A Paperspace alternative at 80% lower cost

Compare Fluence and Paperspace on GPU pricing, deployment control, provider flexibility, bandwidth assumptions, and workload fit across containers, VMs, and bare metal.

View available GPUs

Compare GPU costs

A Paperspace alternative at 80% lower cost

Compare Fluence and Paperspace on GPU pricing, deployment control, provider flexibility, bandwidth assumptions, and workload fit across containers, VMs, and bare metal.

View available GPUs

Compare GPU costs

Access GPU capacity across multiple providers globally

Predictable pricing with unlimited bandwidth and no egress fees

Deploy containers, VMs, or bare metal GPUs at up to 80% lower cost vs. hyperscalers

Access GPU capacity across multiple providers globally

Predictable pricing with unlimited bandwidth and no egress fees

Deploy containers, VMs, or bare metal GPUs at up to 80% lower cost vs. hyperscalers

Fluence vs Paperspace at a glance

Fluence

Paperspace

Best for

Production GPU workloads

Fast starts and managed AI workflows

Core experience

Full infrastructure control

Workflow simplicity

Deployment options

Containers, VMs, bare metal

Notebooks, Machines, Deployments

Provider flexibility

Access across providers globally

Single-vendor environment

Cost model

Predictable hourly pricing, zero egress fees

Usage-based pricing

Compare the real cost of GPU compute

GPU pricing can look simple at small scale. It gets harder to manage as workloads run longer and grow across environments. Fluence is designed to give teams a more transparent and predictable GPU cost model.

Compare the real cost of GPU compute

Run GPU workloads as containers, VMs, or bare metal

GPU Containers

Inference, experiments, batch jobs, repeatable workloads. Fast path to launch containerized GPU workloads.

GPU VMs

Custom environments, persistent services, orchestration, OS-level control. More control over the operating environment and runtime setup.

Bare Metal

Large training jobs, hardware-sensitive workloads, strict isolation. Direct hardware access and maximum infrastructure control.

Run GPU workloads as containers, VMs, or bare metal

GPU Containers

Inference, experiments, batch jobs, repeatable workloads. Fast path to launch containerized GPU workloads.

GPU VMs

Custom environments, persistent services, orchestration, OS-level control. More control over the operating environment and runtime setup.

Bare Metal

Large training jobs, hardware-sensitive workloads, strict isolation. Direct hardware access and maximum infrastructure control.

Where Fluence is different

More infrastructure control

Production workloads often need tighter control over runtime environments, deployment models, and operating conditions.

More provider flexibility

As GPU demand grows, teams want more choice in where workloads run and how capacity is sourced.

Better fit for production workloads

Notebook-led workflows are useful early on, but they do not always map cleanly to large-scale inference, fine-tuning, or sustained training workloads.

Clearer cost planning

As spend increases, pricing transparency becomes more important. Teams need a cost model they can understand and forecast.

Where Fluence is different

More infrastructure control

Production workloads often need tighter control over runtime environments, deployment models, and operating conditions.

More provider flexibility

As GPU demand grows, teams want more choice in where workloads run and how capacity is sourced.

Better fit for production workloads

Notebook-led workflows are useful early on, but they do not always map cleanly to large-scale inference, fine-tuning, or sustained training workloads.

Clearer cost planning

As spend increases, pricing transparency becomes more important. Teams need a cost model they can understand and forecast.

Built for production GPU use cases

Fluence is well suited for:

Production inference

Run model-serving workloads with more control over runtime, environment, and infrastructure costs.

GPU container or GPU VM

Model fine-tuning

Use dedicated GPU capacity for custom training runs, adapters, checkpoints, and experiment cycles.

GPU VM or GPU bare metal

LLM development

Test, iterate, and run GPU-backed workloads without being locked into a notebook-first workflow.

GPU container or GPU VM

Training pipelines

Run longer GPU jobs with control over environment, storage, and deployment setup.

GPU VM or GPU bare metal

Batch inference

Process large volumes of prompts, media, embeddings, or model outputs in repeatable jobs.

GPU container

Simulation and rendering

Run GPU-intensive workloads that benefit from direct infrastructure control and predictable capacity.

GPU VM or GPU bare metal

AI agent inference workloads

Support agent systems that depend on GPU-backed model inference, while keeping the application stack under your control.

GPU container or GPU VM

Built for production GPU use cases

Fluence is well suited for:

Production inference

Run model-serving workloads with more control over runtime, environment, and infrastructure costs.

GPU container or GPU VM

Model fine-tuning

Use dedicated GPU capacity for custom training runs, adapters, checkpoints, and experiment cycles.

GPU VM or GPU bare metal

LLM development

Test, iterate, and run GPU-backed workloads without being locked into a notebook-first workflow.

GPU container or GPU VM

Training pipelines

Run longer GPU jobs with control over environment, storage, and deployment setup.

GPU VM or GPU bare metal

Batch inference

Process large volumes of prompts, media, embeddings, or model outputs in repeatable jobs.

GPU container

Simulation and rendering

Run GPU-intensive workloads that benefit from direct infrastructure control and predictable capacity.

GPU VM or GPU bare metal

AI agent inference workloads

Support agent systems that depend on GPU-backed model inference, while keeping the application stack under your control.

GPU container or GPU VM

Try Fluence with one GPU workload

You do not need to migrate your full workflow to compare Fluence with Paperspace. Start with one workload and evaluate cost, control, and operational fit.

Choose a GPU model

Select container, VM, or bare metal

Pick a region, provider, or available offer

Launch a small test workload

Compare cost, setup experience, and infrastructure control

Try Fluence with one GPU workload

You do not need to migrate your full workflow to compare Fluence with Paperspace. Start with one workload and evaluate cost, control, and operational fit.

Choose a GPU model

Select container, VM, or bare metal

Pick a region, provider, or available offer

Launch a small test workload

Compare cost, setup experience, and infrastructure control