Building Resilient Architecture for AI Travel Apps

Designing #AI applications and integrations requires careful architectural consideration. Similar to building robust and scalable distributed systems, where principles like abstraction and decoupling are important to manage dependencies on external services or microservices, integrating AI capabilities demands a similar approach. If you're building features powered by a single LLM or orchestrating complex AI agents, a critical design principle is key: Abstract your AI implementation! ⚠️ The problem: Coupling your core application logic directly to a specific AI model endpoint, a particular agent framework or a sequence of AI calls can create significant difficulties down the line, similar to the challenges of tightly coupled distributed systems: ✴️ Complexity: Your application logic gets coupled with the specifics of how the AI task is performed. ✴️ Performance: Swapping for a faster model or optimizing an agentic workflow becomes difficult. ✴️ Governance: Adapting to new data handling rules or model requirements involves widespread code changes across tightly coupled components. ✴️ Innovation: Integrating newer, better models or more sophisticated agentic techniques requires costly refactoring, limiting your ability to leverage advancements. 💠 The Solution? Design an AI Abstraction Layer. Build an interface (or a proxy) between your core application and the specific AI capability it needs. This layer exposes abstract functions and handles the underlying implementation details – whether that's calling a specific LLM API, running a multi-step agent, or interacting with a fine-tuned model. This "abstract the AI" approach provides crucial flexibility, much like abstracting external services in a distributed system: ✳️ Swap underlying models or agent architectures easily without impacting core logic. ✳️ Integrate performance optimizations within the AI layer. ✳️ Adapt quickly to evolving policy and compliance needs. ✳️ Accelerate innovation by plugging in new AI advancements seamlessly behind the stable interface. Designing for abstraction ensures your AI applications are not just functional today, but also resilient, adaptable and easier to evolve in the face of rapidly changing AI technology and requirements. Are you incorporating these distributed systems design principles into your AI architecture❓ #AI #GenAI #AIAgents #SoftwareArchitecture #TechStrategy #AIDevelopment #MachineLearning #DistributedSystems #Innovation #AbstractionLayer AI Accelerator Institute AI Realized AI Makerspace

Portkey doesn’t use any other proxy service under the hood. Why? When building an AI Gateway, conventional wisdom would suggest extending existing API infrastructure. After all, why reinvent the wheel when proven solutions exist? We explored this path feverishly in the beginning, but discovered they just don't work. 🔄 AI requests push traditional infrastructure to its limits. Think - streaming calls, high latency, long request-response windows, and more. A blocking architecture would struggle with throughput at scale, leading to higher infrastructure costs and degraded performance. Irregular traffic spikes to different AI providers can overwhelm these systems. By building our own architecture, we now manage 1k rps with all features enabled easily on a machine with 2 vCPUs - performance that would require 5x the resources with traditional solutions. 📊 Traditional API monitoring operates in binaries - success (2XX) or failure (4XX/5XX). AI introduces a spectrum of outcomes where an API can succeed but the response could be problematic. Existing monitoring solutions struggle to capture these nuances, leading to missed issues and false positives. Teams end up flying blind on actual AI performance. Our observability layer now tracks 50+ AI-specific metrics per request, giving teams real-time insights into hallucinations, token optimization, and response quality - things traditional API metrics never considered. 🌐 Legacy API infrastructure was optimized for ingress & collocated services. But AI providers are distributed across regions and cloud providers. You need the AI gateway to be lightweight enough to run across regions, to minimize roundtrip latencies. We achieved this by building a compact ~120kb gateway that runs entirely on the edge! 🔌 AI infrastructure requires deep integration with specialized components - evals, guardrails, security policies, and provider-specific optimizations. We'd have ended up building plugins for plugins, which is just absurd. Today, our purpose-built plugin architecture has enabled teams to deploy custom guardrails and security policies in production within hours instead of weeks, while maintaining enterprise-grade reliability. 💰 AI infrastructure costs follow fundamentally different patterns than traditional API costs. Existing solutions focus on bandwidth and compute optimization, missing the biggest cost factor in AI - token usage and model selection. Our integrated cost optimization has helped teams reduce their AI spending by 30-50% through automatic token optimization, smart routing, and dynamic model selection - all without any code changes. Today, our infrastructure handles millions of AI requests while providing granular observability, sophisticated routing, and cost optimization - all without compromising on performance or developer experience. Building from scratch wasn't the easy choice, but it was the right one for delivering the next generation of AI infrastructure!

Are You Struggling Building Resilient Microservices with Generative AI and RAG Pattern on Azure? Resilience and intelligence in microservices architecture are essential. This architecture integrates Generative AI and Retrieval-Augmented Generation (RAG), making it robust and scalable. Here’s how it all comes together: ✅ Generative AI and RAG Pattern - Azure AI Search Retrieves relevant documents for context and recommendations, empowering AI to give more accurate responses. - Azure OpenAI Generative AI Model Processes and generates intelligent responses, enhancing the user experience through AI-driven recommendations. - Aggregation Service (Azure Functions) Aggregates responses from AI and provides insights back to the client. ✅ Client Interaction  - Clients access the application via Azure Front Door, ensuring global load balancing and enhanced availability. - Azure API Management acts as the API gateway for routing requests to different microservices. ✅ Microservices Ecosystem*  - Azure Kubernetes Service (AKS) manages containerized microservices and scales them as needed. - Azure Service Bus acts as the event hub, enabling reliable communication and event-driven patterns among microservices. ✅ Core Microservices - Recommendations Service (Azure Functions) provides real-time recommendations based on AI insights. - Product, User, and Order Services (Azure App Service) handle core operations, such as product management, user data, and order processing. ✅ Data Layer - Azure SQL Database stores relational data, supporting the transactional needs of the application. - Azure Cosmos DB manages NoSQL data for flexibility in handling diverse datasets. - Azure Blob Storage stores unstructured data and documents for easy retrieval. ✅ Monitoring and Security - Azure Monitor and Application Insights track performance and system health, providing insights for proactive maintenance. - Azure Key Vault and Entra ID secure sensitive data and manage access control to ensure data security. **Points to Consider:** ➖ Use Generative AI and RAG Pattern patterns to improve response relevance, making sure users get the most valuable information and avoid hallucinations. ➖ Ensure High Availability With Azure Front Door and Service Bus. These Services play crucial role in load balancing and fault tolerance, making the system resilient. Would you consider adding Generative AI to your architecture? Let’s discuss below! #Azure #CloudComputing #AI