A Hyperstack alternative for more GPU deployment options

Compare Fluence and Hyperstack on GPU costs, provider choice, deployment options, managed services, and the resources your training and inference workloads require.

View available GPUs

Compare GPU costs

A Hyperstack alternative for more GPU deployment options

Compare Fluence and Hyperstack on GPU costs, provider choice, deployment options, managed services, and the resources your training and inference workloads require.

View available GPUs

Compare GPU costs

Compare GPU configurations across multiple providers and regions

Run containers, VMs, or bare metal through one console

Keep data transfer predictable with unlimited bandwidth and zero egress fees

Compare GPU configurations across multiple providers and regions

Run containers, VMs, or bare metal through one console

Keep data transfer predictable with unlimited bandwidth and zero egress fees

Fluence vs Hyperstack at a glance

Fluence

Hyperstack

Best for

Provider and deployment choice

GPU VMs and managed AI services

Core experience

Multi-provider GPU cloud

Integrated VM and cluster management

Deployment options

Containers, VMs, bare metal

GPU VMs, Kubernetes, dedicated inference

Provider model

Multiple infrastructure providers

Hyperstack regional cloud

Platform tooling

Console, API, preset or custom images

Console, API, Terraform, snapshots

Cost model

Hourly rates with zero egress fees

On-demand, spot, and reserved rates; zero egress

Compare the full cost of GPU compute

Hyperstack bundles CPU, RAM, and local storage with GPU VMs. Public IPs and shared storage cost extra. Fluence configurations vary by provider. Compare GPU specifications, runtime, storage, and support to estimate total costs. Need another GPU model or configuration? Share your requirements and our team will get back to you.

Compare the full cost of GPU compute

Choose how your GPU workloads run

GPU containers

Deploy packaged inference services and batch jobs without maintaining a full operating system.

GPU VMs

Configure the operating system, services, and drivers required by your AI stack.

GPU bare metal

Use dedicated physical servers when isolation and direct hardware access are essential.

Choose how your GPU workloads run

GPU containers

Deploy packaged inference services and batch jobs without maintaining a full operating system.

GPU VMs

Configure the operating system, services, and drivers required by your AI stack.

GPU bare metal

Use dedicated physical servers when isolation and direct hardware access are essential.

More choice across your GPU stack

Choose who supplies your GPUs

Compare offers from different infrastructure providers by region, hardware configuration, and price.

Select the deployment format

Launch containers, VMs, or bare metal with the access and isolation your application requires.

Size compute around the workload

Compare GPU count, VRAM, CPU, and storage together to avoid paying for resources you do not need.

More choice across your GPU stack

Choose who supplies your GPUs

Compare offers from different infrastructure providers by region, hardware configuration, and price.

Select the deployment format

Launch containers, VMs, or bare metal with the access and isolation your application requires.

Size compute around the workload

Compare GPU count, VRAM, CPU, and storage together to avoid paying for resources you do not need.

Built for AI workloads you manage

Run your preferred stack.

Production inference

Serve models with control over batching, runtime configuration, network access, and GPU allocation.

GPU container or GPU VM

Fine-tuning pipelines

Run adapters, checkpoints, and custom training cycles with your preferred frameworks and storage.

GPU VM or GPU bare metal

LLM development

Evaluate open models, quantization settings, and inference engines in a reproducible environment.

GPU container or GPU VM

Training runs

Run sustained single-server or multi-GPU jobs with control over dependencies, storage, and hardware access.

GPU VM or GPU bare metal

Batch inference

Generate embeddings and process datasets, documents, or media in repeatable GPU batches.

GPU container

Model research

Compare architectures, context lengths, and precision formats while keeping experiment environments consistent.

GPU container or GPU VM

AI agent inference

Self-host the models behind AI agents while retaining control over application services and data.

GPU container or GPU VM

Built for AI workloads you manage

Run your preferred stack.

Production inference

Serve models with control over batching, runtime configuration, network access, and GPU allocation.

GPU container or GPU VM

Fine-tuning pipelines

Run adapters, checkpoints, and custom training cycles with your preferred frameworks and storage.

GPU VM or GPU bare metal

LLM development

Evaluate open models, quantization settings, and inference engines in a reproducible environment.

GPU container or GPU VM

Training runs

Run sustained single-server or multi-GPU jobs with control over dependencies, storage, and hardware access.

GPU VM or GPU bare metal

Batch inference

Generate embeddings and process datasets, documents, or media in repeatable GPU batches.

GPU container

Model research

Compare architectures, context lengths, and precision formats while keeping experiment environments consistent.

GPU container or GPU VM

AI agent inference

Self-host the models behind AI agents while retaining control over application services and data.

GPU container or GPU VM

Compare platforms with one GPU workload

Test one workload from your Hyperstack environment before making a broader move. Measure the complete deployment: performance, setup effort, and ongoing cost.

Choose a workload

Match GPU and memory requirements

Select a provider and deployment type

Run under expected production load

Compare performance and total cost

Compare platforms with one GPU workload

Test one workload from your Hyperstack environment before making a broader move. Measure the complete deployment: performance, setup effort, and ongoing cost.

Choose a workload

Match GPU and memory requirements

Select a provider and deployment type

Run under expected production load

Compare performance and total cost