FAQ

vs. Notebooks

Product

Role

Value

Apache Zeppelin, Jupyter Notebook

Notebook-style document + code frontends

Familiarity from data scientists and researchers, but hard to avoid insecure host resource sharing

Backend.AI

Pluggable backend to any frontends

Built for multi-tenancy: scalable and better isolation

vs. Orchestration Frameworks

Product

Target

Value

Amazon ECS, Kubernetes

Long-running interactive services

Load balancing, fault tolerance, incremental deployment

Amazon Lambda, Azure Functions

Stateless light-weight, short-lived functions

Serverless, zero-management

Backend.AI

Stateful batch computations mixed with interactive applications

Low-cost high-density computation, maximization of hardware potentials

vs. Big-data and AI Frameworks

Product

Role

Value

TensorFlow, Apache Spark, Apache Hive

Computation runtime

Difficult to install, configure, and operate at scale

Amazon ML, Azure ML, GCP ML

Managed MLaaS

Highly scalable but dependent on each platform, still requires system engineering backgrounds

Backend.AI

Host of computation runtimes

Pre-configured, versioned, reproducible, customizable (open-source)

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