yes, there is—Kapstan.
Kapstan simplifies and streamlines the deployment of AI/LLM-powered applications:
- Effortless GPU Integration: With just a single click, configure your application to run on a GPU-enabled node. No need to manage complex YAML files. Simply select the "Run on GPU" checkbox, and Kapstan handles the rest.
- Out-of-the-box Ray Serve Support: Kapstan supports Ray Serve applications natively, eliminating the need to write YAML files for deployment. Focus on scaling and serving your AI models while Kapstan automates the underlying infrastructure setup.
- Out-of-the-box Vector Database Integration: Seamlessly integrate with popular vector databases without the hassle of configuration. Kapstan simplifies embedding storage and retrieval for high-performance AI applications.
- Scalable Framework for RAG Use Cases: Whether you're working on retrieval-augmented generation (RAG) or any other AI-driven solution, Kapstan provides ability to deploy secure and scalable databases, caches, object stores, queues and other cloud resources.
- Rapid Application Development: Kapstan accelerates your development process by providing seamless cycle of coding, deploying, testing, debugging, and fixing—so you can focus on building features, not managing infrastructure.
- Secure, Cost-effective Cloud Deployment: Deploy your application to a secure cloud environment with optimised cost control, ensuring both safety and efficiency. Kapstan also supports auto-scaling out of the box, leveraging tools like HPA (Horizontal Pod Autoscaler) or KEDA (Kubernetes Event-driven Autoscaler) to dynamically manage workloads.
- Built-in Observability and Monitoring: Kapstan provides built-in observability, allowing you to monitor your application's state, view real-time logs, and search through historical logs—all from a single, intuitive interface.
With Kapstan, you can skip the complexity with regards to deploying AI / LLM apps and focus on building impactful applications that truly make a difference. By automating the heavy lifting of infrastructure management, Kapstan allows you to channel your energy into innovation rather than operational headaches. Whether you're scaling cutting-edge AI/LLM solutions, managing resource-intensive workloads, or ensuring seamless integration with cloud-native tools, Kapstan is designed to make your journey effortless and efficient.
Say goodbye to the labyrinth of AI deployment, with its endless YAML files, manual configurations, and maintenance burdens. Instead, embrace a streamlined, scalable, and secure approach that empowers your team to iterate faster, deploy confidently, and deliver exceptional results. Step into the future with Kapstan—where your vision meets simplicity and your AI dreams come to life without compromise.
