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Scaling Freshworks’ AI Data Platform with ScyllaDB by Vigneshkumar AK & Premkumar Patturaj

Innovation never sleeps at Freshworks. Freshworks has been rebuilding its AI data platform to support 10X growth, 95% lower tail latency, and lower operating cost by moving multiple use cases to ScyllaDB. In this session, we walk through four production use cases now with ScyllaDB: a zero-downtime migration from Cassandra for a multi-billion-record customer store, offloading petabytes of MySQL text and blob data, replacing DynamoDB to remove item-size and throttling limits, and using ScyllaDB for new AI workloads such as feature stores and low-latency caching.

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A ScyllaDB Community Scaling Freshworks’ AI Data Platform with ScyllaDB Vigneshkumar AK Lead Software Engineer Premkumar Patturaj Director - Engineering
Vigneshkumar AK, Lead Software Engineer ■ 10+ years of software engineering experience ■ Driving the reliability, scalability, and high availability of distributed database systems, ensuring efficient operation at scale. ■ 16+ years of IT experience, with 10+ years at Freshworks. ■ Expertise in Relational and NoSQL databases, specializing in designing and optimizing scalable, high-performance systems. ■ Experienced in solving complex technical challenges, mentoring teams, and fostering a culture of continuous learning. Premkumar Patturaj, Director - Engineering
2010 Founded FRSH IPO September 2021 $800M+ 2025 Annual Revenue Guidance 4,500+ Employees 75,000+ Total Customers Recognition 3 Gartner Magic Quadrants Leader in 3 Major Peer Reviews About Freshworks
Freddy AI Insights Freddy AI Copilot Integrate & Extend Developer tools Marketplace Unify Data Analytics Admin Security Manage & Secure Employee Experience Customer Experience SOLUTIONS Freshservice Customer Service Suite Freshdesk Freshchat Freshsales Freshmarketer Freshservice for Business Teams Device42 PLATFORM AI Freddy AI for Customer Service, Sales, Marketing, IT & Developers for Business Leaders Freshworks Neo Freddy AI Agent for Customers & Employees Freshworks Stack
A journey to multi-persona, AI-guided strategy OUR INNOVATIVE SPIRIT AND PACE Freddy AI Customer 360 Vision ITSM Freshservice Freshservice for Business Teams Modern messaging Freshchat Cloud telephony Freshcaller 2023 2020 2011 Freshworks Neo Customer service Freshdesk Platform Marketing automation Freshmarketer Freshsales Sales Freshdesk Omni Customer service (formerly Customer Service Suite) ESM 2024 Device42
Powering reliable, scalable, and seamless database experiences — so application teams stay database-agnostic. A mix of Self Hosted and Cloud solutions DATA ENGINEERING RELIABILITY & SCALE SECURITY & KEEP CURRENT SELF SERVICE & ENTERPRISE GRADE DISASTER RECOVERY & BCP Finding the best balance for performance, cost & scale Dataverse at Freshworks
Deployment model BYOA ScyllaDB @ Freshworks Performance and scale factors ScyllaDB use cases in Freshworks Migration Journey Future Use cases
ScyllaDB Deployment ■ ScyllaDB is deployed within Freshworks’ own AWS account ■ Operates under Freshworks’ AWS discounting and governance ■ Ensures complete control, security, and cost efficiency ■ Fully integrated with existing Freshworks cloud infrastructure
Performance Results – Accelerating Tail Latency RPS ScyllaDB Cassandra P99.99 (ms) Reads 400 9.42 50.53 2k 7.22 269.57 4k 7.92 324.86 Writes 600 12.88 62.53 3k 6.63 267.77 6k 7.39 327.68
Conversation Store Message Storage Instant storage and retrieval of all customer messages with full-text search capabilities. Conversation Management Complete thread management with metadata and participant tracking across channels. Multilingual Support Locale-specific conversions enabling responses in customers' preferred languages. AI Bot Integration Persistent bot conversations and training data for continuous learning. Use case - 1
4B+ customer contacts <5ms latency 15k ops/sec Use case - 2 Centralized Customer Data - UCR
Use case - 2
Centralized Customer Data - UCR Massive Scale Stores 4B+ customer contacts with ease. Engineered for seamless enterprise growth. Real-Time Performance Delivers <5ms latency for instant lookups. Optimized for fast, responsive experiences. High Throughput Handles 15K ops/sec consistently. Built for heavy concurrent, mission-critical workloads. Single Source of Truth Centralizes all customer data reliably. Removes silos and ensures consistency Use case - 2
During migration, data is simultaneously written to Cassandra and ScyllaDB through the Zero Downtime Migration (ZDM) proxy. Migration Steps – Cassandra to ScyllaDB Use case - 2
The rest of the data is migrated to ScyllaDB nodes using CDM (Cassandra Data Migrator). Migration Steps – Cassandra to ScyllaDB Use case - 2
Validating data consistency between the source and destination using CDM (Cassandra Data Migrator) and fixing inconsistencies in ScyllaDB Migration Steps – Cassandra to ScyllaDB Use case - 2
250k+ Workflows stored 75k/min Workflow executed 25k ops/sec Workflow Automator Use case - 3
Workflow Automator Use case - 3 Workflow Definition Storage Stores 250k+ complete workflow definitions in ScyllaDB with flexible schema for complex workflow structures. Performance & Scalability 75k/min workflows executed per minute with sub-millisecond state query latency, delivering 25k ops/sec and horizontally scaling for unlimited throughput. State Management Supports real-time workflow state persistence with instant state recovery and replay. Conductor Benefits Microservices orchestration with a visual workflow designer, monitoring, fault tolerance, & automatic retries.
■ Massive Scalability & Low Latency Handles millions of writes with consistent, single-digit millisecond performance, crucial for real-time logging. ■ Cost-Effective Throughput On-demand capacity mode automatically scales to handle bursty traffic, optimizing costs for unpredictable activity patterns. ■ Built-in Data Lifecycle Management Time-to-Live (TTL) feature automatically expires old records, simplifying data retention and reducing storage overhead. ■ Massive Scalability & Low Latency "Streams” enable seamless integration with AWS Lambda for immediate processing of events (analytics, anomaly detection). Activity Tracker Service – DynamoDB to ScyllaDB
■ Phase 1 – Preparation & Change Capture Schema Migration: Replicate DynamoDB data models into ScyllaDB. Change Capture: Stream real-time updates (inserts/updates/deletes) via Streams, CDC, dual-writes, Lambda, Kafka, etc. ■ Phase 2 – Data Forklifting & Replication Bulk Migration: Forklift all DynamoDB records into ScyllaDB. Replay Pipeline: Apply captured changes in order through Kafka/Lambda/Spark for continuous sync. ■ Phase 3 – Validation & Synchronization Consistency Checks: Compare record counts, checksums, and sampled data across both DBs. Integrity Validation: Verify hashes, random samples, and transaction correctness. ■ Phase 4 – Cutover & Transition Switch Traffic: Shift reads and writes fully to ScyllaDB with DynamoDB as fallback. Final Migration: Decommission DynamoDB once ScyllaDB becomes the primary store. DynamoDB to ScyllaDB Migration
Online (Live) Migration DDB / CDC / Dual writes read writes Dynamo DB Dynamo DB Migrate Schema / Capture changes Replay Changes Consume from Kafka, AWS Lambda, Spark, etc Dynamo DB Validator Checks DBs in Sync Dynamo DB Validator Fade Off read writes Phase 2 Phase 1 Phase 3 Phase 4
AI Use Cases for ScyllaDB Traditional Databases Fail High Interference Latency AI apps like recommendations, and personalisation need <1 ms responses. Traditional DBs slow this down. Feature Store Bottlenecks Most inference delays happen when fetching features from the database, not in the model itself. Expensive Recalculation Without fast access, systems are forced to recompute features repeatedly, wasting CPU and increasing costs. ScyllaDB Works Ultra-Low, Predictable Latency Built on a shard-per-core architecture that avoids noisy-neighbour issues. High Throughput at Scale Handles millions of reads/writes per second with consistent performance. Perfect for Feature Stores Enables instant feature retrieval → faster inference, lower cost, and more accurate real-time decisions. Future use case - 1
AI Use Cases for ScyllaDB What is Feature Caching? ■ Stores precomputed model features, inputs, or outputs for instant reuse ■ Removes redundant computations for repeated or similar requests ■ Significantly reduces inference latency and improves system responsiveness Key Applications ■ Personalized Recommendations: Instant delivery of user-specific results ■ Dynamic Pricing: Real-time retrieval of pricing features and signals Why ScyllaDB Excels ■ Ultra-Low Latency: Predictable P99 performance in single-digit milliseconds ■ High Throughput: Sustains massive read/write volumes without degradation ■ Horizontal Scalability: Expands seamlessly as data and traffic grow
Data Store for Model Training What? ■ Cache model inputs, outputs, and precomputed features to avoid repeated expensive model calls. Used in fraud detection, ad-tech, recommendation systems. Key Applications ■ Model receives request → feature lookup in ScyllaDB. Cache model output or precomputed scores. TTL-based automatic cleanup for short-lived cache entries. Integrate with streaming systems (Kafka/Fluentd) for updates. Why ScyllaDB Excels ■ Extremely high write/read throughput for ephemeral or hot data. ■ Predictable p99 latencies ensure consistent inference response times. ■ Scales horizontally to meet inference request growth.
BLOB Store Use case - 4 Blob Storage Migration Seamless transition of 2PB MySQL BLOB/Text data into ScyllaDB for high-performance storage. Historical Data Processing Large-scale backfill powered by Spark jobs and Parquet pipelines for efficient bulk migration. Real-Time Data Replication Continuous syncing of live MySQL blob updates into ScyllaDB through a Kafka CDC pipeline. Optimized Storage Footprint Achieved 50% reduction in storage usage while improving performance and scalability.
BLOB Store Use case - 4 2 PB Seamless transition 50% Reduction in storage
Takeaways ■ Low Tail Latency ■ BYOA ■ Scales for Enterprise workloads ■ Cache Workloads, DynamoDB Workloads and Cassandra Workloads ■ Zero Downtime Patching for Version Upgrades ■ Dynamic Scaling Capabilities
Stay in Touch Vigneshkumar AK vigneshkumar.ak@freshworks.com www.linkedin.com/in/vigneshkumarak Premkumar Patturaj premkumar.patturaj@freshworks.com https://x.com/iam_prem https://www.linkedin.com/in/prem-k umar-patturaj-27217933/