Using LLMs as Microservices in Application Development

Hot take: The future of AI app development isn't about bigger models. It's about better orchestration. We're entering the era of multi-modal, agentic apps—but here's the twist: the winners won't be those stacking the largest LLMs. They'll be the teams that know how to compose the minimum viable model with just the right tool for the job. Here's what that looks like in practice: • A small vision model (Florence-2) for extracting screen context • A fast LLM (Llama 3.1 8B) for parsing user intent • A retrieval engine tuned to your business logic • A thin agent layer (LangGraph) to coordinate them all !!This isn't AI as monolith. It's AI as distributed system design.!! The new AI app stack looks like: Development: Containerized model serving + CDE for consistent environments -> Runtime: Event-driven microservices + lightweight agents + model orchestration -> Deployment: Each component scaled independently, swapped without downtime Example: Instead of throwing GPT-4o at every task, you might route: • Simple classification → local quantized model (100ms) • Complex reasoning → cloud LLM (2s) • Tool execution → specialized agents All coordinated through container-based orchestration 🧠 The core question becomes: What's the smallest, fastest, most reliable way to accomplish each task? This is where containers shine—packaging each AI component with its dependencies, making it trivial to swap models, scale components independently, and maintain consistency from local dev to production. AI app development is becoming a full-stack discipline. Model worship is out. Systems thinking + containerization is in. #AI #LLMs #AgenticAI #Containers #CloudNative #AIEngineering #LangGraph #ModelOrchestration

Have you observed lately that many agentic AI applications fail because they rely directly on raw LLM calls without a gateway to handle context routing, model orchestration, caching, rate-limiting, and fallback strategies? You must need an LLM gateway or a layer of such kind that acts as a middleware layer that sits between your application and multiple LLM providers. Hence, an LLM gateway is essential for building scalable, safe, and cost-effective agentic AI applications in the enterprise. An LLM gateway essentially functions as a central control panel to orchestrate workloads across models, agents, and MCP servers (the emerging protocol connecting AI agents to external services). Core functions and concepts of an LLM gateway include: ➤ Unified Entry Point: It provides a single, consistent interface (API) for applications to interact with multiple foundational model providers. ➤ Abstraction Layer: It hides the complexity and provider-specific quirks of working directly with individual LLM APIs. This means developers can use the same code structure regardless of which model they call. ➤ Traffic Controller: It intelligently routes requests to the most suitable LLM based on specific criteria like performance, cost, or policy. ➤ Orchestration Platform: It improves the deployment and management of LLMs in production environments by handling security, authentication, and model updates from a single platform. LLM gateways are becoming essential, particularly for enterprises building production-ready and scalable agentic AI applications, because they address multidimensional challenges related to vendor lock-in, complexity, costs, security, and reliability. Know more about LLM gateways through below resources: https://lnkd.in/gimgJ4hD https://lnkd.in/gawvkzGw https://lnkd.in/g-377ESP

This is how Adyen built an LLM-based ticket routing + support agent copilot to increase the speed of their support team. - Adyen used LangChain as the primary framework. The entire setup runs on Kubernetes for flexibility and scalability. - First, the ticket routing system uses an LLM to automatically direct support tickets to the right agents based on content analysis. This improved the accuracy in ticket allocation compared to their human operators. - For the support agent copilot, Adyen built a document management and retrieval system. It uses vector search to retrieves relevant docs from their internal support documentation and suggests answers to support agents, which cuts down the response time significantly. - The architecture is modular so their existing microservices are integrated easily too. Link to article: https://lnkd.in/gqUZZ6nd #AI #RAG #LLMs