Sunil Kapil

Tip: Scale large HNSW vector sets in Redis by splitting your dataset across multiple instances and querying them in parallel with multiplexing, enabling efficient parallel reads and writes that drastically improve performance and manage memory effectively. This approach leverages Redis's scalable vector support to handle hundreds of millions of vectors seamlessly. #Redis #HNSW #VectorSearch #ScalableAI #DataScience

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Redis 8 is GA and packed with new features, including new data types, faster commands, higher throughput, and faster replication. Learn more about our most performant and scalable version yet: https://lnkd.in/eFU5qPDs

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LiteLLM is an open-source proxy that connects your app to LLMs through one unified interface. Paired with Redis, it gives AI and ML teams a simple yet powerful way to unify access to LLMs, accelerate response times, and make AI apps real-time. Redis handles performance, memory, and data coordination that modern AI apps demand, while LiteLLM handles abstraction and routing. Our AI Product Marketing Manager Rini Vasan dives into: ▶️ What LiteLLM is ▶️ Why it works well with Redis ▶️ How LiteLLM and Redis work together Learn how to scale your LLM gateway with LiteLLM & Redis: https://lnkd.in/gqsTebF7

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Traditional Kafka consumers for analytics = expensive, complex, and fragile. Replay, deduplication, schema drift… it’s a lot. On Oct 9, we’ll show how Estuary + MotherDuck simplify all of this with a fresh approach to streaming ingestion. 🔗 https://lnkd.in/dncWFDGm

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Your CI/CD bill is not exploding because of “expensive runners,” it is bleeding out through flaky tests, over-parallelized jobs, and slow feedback loops that quietly tax every deploy This piece shows you how to redesign pipelines so you cut compute waste, shrink queues, and speed up developer flow without adding yet another approval gate or YAML religion Full breakdown in the comments. #DevOps #CICD #PlatformEngineering

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The most effective architectural changes are often subtractive - removing entire layers of cost and complexity. Behind the scenes, OpsHelm, Inc.'s streaming backbone was migrated from a costly MSK and unreliable NATS setup to a single diskless Kafka architecture. The results were immediate: - Annual spend dropped from >$50,000 to <$10,000. - Data loss incidents went from recurring to zero. - Manual scaling and cross-cloud networking fees were eliminated. Read the full story of their migration and 78% cost reduction: https://lnkd.in/dS6UK9-7 #disklesskafka #datastreaming #Aiven

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Anyscale 🤝GKE Through our continued collaboration with the Google Kubernetes Engine (GKE) team, we’re advancing the experience to scale Ray and Kubernetes in the cloud. This work includes: Label Selectors for improved scheduling: https://lnkd.in/gUZ8iVUW New Features for AI Scaling: https://lnkd.in/gAGSMiir Enhanced TPU Support: https://lnkd.in/gsKDvwpv

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In this post, we explore how to monitor and manage Amazon Bedrock batch inference jobs using Amazon CloudWatch metrics, alarms, and dashboards to optimize performance, cost, and operational efficiency.

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𝗔𝗜 𝗱𝗶𝗱𝗻’𝘁 𝗯𝗿𝗲𝗮𝗸 𝘆𝗼𝘂𝗿 𝗙𝗶𝗻𝗢𝗽𝘀. 𝗜𝘁 𝗼𝘂𝘁𝗴𝗿𝗲𝘄 𝗶𝘁. Model training can run for hours or weeks. Inference costs change with user behavior. Experiments share GPU infrastructure across teams and timelines, making ownership and accountability unclear. GPU costs are 10–20x higher than standard compute, yet most cost tools only show totals - not intent, impact, or outcomes. Why Enterprise FinOps Needs an AI-Oriented Architecture explains why cost governance breaks under AI workloads and how AI-aware visibility, guardrails, and outcome-based tracking help enterprises regain control before budgets spiral. 𝘙𝘦𝘢𝘥 𝘵𝘩𝘦 𝘧𝘶𝘭𝘭 𝘣𝘭𝘰𝘨 𝘣𝘦𝘭𝘰𝘸 𝘪𝘯 𝘵𝘩𝘦 𝘤𝘰𝘮𝘮𝘦𝘯𝘵𝘴 𝘵𝘰 𝘶𝘯𝘥𝘦𝘳𝘴𝘵𝘢𝘯𝘥 𝘸𝘩𝘢𝘵 𝘍𝘪𝘯𝘖𝘱𝘴 𝘧𝘰𝘳 𝘈𝘐 𝘳𝘦𝘢𝘭𝘭𝘺 𝘳𝘦𝘲𝘶𝘪𝘳𝘦𝘴. #FinOps #AICostManagement #AIInfrastructure #CloudCosts #EnterpriseAI #CAIStack

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The Redis AI Challenge ends in less than a week (Aug 10th deadline)! Think it's too late to join? Think again! Here are 3 project ideas that you can build in a week. Project 1: Real-Time Poll App Prompt: Beyond the Cache Build a web app where users can create polls and vote, with results updating live for everyone. A perfect project to showcase Redis for real-time applications. Redis Use: Hashes for poll data, Sorted Sets for vote counts, and Pub/Sub for live updates. Project 2: Simple Job Queue Prompt: Beyond the Cache Create a background job system. A web app submits a task (like sending an email) to a queue, and a separate worker process handles it. This is a core backend development pattern. Redis Use: Redis Lists as a simple and reliable message queue using LPUSH and RPOP. Project 3: AI-Powered Article Suggester Prompt: Real-Time AI Innovators Build a blog that recommends similar articles to the one being read. It's a fantastic, beginner-friendly entry into the world of AI. Redis Use: Use Redis as a vector database to store AI-generated embeddings of your articles and perform lightning-fast similarity searches. --- Head to the challenge page to see entry details and peep what other people are building. Can't wait to see what you build! https://lnkd.in/eH_Dk2wu #Redis #AI #VectorDatabase #MachineLearning #Backend #Programming

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