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Description
Kinesis serves as a comprehensive compute platform that integrates disparate infrastructures spanning clouds, on-premises systems, edge locations, and partner data centers into a cohesive grid. Users can easily deploy applications by pushing a GitHub repository, supplying a Dockerfile or container image, linking a registry, selecting a template, or outlining application requirements, after which Kinesis evaluates the workload, identifies appropriate CPU or GPU resources, and facilitates a live deployment. With its intent-driven controls, Kinesis allows teams to optimize various parameters including cost, reliability, latency, and multi-cloud functionality, all while avoiding the complexities of configuring VPCs, IAM hierarchies, and security groups. Standard containers are capable of running seamlessly across different providers without requiring any rewrites or vendor lock-in, and essential features such as networking, autoscaling, monitoring, health checks, failover mechanisms, recovery options, certificates, secrets management, and rollback capabilities are integrated into every deployment. Additionally, Kinesis continuously assesses and makes intelligent decisions regarding placement, scaling, utilization, and failure management within a diverse compute environment, ensuring efficiency and resilience in operations. This means users can focus on their applications without the burden of underlying infrastructure concerns.
Description
TensorHost delivers GPU computing resources through its own infrastructure, avoiding reliance on third-party providers. Clients indicate their workload specifications, GPU needs, quantity, and preferred timing; service details and location are verified before a final agreement on allocation is reached. This service enables access to NVIDIA GPU capacity for AI applications and other highly demanding computational tasks. TensorHost maintains the foundational infrastructure, allowing clients to utilize the allocated compute for their specific applications and operations. It caters primarily to developers, AI teams, and businesses seeking GPU resources without the burden of buying and managing their own GPU servers. Users are charged based on their actual usage, with the capacity and pricing determined based on the requested resources. This model allows for flexibility and scalability in meeting the diverse computing demands of clients in a rapidly evolving technological landscape.
API Access
Has API
No
API Access
Has API
No
Screenshots View All
No images available
Integrations
GitHub
No
Pricing Details
No price information available.
Free Trial
No
Free Version
No
Pricing Details
$0.50/GPU-hour
RTX 5090 compute is $0.50 per GPU-hour, billed per second, with no minimums or egress charges. Other GPU configurations are quoted in USD.
Free Trial
No
Free Version
No
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Deployment
Web-Based
Yes
On-Premises
No
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
Chromebook
No
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
Yes
Customer Support
Business Hours
No
Live Rep (24/7)
No
Online Support
No
Types of Training
Training Docs
Yes
Webinars
No
Live Training (Online)
Yes
In Person
No
Types of Training
Training Docs
No
Webinars
No
Live Training (Online)
No
In Person
No
Vendor Details
Company Name
Kinesis Network
Founded
2024
Country
United States
Website
kinesis.network/
Vendor Details
Company Name
GPU Server Hosts
Founded
2025
Country
United Kingdom
Website
tensorhost.com
