ScyllaDB Product Update: Vector Search, Tablets & What’s Next

ScyllaDB Product Update: Vector Search, Tablets & What’s Next

• 1.
  2026.1 Highlights ScyllaDB A realtimeNoSQL DB for data-intensive apps. “ScyllaDB has been a quiet, well-behaved database… We’re not having weekend-long firefights, nor are we juggling nodes in the cluster to attempt to preserve uptime.” - Bo Ingram, Discord
• 2.
  Introductions Szymon Wasik + Experiencedresearcher and ex-Googler + Engineering Director at ScyllaDB responsible for Core AI research and development + Professor at Merito University Poznań, Poland Faisal Saeed + Solution Architect and Customer Success Engineer at ScyllaDB. + Passionate about massive-scale NoSQL Tzach Livyatan + Long career in development, system engineering and product management. + NoSQL enthusiast
• 3.
  2026.1 More Power →Reduce TCO See how more powerful Vector Search, elasticity and performance improvement can drive down ScyllaDB TCO.
• 4.
  + Vector Searchenhancements + X Cloud and Tablets - feature parity + Reduce TCO + Incremental repair + Dictionary Compression + New ARM Instances: I8g family 2026.1 Highlights
• 5.
  Real Time AI VectorSearch
• 6.
  6 ScyllaDB’s AI UseCases 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
• 7.
  Vector Search Vector Database Savedin Searched in Embeddings Embeddings Document Image Audio Transformed into User’s Search Query Transformed into 1 2 3 Perform Similarity Search Accurate Search Result To Provide
• 8.
  Vector Search Architecturein Scylla Cloud 8 App App App AZ1 AZ2 AZ3 ScyllaD B ScyllaD B ScyllaD B ScyllaD B ScyllaD B ScyllaD B ScyllaD B ScyllaD B ScyllaD B VS VS CQL
• 9.
  Superior Performance 1B@ 98% recall ScyllaDB: 3 x i4i.16xlarge (3 x 64 vCPUs) Vector Store: 3 x r7i.48xlarge (192 vCPUs, 1.5 TB RAM)
• 10.
  Small 10M vectors 768 dimensions K:10 No Quantization ScyllaDB: 3 x i8g.large $796 / month VS: 2 x r7g.2xlarge $847 / month Sum: $1,643 / Month Medium 100M vectors 768 dimensions K: 10 Scalar Quantization ScyllaDB: 3 x i8g.4xlarge $6,365 / month VS: 2 x r7g.8xlarge $3,387 / month Sum: $9,752 / Month + Recall: ~90% + Metadata per Vector: 100B + P99 Latency <= 15ms Vector Search Example Systems 10 12,763 QPS; P99: 7.8 ms 24,265 QPS; P99: 9.9
• 11.
  + Quantization + Filtering +Retrieve Similarity Values + Performance improvement Vector Search - New in 2026.1
• 12.
  Vector Search Quantization Quantizationis the process of taking values from a large range, and packing them into a smaller range. It is a form of lossy compression. https://ngrok.com/blog/quantization Quantization allow to fit more vectors / dimensions into memory, on the expense of accuracy
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  Vector Search Quantization No Quantization Scalar(8-bit) Scalar (4-bit) Binary (1-bit) Compression Factor 1x (Base) 4x 8x 32x Expected Recall 100% (Base) ~97% 90% 70% Quantization High Recall Low Memory Usage
• 14.
  Rescoring Compressed Binary Vector Index FullVectors User’s Search Query Query: Retrieve K=10 K=100 100 NN Return: 10 top values Rescore
• 15.
  2025.4 - AllVector Semantic Searches scanning ALL the vectors 2026.1 - use WHERE to filter the result Examples use cases: + Most similar user profile in the EU + Most similar product added this month + Most similar picture published by this user Vector Search + Filtering
• 16.
  Demo! Real Time Recommendationwith Vector Semantic Search!
• 17.
  CREATE CUSTOM INDEXIF NOT EXISTS articles_embedding_idx ON demo.articles (embedding) USING 'vector_index' WITH OPTIONS = { 'similarity_function': 'COSINE' }; Create a Vector Index 17
• 18.
  SELECT id, title,body FROM demo.articles ORDER BY embedding ANN OF [0.85, 0.15, 0.0, 0.0] LIMIT 2; Use a Vector Index 18
• 19.
  Vector Search +Filtering - Global Index SELECT id, title FROM myapp.items WHERE created_at >= '2024-01-01' ORDER BY item_vector ANN OF [0.12, 0.34, ...] LIMIT 10 ALLOW FILTERING; CREATE CUSTOM INDEX global_idx ON myapp.items(item_vector) USING 'vector_index' WITH OPTIONS = { 'similarity_function': 'COSINE' };
• 20.
  Filtering Vector Search +Filtering - Local Index SELECT id, title FROM myapp.items WHERE tenant_id = 'acme' AND created_at >= '2024-01-01' ORDER BY item_vector ANN OF [0.12, 0.34, ...] LIMIT 10; CREATE CUSTOM INDEX local_idx ON myapp.items( (tenant_id), item_vector) USING 'vector_index' WITH OPTIONS = { 'similarity_function': 'COSINE' };
• 21.
  SELECT commenter, comment, similarity_cosine(comment_vector,[0.12, 0.34, 0.56, 0.78, 0.91]) FROM myapp.comments ORDER BY comment_vector ANN OF [0.12, 0.34, 0.56, 0.78, 0.91] LIMIT 5; Retrieve Similarity Values Retrieve similarity values, not just order, from the Vector Search Index. Available functions: + similarity_cosine + similarity_dot_product + similarity_euclidean
• 22.
  X Cloud FeatureParity 2026.1 adds support for Counters on Tablets Support for Tablets is complete X Cloud and Tablets become default
• 23.
  X Cloud -True Elastic Scaling
• 24.
  90% Storage Utilization
• 26.
  Performance Improvements Incremental repair Dictionary Compression NewARM Instances: I8g family
• 27.
  Repair - FixInconsistent Data Views: 100 Views: 150 Views: 150 Replica 2 Replica 3 Views: 150 Views: 150 Replica 1 27
• 28.
  Incremental Repair T1 Tablet SSTables Tablet SSTables Tablet SSTables Repaired SSTable
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  Incremental Repair T2 Tablet SSTables Tablet SSTables Tablet SSTables Repaired SSTable UnRepair SSTable RepairBorder
• 30.
  Incremental Repair T3 Tablet SSTables Tablet SSTables Tablet SSTables Repaired SSTable OnlyRepair New SSTables Repair Border
• 31.
  Incremental Repair T4 Tablet SSTables Tablet SSTables Tablet SSTables Repaired SSTable RepairBorder
• 32.
  Incremental Repair inAction
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  Incremental Repair inAction
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  Up to 3times better compression, less network and storage usage. Dictionary Compression - Network
• 35.
  Up to 3times better compression, less network and storage usage. Dictionary Compression - Stroage
• 36.
  36 + 2x Higherthroughput → ½ number of instances + Same instance price -> Lower TCO + Low Consistent Latency → Better UX + Low latency during operation → Seamless elasticity with X Cloud Introducing Amazon EC2 I8g and I8ge
• 37.
  37 Compute Bound Max Throughput- i4i / i8g
• 38.
  38 Storage Bound Max Throughput- i3en / i8ge
• 39.
  I8g and I8geAre Available in ScyllaDB X Cloud 39
• 40.
  + ScyllaDB Cloud- Terraform Provider (1.10) - Available today + ScyllaDB Cloud - flexible zone assignments - Available today + ScyllaDB Cloud - Private Service Connect - Limited Availability + Alternator (DynamoDB API) Vector Search + vNode to Tablet migration + Hybrid Search + Obj Storage Backend + Faster K/V engine + Much more… Coming Soon
• 41.
  Get practical tipsfrom experts who have led hundreds of evaluations Wednesday, May 20, 2026 How to Compare High Performance Databases
• 42.
  Thank you for joiningus today. @scylladb scylladb/ slack.scylladb.com @scylladb company/scylladb/ scylladb/