A Together AI alternative at 69% lower costs
Compare Fluence and Together AI across GPU pricing, provider choice, deployment control, managed AI services, and infrastructure requirements for training and inference.
A Together AI alternative at 69% lower costs
Compare Fluence and Together AI across GPU pricing, provider choice, deployment control, managed AI services, and infrastructure requirements for training and inference.
Access GPU capacity across multiple providers and regions
Run containers, virtual machines, or bare metal through one platform
Keep GPU costs clear with hourly pricing, unlimited bandwidth, and zero egress fees
Access GPU capacity across multiple providers and regions
Run containers, virtual machines, or bare metal through one platform
Keep GPU costs clear with hourly pricing, unlimited bandwidth, and zero egress fees
Fluence vs Together AI at a glance
Category
Fluence
Together AI
Best for
Self-managed AI workloads and custom infrastructure
Managed inference, fine-tuning, and large GPU clusters
Core experience
Direct infrastructure control across providers
Managed model services and cluster operations
Deployment options
GPU containers, VMs, bare metal
Serverless and dedicated inference, dedicated containers, Kubernetes and Slurm clusters
Provider flexibility
Capacity across multiple providers
Together AI-operated infrastructure
Platform tooling
Console, API, preset or custom images
APIs, CLI, Terraform, SkyPilot, managed orchestration
Cost model
Transparent hourly pricing with zero egress fees
Per-token and per-GPU pricing across on-demand, reserved, and preemptible capacity
Fluence vs Together AI at a glance
Fluence
Together AI
Best for
Self-managed AI workloads and custom infrastructure
Managed inference, fine-tuning, and large GPU clusters
Core experience
Direct infrastructure control across providers
Managed model services and cluster operations
Deployment options
GPU containers, VMs, bare metal
Serverless and dedicated inference, dedicated containers, Kubernetes and Slurm clusters
Provider flexibility
Capacity across multiple providers
Together AI-operated infrastructure
Platform tooling
Console, API, preset or custom images
APIs, CLI, Terraform, SkyPilot, managed orchestration
Cost model
Transparent hourly pricing with zero egress fees
Per-token and per-GPU pricing across on-demand, reserved, and preemptible capacity
Compare the real cost of GPU compute
Together AI prices serverless inference by token and GPU clusters by GPU-hour. Fluence pricing varies by provider, GPU model, and deployment type. Compare the full configuration, runtime, and managed services to understand the real cost of each option. Need another GPU model or setup? Send us your requirements and we will get back to you.
Compare the real cost of GPU compute
Together AI prices serverless inference by token and GPU clusters by GPU-hour. Fluence pricing varies by provider, GPU model, and deployment type. Compare the full configuration, runtime, and managed services to understand the real cost of each option. Need another GPU model or setup? Send us your requirements and we will get back to you.
Run GPU workloads on your terms
GPU containers
Package inference, batch, and development workloads without managing a full operating system.
GPU VMs
Use a complete operating system for persistent services, custom drivers, and system-level tooling.
GPU bare metal
Run directly on dedicated servers for long training jobs, strict isolation, and hardware-level control.
Run GPU workloads on your terms
GPU containers
Package inference, batch, and development workloads without managing a full operating system.
GPU VMs
Use a complete operating system for persistent services, custom drivers, and system-level tooling.
GPU bare metal
Run directly on dedicated servers for long training jobs, strict isolation, and hardware-level control.
Where Fluence gives you more control
Choose the infrastructure layer
Select containers, VMs, or bare metal according to the runtime access, isolation, and control required by each workload.
Compare offers across providers
Review GPU models, regions, configurations, and hourly rates before deciding where to deploy.
Scale from one GPU
Use single-GPU or multi-GPU capacity when the workload does not require a complete managed cluster.
Keep your existing toolchain
Bring your preferred images, runtimes, and automation through the Fluence console and API.
Where Fluence gives you more control
Choose the infrastructure layer
Select containers, VMs, or bare metal according to the runtime access, isolation, and control required by each workload.
Compare offers across providers
Review GPU models, regions, configurations, and hourly rates before deciding where to deploy.
Scale from one GPU
Use single-GPU or multi-GPU capacity when the workload does not require a complete managed cluster.
Keep your existing toolchain
Bring your preferred images, runtimes, and automation through the Fluence console and API.
Built for self-managed GPU workloads
Fluence is well suited for:
Production inference
Operate model-serving endpoints with control over runtime configuration, networking, and GPU resources.
GPU container or GPU VM
Fine-tuning pipelines
Run adapters, checkpoints, and custom training cycles in persistent GPU environments.
GPU VM or GPU bare metal
LLM development
Test open models, quantization methods, and serving frameworks with your preferred dependencies.
GPU container or GPU VM
Training jobs
Run single-node or multi-GPU workloads with direct control over software, storage, and hardware access.
GPU VM or GPU bare metal
Batch inference
Process documents, embeddings, media, and model outputs through repeatable GPU jobs.
GPU container
Model research
Compare architectures, context settings, and precision formats in isolated, repeatable environments.
GPU container or GPU VM
AI agent inference
Host models and supporting services for agent applications while retaining control over the surrounding stack.
GPU container or GPU VM
Built for self-managed GPU workloads
Fluence is well suited for:
Production inference
Operate model-serving endpoints with control over runtime configuration, networking, and GPU resources.
GPU container or GPU VM
Fine-tuning pipelines
Run adapters, checkpoints, and custom training cycles in persistent GPU environments.
GPU VM or GPU bare metal
LLM development
Test open models, quantization methods, and serving frameworks with your preferred dependencies.
GPU container or GPU VM
Training jobs
Run single-node or multi-GPU workloads with direct control over software, storage, and hardware access.
GPU VM or GPU bare metal
Batch inference
Process documents, embeddings, media, and model outputs through repeatable GPU jobs.
GPU container
Model research
Compare architectures, context settings, and precision formats in isolated, repeatable environments.
GPU container or GPU VM
AI agent inference
Host models and supporting services for agent applications while retaining control over the surrounding stack.
GPU container or GPU VM
Test Fluence with one GPU workload
Recreate one representative workload and compare cost, setup, performance, and infrastructure control before expanding the deployment.
1
Choose a workload
2
Match its GPU and memory requirements
3
Select container, VM, or bare metal
4
Run under production-like conditions
5
Compare cost, performance, and control
Test Fluence with one GPU workload
Recreate one representative workload and compare cost, setup, performance, and infrastructure control before expanding the deployment.
1
Choose a workload
2
Match its GPU and memory requirements
3
Select container, VM, or bare metal
4
Run under production-like conditions
5
Compare cost, performance, and control
FAQ
How do Together AI and Fluence differ?
How much can Fluence save compared with Together AI?
Which GPUs are available on Together AI and Fluence?
Do both platforms support single-GPU and multi-GPU workloads?
How does regional availability compare?
Show more
FAQ
How do Together AI and Fluence differ?
How much can Fluence save compared with Together AI?
Which GPUs are available on Together AI and Fluence?
Do both platforms support single-GPU and multi-GPU workloads?
How does regional availability compare?
Show more
Run AI workloads on infrastructure you control
Compare GPU offers across providers and choose the deployment model, region, and configuration required by your stack.