ScyllaDB Vector Search: A Technical Deep Dive on Real-Time Performance at Billion-Vector Scale by Szymon Wasik

ScyllaDB Vector Search: A Technical Deep Dive on Real-Time Performance at Billion-Vector Scale by Szymon Wasik

• 1.
  A ScyllaDB Community ScyllaDBVector Search A Technical Deep Dive on Real-Time Performance at Billion-Vector Scale Prof. Szymon Wąsik Director of Engineering
• 2.
  Szymon Wąsik ■ 2007-2018:Research work on discrete optimization and modeling in bioinformatics ■ 2018-2024: Software Engineering at Google, working on auto-scaling and analytical infrastructure ■ Currently: ■ Engineering Director at ScyllaDB ■ Professor at Merito University Poznań, Poland
• 3.
  Intelligence is theability to adapt to change Stephen Hawking
• 4.
  Vector Search Trilemma Performance CostQuality Low recall No high availability High memory usage Multiple replicas >100 ms tail latency
• 5.
  ScyllaDB brings themmuch closer! Performance Cost Quality
• 6.
  ■ Fully managedScyllaDB Cloud service ■ Horizontally and vertically scalable ■ High availability ■ Supports XCloud for flexible storage options ■ Driver support: Rust, C++, Python, Java, C#, Node.js, GoLang Vector Search is live
• 7.
  Grafana monitoring Source: github.com/ashvardanian/USearchBench.py
• 8.
  Designed for real-timeAI
• 9.
  High level architecture ScyllaDB’spredictable storage performance High Availability by replicating to different AZs Horizontal scalability by adding replicas Vertical scalability supporting up to 1.5T RAM and 192 CPUs HA HA
• 10.
  Internal architecture 1: indexcreated 2: full scan 3: stream updates 4: VSS queries
• 11.
  Internal architecture Workload isolation protectsperformance of ScyllaDB and Vector Search USearch: top performing vector indexing library Data protected by TLS and ScyllaDB RBAC (Role Based Access Control) ScyllaDB Change Data Capture provides real time consistent updates
• 12.
  Real-Time AI onYour Operational Data No ETL drift, no dual writes Avoid syncing a transactional DB and a separate vector DB. The only pipeline you need is to create embeddings using your favourite LLM / ML model. Performance + cost efficiency Real-time latency and massive throughput (no network hops). Do more with fewer resources compared to any other VS solution. Operational simplicity Built on ScyllaDB’s proven core - fewer moving parts: one cluster to secure, and monitor. Distributed, highly available, multi-AZ/multi-region, consolidated. One dataset, two retrieval modes Store both structured attributes and unstructured meaning in the same table. Run hybrid queries: “Find top 5 similar documents, but only from this customer and last 30 days.”
• 13.
  Stunning performance
• 14.
  USearch - TopVector Search Library Source: github.com/ashvardanian/USearchBench.py
• 15.
  Billion Vectors: HighRecall (98%) with Slightly Higher Latency ScyllaDB: 3 x i4i.16xlarge (3 x 64 vCPUs) Vector Store: 3 x r7i.48xlarge (192 vCPUs, 1.5 TB RAM)
• 16.
  Billion Vectors: Ultra-LowLatency with Moderate Recall (70%) ScyllaDB: 3 x i4i.16xlarge (3 x 64 vCPUs) Vector Store: 3 x r7i.48xlarge (192 vCPUs, 1.5 TB RAM)
• 17.
  New in ScyllaDB2026.1
• 18.
  ■ Vector storagecompression: ■ 75% on scalar (i8) representation ■ 97% on binary (b1) representation ■ Index compression depends on graph density ■ Using oversampling and rescoring to keep recall high Quantization CREATE CUSTOM INDEX products_by_images ON products (image_embeddings) USING 'vector_index' WITH OPTIONS = "{ 'quantization': 'b1', 'similarity_function': 'dot_product', 'rescoring': 'true', 'oversampling': '10.0'}";
• 19.
  ■ Two typesof index: ■ Global when searching across most of the vectors ■ Local (per partition) when filter splits dataset to huge number of subsets ■ Supporting comparison operators ■ Filtering in Vector Store for best recall and performance Filtering SELECT commenter, comment FROM cycling.comments_vs WHERE lang = "PL" AND create_at > "2026-03-10 00:00:00" ORDER BY comment_vector ANN OF [0.1, 0.2, 0.3, 0.4, 0.5] LIMIT 5;
• 20.
  Roadmap Q4 Supporting >10B vectorsscale Embeddings generation Q3 Dedicated search nodes Product quantization Indexes snapshotting Q2 Hybrid text search LangChain and other frameworks compatibility Q1 Vector Search GA Quantization Filtering
• 21.
  Additional Resources Videos: ● ⏱1 min — Quick Intro ● 🎛 2 min — Live Demo ● 🧭 10 min — Product Overview ● 󰳓 15 min — Step-by-Step Tutorial Sample Use Cases ● 🎬 Movie Recommendation ● 💬 RAG Chatbot ● ⚡ Semantic Cache (reduce LLM latency & cost) Sample Applications Learn More ● 📚 Vector Search Documentation ● 🎓 ScyllaDB University Course ● 🎤 P99 Talk - Engineering a Low-Latency Vector Search Engine for ScyllaDB
• 22.
  ScyllaDB Monsters
• 23.
  ScyllaDB Summit 2025Styles The default body font is Roboto Condensed. You can adjust the size as needed. You can also use Roboto (the uncondensed version). For code you should use Roboto Mono and you can set it on this dark background Header font is Archivo: Monster Scale Color palette (base for the gradient): Accent colors: #15D2A8 #0D72FF #7933FF #0F1040 #F0581E #CE1576 #A3E3FF https://fonts.google.com/specimen/Archivo #FFB32B Body copy font is Roboto
• 24.
  Stay in Touch SzymonWąsik szymon.wasik@scylladb.com github.com/swasik https://www.linkedin.com/in/szymon-wasik/