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Average Ratings 0 Ratings

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ease
features
design
support

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Write a Review

Description

The Stackable data platform was crafted with a focus on flexibility and openness. It offers a carefully selected range of top-notch open source data applications, including Apache Kafka, Apache Druid, Trino, and Apache Spark. Unlike many competitors that either promote their proprietary solutions or enhance vendor dependence, Stackable embraces a more innovative strategy. All data applications are designed to integrate effortlessly and can be added or removed with remarkable speed. Built on Kubernetes, it is capable of operating in any environment, whether on-premises or in the cloud. To initiate your first Stackable data platform, all you require is stackablectl along with a Kubernetes cluster. In just a few minutes, you will be poised to begin working with your data. You can set up your one-line startup command right here. Much like kubectl, stackablectl is tailored for seamless interaction with the Stackable Data Platform. Utilize this command line tool for deploying and managing stackable data applications on Kubernetes. With stackablectl, you have the ability to create, delete, and update components efficiently, ensuring a smooth operational experience for your data management needs. The versatility and ease of use make it an excellent choice for developers and data engineers alike.

Description

dstack simplifies GPU infrastructure management for machine learning teams by offering a single orchestration layer across multiple environments. Its declarative, container-native interface allows teams to manage clusters, development environments, and distributed tasks without deep DevOps expertise. The platform integrates natively with leading GPU cloud providers to provision and manage VM clusters while also supporting on-prem clusters through Kubernetes or SSH fleets. Developers can connect their desktop IDEs to powerful GPUs, enabling faster experimentation, debugging, and iteration. dstack ensures that scaling from single-instance workloads to multi-node distributed training is seamless, with efficient scheduling to maximize GPU utilization. For deployment, it supports secure, auto-scaling endpoints using custom code and Docker images, making model serving simple and flexible. Customers like Electronic Arts, Mobius Labs, and Argilla praise dstack for accelerating research while lowering costs and reducing infrastructure overhead. Whether for rapid prototyping or production workloads, dstack provides a unified, cost-efficient solution for AI development and deployment.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

Amazon Web Services (AWS)
Apache Airflow
Apache Druid
Apache HBase
Apache Hive
Apache Iceberg
Apache Kafka
Apache NiFi
Apache Spark
Apache ZooKeeper
Docker
Git
Google Cloud Platform
Kubernetes
Microsoft Azure
MinIO
OpenSearch
Prometheus
Python
Trino

Integrations

Amazon Web Services (AWS)
Apache Airflow
Apache Druid
Apache HBase
Apache Hive
Apache Iceberg
Apache Kafka
Apache NiFi
Apache Spark
Apache ZooKeeper
Docker
Git
Google Cloud Platform
Kubernetes
Microsoft Azure
MinIO
OpenSearch
Prometheus
Python
Trino

Pricing Details

Free
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Stackable

Founded

2020

Country

Germany

Website

stackable.tech/

Vendor Details

Company Name

dstack

Founded

2022

Country

Germany

Website

dstack.ai/

Product Features

Data Management

Customer Data
Data Analysis
Data Capture
Data Integration
Data Migration
Data Quality Control
Data Security
Information Governance
Master Data Management
Match & Merge

Data Warehouse

Ad hoc Query
Analytics
Data Integration
Data Migration
Data Quality Control
ETL - Extract / Transfer / Load
In-Memory Processing
Match & Merge

Product Features

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Alternatives

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