Run Semantic Kernel on Dedicated Cloud CPU
Deploy your Semantic Kernel application on Fluence with dedicated vCPUs for agent logic, tools and background workers. Connect a supported model API to handle inference.
Fluence
$2.54/mo
Hetzner
$14.00/mo
DigitalOcean
$31.50/mo
AWS
$53.50/mo
Run Semantic Kernel on Dedicated Cloud CPU
Deploy your Semantic Kernel application on Fluence with dedicated vCPUs for agent logic, tools and background workers. Connect a supported model API to handle inference.
Fluence
$2.54/mo
Hetzner
$14.00/mo
DigitalOcean
$31.50/mo
AWS
$53.50/mo
Dedicated hosting for Semantic Kernel agents
Give your agent application dedicated CPU capacity, persistent storage and private networking for supporting services.
Dedicated hosting for Semantic Kernel agents
Give your agent application dedicated CPU capacity, persistent storage and private networking for supporting services.
A hosting stack for Semantic Kernel agents
Run application logic and tools on Fluence. Connect model inference, data storage and external services separately.
A hosting stack for Semantic Kernel agents
Run application logic and tools on Fluence. Connect model inference, data storage and external services separately.
Bring your Semantic Kernel application online
Deploy your C# or Python application using a CPU VM and a container or Linux service.
1
Choose your instance
Create a Dedicated CPU instance and configure persistent storage, networking and application access.
2
Deploy a pinned application
Deploy your C# or Python application with pinned Semantic Kernel packages and a compatible model connector.
3
Connect and test
Connect model credentials and data services, then test authentication, tool execution and conversation restoration. Setup caveat: Pin dependencies and validate connector support. Multi-agent orchestration and Agent RAG remain experimental; vector APIs are in preview. Configure permissions, backups and recovery for the application.
Bring your Semantic Kernel application online
Deploy your C# or Python application using a CPU VM and a container or Linux service.
1
Choose your instance
Create a Dedicated CPU instance and configure persistent storage, networking and application access.
2
Deploy a pinned application
Deploy your C# or Python application with pinned Semantic Kernel packages and a compatible model connector.
3
Connect and test
Connect model credentials and data services, then test authentication, tool execution and conversation restoration. Setup caveat: Pin dependencies and validate connector support. Multi-agent orchestration and Agent RAG remain experimental; vector APIs are in preview. Configure permissions, backups and recovery for the application.
Know your Semantic Kernel hosting costs
Estimate CPU capacity, storage and public-IP charges for your application. Add model usage and supporting services separately when planning the complete operating budget.
No Fluence egress fees
Estimates before VM provisioning
Compute, storage and IP breakdown
Note: Estimates cover CPU hosting. Model APIs, additional services and self-hosted inference are priced separately.
Know your Semantic Kernel hosting costs
Estimate CPU capacity, storage and public-IP charges for your application. Add model usage and supporting services separately when planning the complete operating budget.
No Fluence egress fees
Estimates before VM provisioning
Compute, storage and IP breakdown
Note: Estimates cover CPU hosting. Model APIs, additional services and self-hosted inference are priced separately.
Semantic Kernel on dedicated CPU vs. shared hosting
Compare dedicated and shared CPU hosting for agent applications, from light API services to sustained tool processing.
Criteria
Fluence Dedicated CPU
Shared VPS
CPU
Dedicated vCPUs
Shared vCPUs
Runtime control
VM-level
Usually VM-level
Storage
Persistent volumes available
Provider-dependent
Private networking
VPC and security groups
Provider-dependent
Pricing
Itemized resource estimates
Provider-dependent
Egress
No Fluence egress fees
Provider-dependent
Semantic Kernel on dedicated CPU vs. shared hosting
Compare dedicated and shared CPU hosting for agent applications, from light API services to sustained tool processing.
Fluence Dedicated CPU
Shared VPS
CPU
Dedicated vCPUs
Shared vCPUs
Runtime control
VM-level
Usually VM-level
Storage
Persistent volumes available
Provider-dependent
Private networking
VPC and security groups
Provider-dependent
Pricing
Itemized resource estimates
Provider-dependent
Egress
No Fluence egress fees
Provider-dependent
Why host Semantic Kernel on Fluence
Why host Semantic Kernel on Fluence
What you can build with Semantic Kernel on Fluence
What you can build with Semantic Kernel on Fluence
Cloud compute from global providers
Select available CPU capacity by provider and location to suit your Semantic Kernel application and its connected services.
Cloud compute from global providers
Select available CPU capacity by provider and location to suit your Semantic Kernel application and its connected services.
FAQ
Can Semantic Kernel run on Fluence?
Is Semantic Kernel still maintained?
Can I run Semantic Kernel with Docker?
Does Semantic Kernel need a GPU?
Can I connect models, tools and databases?
Show more
FAQ
Can Semantic Kernel run on Fluence?
Is Semantic Kernel still maintained?
Can I run Semantic Kernel with Docker?
Does Semantic Kernel need a GPU?
Can I connect models, tools and databases?
Show more
Be the first to know
Be the first to know