A Runpod alternative at 80% lower cost

Compare Fluence and Runpod on GPU pricing, deployment control, provider flexibility, billing predictability, networking assumptions, and workload setup across containers, VMs, and bare metal.

View available GPUs

Compare GPU costs

A Runpod alternative at 80% lower cost

Compare Fluence and Runpod on GPU pricing, deployment control, provider flexibility, billing predictability, networking assumptions, and workload setup 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 Runpod at a glance

Fluence

Runpod

Main use case

Infrastructure-led GPU workloads

Fast GPU starts, Pods, Serverless, and developer workflows

Core experience

Direct GPU infrastructure access

Managed AI cloud experience

Deployment options

Containers, VMs, bare metal

GPU Pods, Serverless endpoints, clusters

Provider flexibility

Access across multiple providers globally

Runpod platform environment across cloud tiers

Cost model

Predictable hourly pricing with zero egress fees

Fine-grained billing, savings plans, spot, and no Pod ingress/egress fees

Runtime control

Custom images, SSH keys, env vars, forwarded ports, UDP port formats

Templates, custom images, SDKs, CLI, and Serverless scaling

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

Deploy GPU workloads your way

GPU containers

Package inference services, batch jobs, eval runs, and repeatable experiments in custom images. A practical path for teams that want fast deployment without giving up runtime control.

GPU VMs

Run persistent services, custom environments, orchestration layers, and model-serving stacks with OS-level control. Use VMs when your workload needs a more configurable operating environment.

GPU bare metal

Run large training jobs, hardware-sensitive workloads, strict isolation requirements, and high-performance pipelines that benefit from direct hardware access.

Deploy GPU workloads your way

GPU containers

Package inference services, batch jobs, eval runs, and repeatable experiments in custom images. A practical path for teams that want fast deployment without giving up runtime control.

GPU VMs

Run persistent services, custom environments, orchestration layers, and model-serving stacks with OS-level control. Use VMs when your workload needs a more configurable operating environment.

GPU bare metal

Run large training jobs, hardware-sensitive workloads, strict isolation requirements, and high-performance pipelines that benefit from direct hardware access.

Where Fluence is different

Use the deployment primitive you need

Start with containers for packaged workloads, use VMs for deeper environment control, and use bare metal when direct GPU access matters.

Control placement and provider choice

Select GPU capacity by provider, location, and available offer instead of building every workload around one platform workflow.

Plan spend around sustained workloads

Runpod’s fine-grained billing works well for short jobs. Fluence gives teams predictable hourly pricing for workloads that run longer and need clearer cost planning.

Support real service requirements

Use custom images, SSH keys, environment variables, forwarded ports, and UDP-friendly port configuration when your architecture needs more than a simple managed endpoint.

Where Fluence is different

Use the deployment primitive you need

Start with containers for packaged workloads, use VMs for deeper environment control, and use bare metal when direct GPU access matters.

Control placement and provider choice

Select GPU capacity by provider, location, and available offer instead of building every workload around one platform workflow.

Plan spend around sustained workloads

Runpod’s fine-grained billing works well for short jobs. Fluence gives teams predictable hourly pricing for workloads that run longer and need clearer cost planning.

Support real service requirements

Use custom images, SSH keys, environment variables, forwarded ports, and UDP-friendly port configuration when your architecture needs more than a simple managed endpoint.

Built for GPU workloads that need control

Fluence is well suited for:

Inference APIs

Run model-serving workloads with control over runtime, provider choice, networking, and infrastructure cost.

GPU container or GPU VM

Fine-tuning runs

Use dedicated GPU capacity for adapters, checkpoints, custom training cycles, and repeatable experiments.

GPU VM or GPU bare metal

LLM application development

Build and test GPU-backed workloads without being locked into a Serverless-first workflow.

GPU container or GPU VM

Training pipelines

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

GPU VM or GPU bare metal

Batch inference

Process prompts, media, embeddings, documents, or model outputs in repeatable GPU jobs.

GPU container

Simulation and rendering

Run GPU-intensive workloads that need predictable capacity and deeper infrastructure control.

GPU VM or GPU bare metal

AI agent model backends

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

GPU container or GPU VM

Built for GPU workloads that need control

Fluence is well suited for:

Inference APIs

Run model-serving workloads with control over runtime, provider choice, networking, and infrastructure cost.

GPU container or GPU VM

Fine-tuning runs

Use dedicated GPU capacity for adapters, checkpoints, custom training cycles, and repeatable experiments.

GPU VM or GPU bare metal

LLM application development

Build and test GPU-backed workloads without being locked into a Serverless-first workflow.

GPU container or GPU VM

Training pipelines

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

GPU VM or GPU bare metal

Batch inference

Process prompts, media, embeddings, documents, or model outputs in repeatable GPU jobs.

GPU container

Simulation and rendering

Run GPU-intensive workloads that need predictable capacity and deeper infrastructure control.

GPU VM or GPU bare metal

AI agent model backends

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

GPU container or GPU VM

Test Fluence with one GPU workload

You do not need to replace every Runpod Pod or Serverless endpoint at once. Start with one workload where cost, runtime control, provider placement, or networking behavior matters.

Pick a GPU model

Choose container, VM, or bare metal

Select a region, provider, or available offer

Run a small test workload

Compare cost, setup experience, and operational control

Test Fluence with one GPU workload

You do not need to replace every Runpod Pod or Serverless endpoint at once. Start with one workload where cost, runtime control, provider placement, or networking behavior matters.

Pick a GPU model

Choose container, VM, or bare metal

Select a region, provider, or available offer

Run a small test workload

Compare cost, setup experience, and operational control