Top 10 tools to build and deploy your next GenAI Application

Introduction: The New Era of AI Operations

The AI landscape has evolved dramatically with the rise of large language models (LLMs), retrieval-augmented generation (RAG), and multimodal AI systems. Traditional MLOps frameworks struggle to handle:

• Billion-parameter LLMs with unique serving requirements
• Vector databases that power semantic search
• GPU resource management for cost-effective scaling
• Prompt engineering workflows that require version control
• Embedding pipelines that process millions of documents

In this article I will be providing a blueprint on different development tools for different components of building an AI/MLOps infrastructure capable which supports the recent advanced AI applications.

Core Components of AI-Focused MLOps

1. LLM Lifecycle Management
2. Vector Database & Embedding Infrastructure
3. GPU Resource Management
4. Prompt Engineering Workflows
5. API Services for AI Models

1. LLM Lifecycle Management

a) Tooling Stack:

• Model Hubs: Hugging Face, Replicate
• Fine-tuning: Axolotl, Unsloth, TRL
• Serving: vLLM, Text Generation Inference (TGI)
• Orchestration: LangChain, LlamaIndex

b) Key Considerations:

• Version control for adapter weights (LoRA/QLoRA)
• A/B testing frameworks for model variants
• GPU quota management across teams

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2. Vector Database & Embedding Infrastructure

Database Choice

• Pinecone
• Weaviate
• Milvus
• PGVector
• QDrant

Embedding Pipeline Best Practices:

1. Chunk documents with overlap (512-1024 tokens)
2. Batch process with SentenceTransformers
3. Monitor embedding drift with Evidently AI

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3. GPU Resource Management

Deployment Patterns:

Optimization Techniques:

• Quantization (GPTQ, AWQ)
• Continuous batching (vLLM)
• FlashAttention for memory efficiency

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4. Prompt Engineering Workflows

MLOps Integration:

• Version prompts alongside models (Weights & Biases)
• Test prompts with Ragas evaluation framework
• Implement canary deployments for prompt changes

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5. API Services for AI Models

Production Patterns:

Essential Features:

• Automatic scaling
• Request batching
• Token-based rate limiting

End-to-End Reference Architecture

Below will be the whole infrastructure diagram for an AIOps Infrastructure, feel free to take a pause to go over as it could be overwhelming :)

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Final Takeaways

Quick lessons for production,

• Separate compute planes for training vs inference
• Implement GPU-aware autoscaling
• Treat prompts as production artifacts
• Monitor both accuracy and infrastructure metrics

This infrastructure approach enables organizations to deploy AI applications that are:

• Scalable (handle 100x traffic spikes)
• Cost-effective (optimize GPU utilization)
• Maintainable (full lifecycle tracking)
• Observable (end-to-end monitoring)

References for Further Learning

• Hugging Face Production Guide
• LoRA Fine-Tuning Tutorial
• MLOps Community Resources
• PgVector vs PineCone
• LlamaIndex RAG Best Practices
• vLLM
• NVIDIA TensorRT-LLM Tutorial
• Prometheus for ML Monitoring

Thanks for reading— I hope this guide helps you tackle those late-night MLOps fires with a bit more confidence. If you’ve battled AI infrastructure quirks at your own, I’d love to hear your war your solutions! :)