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What Real-Time AI Requires from Your Database - May 2026

AI is pushing databases to their limits; ScyllaDB is built for this kind of pressure

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What Real-Time AI Requires from Your Database Dor Laor, CEO & Co-Founder, ScyllaDB Avi Kivity, CTO & Co-Founder, ScyllaDB
Avi Kivity, CTO & Co-Founder, ScyllaDB Dor Laor, CEO & Co-Founder, ScyllaDB Introductions 2 ScyllaDB, Senior agent, ScyllaDB
Powering India's top social media platform Video recommendation management Real-time fraud detection Seamless experiences across content + devices Network security threat detection Content personalization & recommendation platform Mobile Growth & Monetization Platform Inventory hub for retail operations Property listings and updates Cryptocurrency exchange app Real-time auctions advertising platform Predictable performance for on sales surges Online gaming ad targeting Media streaming for 45M+ subscribers Bridging AI to IT Service Management Real-time ML-driven recommendations Real-time endpoint threat detection and security Real-time personalized recommendations World leading beauty platform behind Avon Real-time AI decisioning for digital advertisers AI-centric customer research platform Powering Unreal Engine real-time asset distribution Real-time interactions at massive scale Always-on e-commerce platform for millions of fans ScyllaDB Users
AI Break Databases
Prehistoric Past Centralized DBs & early NoSQL The Iron Age Close to metal DBs Real Time AI Age AI and ML boom Agentic AI (in Real Time) Near Future + Huge scale + Enormous bursts + Low latency expectations + Complications: Hybrid DBs - Vector & Text search AI Break Database Workloads
AI Break Database Workloads Vector Search Enables semantic information retrieval by indexing high-dimensional embeddings. Critical for RAG architectures, Feature Store A centralized repository for managing and serving ML features. Ensures consistency between training and serving while promoting feature reusability across teams. Agentic AI Autonomous systems capable of reasoning, planning, and tool utilization. Orchestrates multi-step tasks by shifting from passive chat to active goal execution. Large Scale High-throughput workloads requiring massive distributed clusters. Optimized for model pre-training, fine-tuning, and ultra-low latency inference at scale.
AI Break Database Workloads Vector Similarity Feature Store Highly Scalable Database within an AI Stack Dissimilar Dissimilar Similar Results (Matches) Vector Space Query Vector Fast, Accurate Matching of Complex Data Raw Data Sources Real-time Event Streams ML Training ML Serving (Interference) Central Feature Store (ScyllaDB) Ingestion Processing & Training Serving & Inference High Throughput & Low Latency Access Agentic AI
The Agentic AI Age
+ Agents are the primary database user — Not Humans + Human - Chat requests per second; Agents - per milliseconds + Human - few users per org; Agents - Unlimited number + Human - predictable usage (mostly); Agents - Unpredictable Brave New Agentic World
LangGraph: Achieving zero agent downtime with ScyllaDB.
More Agentic Resources:
AI Use Case: Feature Store
Driving Tripadvisor ML personalization
How ShareChat built a scalable cost efficient ML Feature system https://sharechat.com/blogs/artificial-intelligence/how-sharechat-built-a-scalable-cost-efficient-ml-feature-system 14
Latency, Op/s, Scale Results
The Real Time AI Age
Self-driving Model Training Use case: Serves a huge GPU farm as a database for model training. Migration off a well known DB due to cost Scale: + 180TB per node, compressed + 0.5PB per node uncompressed + Tens of nodes + > 1M op/s + 256 cores per node
Scylla Design Decisions Threads Shards 1 C++ instead of Java 2 All Things Async 3 Shard per Core 4 Unified Cache 5 I/O Scheduler 6 Autonomous
Scylla Design Decisions Cassandra Scylla Key cache Row cache On-heap / Off-heap Linux page cache SSTables Unified cache SSTables 1 C++ instead of Java 2 All Things Async 3 Shard per Core 4 Unified Cache 5 I/O Scheduler 6 Autonomous App thread Kernel SSD Page fault Suspend thread Initiate I/O Context switch I/O completes Interrupt Context switch Map page Resume thread Page fault
Scylla Design Decisions 1 C++ instead of Java 2 All Things Async 3 Shard per Core 4 Unified Cache 5 I/O Scheduler 6 Autonomous Memtable Seastar Scheduler Compaction Query Repair Commitlog SSD Compaction Backlog Monitor Memory Monitor Adjust priority Adjust priority WAN CPU
AI & DB Peaks
AWS DynamoDB Auto Scaling is Not a Magic Bullet + Faster Topology Changes + Immediate Request Serving + Easy Downscale + Continuous Auto-Balancing Tablets – True Elastic Scale
X Cloud Elasticity -> Serverless Policy Updated New Nodes Joining Mixed Instance Size
Latency: Before, During, 1M Scale
+ At rest and in transit Dictionary compression + Latest ARM instances + Trie Indexes + Incremental repair + Authentication and authorization Cache + Thin Replicas + Tiered Storage TCO - Every ScyllaDB release is more efficient
Every Agent is different
AI wouldn’t break ScyllaDB Time Volume ie3n’s i8g Time Throug hput Capacity Required Time Throug hput On-demand Base Typeless Sizeless limitless
AI Use Case: Vector Search
Vector Similarity & Text Search Architecture App App App AZ1 AZ2 AZ3 Scylla DB Scylla DB Scylla DB Scylla DB Scylla DB Scylla DB Scylla DB Scylla DB Scylla DB VS VS CQL
Novel CDC at Scale ScyllaDB->CDC->Vector node INSERT INTO base_table(...)... CQL (Opt) preimage read
Vector Similarity Search Resources Native Vector Search for the DynamoDB API Movie RAG search with CQL Local or Global index filters
Is your Database ready? Real-Time Agentic AI is here
Thank you for joining us today. @scylladb scylladb/ slack.scylladb.com @scylladb company/scylladb/ scylladb/