Accelerate Real-Time AI Inferencing on Live Production Data
Deliver real-time AI inferencing with instant access to live context data – while maintaining mission-critical performance and cost control for your most critical workloads.
Request a DemoWhy Cloud-Native Storage Falls Short for Real-Time AI Inferencing
Enterprises are racing to deploy AI inferencing, but limited access to live, production-grade context data from relational systems slows innovation and drives up cloud costs. Directly querying already strained production databases introduces latency, unpredictable performance, and operational risk – especially as even modest AI models can generate traffic equivalent to thousands of concurrent users with bursty, non-human access patterns. Native cloud storage and legacy architectures aren’t designed for this behavior, resulting in latency spikes, noisy-neighbor effects, and unstable economics.
Silk addresses this challenge at the cloud data layer, delivering sub-millisecond access to live enterprise data with built-in resiliency and unified access – without impacting production systems. By isolating inferencing workloads while supporting concurrent access patterns, Silk enables unlimited performance and predictable scale for AI inferencing, analytics, and mission-critical databases. The result: AI inferencing deployed in days, not months, without refactoring applications or risking important workloads.
Why Leading Enterprises Trust Silk for Real-Time AI Inferencing
Real-Time AI Inferencing on Live Production Data
Real-time AI inferencing depends on timely access to production-grade context data. Enabling models to consume live data directly – without impacting primary databases – supports low-latency inferencing while preserving application performance and operational stability. Sub-millisecond access and high throughput ensure AI workloads and production systems operate concurrently and predictably.
Predictable Performance Across Mixed Workloads
When AI inferencing, analytics, and transactional workloads share the same data platform, performance conflicts are inevitable. Real-time adaptive block sizing allows multiple access patterns – sometimes within the same database – to run concurrently without noisy-neighbor effects or latency spikes. The result is consistent, predictable performance as AI workloads scale alongside mission-critical applications.
Risk-Free AI Inferencing with Fully Isolated Production Clones
Production enterprise databases contain highly sensitive data. Fully isolated, high-performance database copies enable AI inferencing, experimentation, and development without exposing production systems to risk. Combined with data masking, these thin clones provide production-grade context to AI models while preventing data leakage or performance impact – enabling full-speed AI innovation with a tightly controlled blast radius.
Sentara's AI Initiative Is Enabled By Silk's Faster Performance and Reduced Cloud Costs
