Enterprises, system integrators (SIs), OEMs, and developers can take control with Megh’s Open Analytics VAS SDK: A simplified and supported platform for custom, high-performance intelligent video analytics solutions.

VAS SDK allows developers to optimize video (and data) analytics pipelines and integrate highly customizable AI into applications and products.

With AI+ acceleration using GPUs, FPGAs, and SOCs, VAS SDK offers the highest performance, most scalability, greatest flexibility, and best vendor choice for system integrators and independent software vendors.


VAS SDK is a platform optimized for real-world complexity and performance.

Fully customizable pipeline

Real-time system

Cross-platform support

Open vendor choice

Intelligent video analytics

VAS SDK enables development of end-to-end intelligent video analytics solutions with the following Megh products:

VAS Suite— Deploy market-ready solutions that create actionable insights to reduce risks and improve operational efficiencies.

VAS Portal— Manage multiple VAS Suite instances via any browser with our easy-to-use dashboard.

For help with creating or implementing video analytics for your custom use case, contact us.

Specifications and implementation

  • Languages supported: Python
  • AI model frameworks: Pytorch and TensorFlow (TF-Lite)
  • Platforms: CPU, GPU, FPGA, SOC
  • Vendors: Intel, AMD, ARM, Xilinx, NVIDIA, Qualcomm
  • Cloud infrastructure supported: AWS, Microsoft Azure, Google Cloud

For more technical details, learn about The Megh Platform.

SDK extensions

Enhance VAS SDK with our SDK extensions:

  • DLE SDK: Train, build, and deploy custom models with Pytorch, TF-Lite, and other frameworks on FPGAs for AI deep learning inferencing
  • AFU SDK (coming soon): Develop custom FPGA accelerator function units (AFUs) for analytics using RTL/HLS for deployment on scale-out FPGA platforms
Click to enlarge

For more information about DLE SDK, contact us.


VAS SDK is an Open Analytics solution, designed to enable flexible, scalable, high-performance, end-to-end integration into your business operations, applications, and products.

Comprehensive control

  • Customize the AI/video analytics pipeline for any camera/sensor stream
  • Optimize for accuracy, TCO, or performance based on use case
  • Edge-to-cloud deployment options for performance, cost, and regulatory adherence

Safe scalability

  • Start with CPU and scale to GPU/FPGA as needed
  • Change compute platform and/or deployment type on the fly
  • Stability for business users while scaling (no need to recode app layer)

Easy AI adoption

  • Utilities provided to help report data and optimize analytics
  • Concierge Support for custom integration or model development

Optimized TCO

  • Right-size hardware for simple-to-complex use cases, based on pre-tested specs
  • Lowest TCO for any performance target

Enables operational reliability

  • Intelligent video analytics that actually deliver in complex environments and use cases
Get started with VAS SDK
More information, product demo, free trial, expert consultation—begin exploring how to transition to AI-driven operations with VAS SDK

Product brief

VAS SDK product brief

Technical papers

This 11-page Dell-EMC technical white paper evaluates a video analytics pipeline with deep learning inference using Megh’s VAS and Intel Programmable Acceleration Card (PAC) FPGAs on Dell EMC PowerEdge R740/R740xd servers.

Real Time Streaming Analytics with Megh
Computing on Dell EMC PowerEdge Servers

This 3-page HPE technical white paper discusses how customers can achieve dramatically increased performance and reduced TCO by running the Megh Computing real-time streaming video analytics platform on the HPE ProLiant DL380 Gen10 server with Intel Arria 10 GX FPGAs compared to a configuration without FPGAs.

Real-Time Streaming Video Analytics with Hewlett
Packard Enterprise, Intel, and Megh Computing

Intel partnership

In the following video, see how Intel and Megh have partnered in developing low-latency analytics using AI with Intel FPGAs.

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