Technical deep-dive into how Visylix achieves 1M+ concurrent stream capacity through microservices, GPU-accelerated transcoding, distributed storage, and intelligent load balancing.
Most VMS platforms were designed in an era when 100 cameras was considered a large deployment. Today, smart city projects span tens of thousands of cameras, and global enterprises manage camera networks across hundreds of locations. Supporting one million concurrent streams requires fundamental architectural decisions that cannot be bolted onto legacy monoliths.
Visylix was built cloud-native from day one using a microservices architecture where each function, stream ingestion, AI inference, recording, playback, and user management, runs as an independently scalable service. This allows each component to scale horizontally based on actual demand rather than provisioning for peak load across the entire system.
The ingestion layer handles protocol negotiation (RTSP, RTMP, SRT, ONVIF) and routes streams to processing pipelines. Each ingestion node manages up to 2,000 concurrent streams using async I/O and zero-copy buffer passing to minimize CPU overhead.
Distribution uses a tiered SFU (Selective Forwarding Unit) architecture. Origin servers receive one copy of each stream and relay to edge servers positioned close to viewers. This origin-edge topology reduces backbone bandwidth by 90% compared to direct server-to-viewer delivery for high-fanout streams.
Video transcoding and AI inference are the most compute-intensive operations. Visylix uses NVIDIA GPU hardware encoding (NVENC) for transcoding and dedicated inference engines (TensorRT) for AI model execution. A single NVIDIA A100 GPU handles 200+ simultaneous AI inference streams at 15fps each.
The scheduler dynamically allocates GPU resources between transcoding and inference based on demand. During business hours when more viewers are actively monitoring, transcoding gets priority. During off-hours, GPU cycles shift to batch analytics and forensic search indexing.
At one million streams, even modest retention policies generate petabytes of data. Visylix uses tiered storage: hot storage (NVMe SSDs) for the most recent 24-72 hours of footage, warm storage (HDD arrays or S3-compatible object stores) for 30-90 day retention, and cold archival (glacier-class storage) for compliance-mandated long-term retention.
Intelligent retention policies reduce storage costs by 50-70% by recording at full resolution only when events are detected. Idle cameras store low-resolution keyframes, with full-quality recording triggered automatically by AI detections or manual operator activation.
For mission-critical surveillance, downtime is unacceptable. Visylix achieves 99.99% uptime through active-active clustering across multiple availability zones, automatic failover with sub-second recovery, and continuous data replication with point-in-time restore capability.
Each component is designed for graceful degradation. If the AI inference cluster goes down, streams continue recording and displaying. If a storage node fails, recordings are seamlessly rerouted to healthy nodes. The system never has a single point of failure that takes down the entire platform.