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GPUaaS vs Together AI: Pricing, Specs, Strengths & Weaknesses

Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for GPUaaS and Together AI. Rates sourced from public APIs — verify current availability and billing terms before purchasing.

Overview

Provider type
Specialist
Specialist
Founded
2022
2022
Headquarters
Europe
San Francisco, CA
Billing model
On-demand
Per-token (inference), On-demand / Reserved (GPU cloud)
Min commitment
None
None
Support tier
Standard
Community → Enterprise
Regions
1 regions
2 regions

Strengths & Best For

GPUaaS

GPUaaS delivers H100 and A100 GPU compute as a fully managed service, enabling European AI teams to access high-performance GPU infrastructure without any infrastructure overhead or operational complexity. On-demand billing and a managed service model make it easy to scale AI training and inference workloads without dedicated DevOps resources. A strong choice for European AI teams that want managed GPU-as-a-service with EU data residency and minimal operational burden.

Strengths
  • Managed service
  • Simple onboarding
  • European presence
Weaknesses
  • Small provider — limited GPU catalog
  • Fewer regions than larger providers
  • Less mature ecosystem vs CoreWeave
Best For
Teams avoiding infrastructureEuropean AI workloadsManaged inference
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Together AI

Together AI is a full-stack AI platform combining a high-throughput inference API (200+ open-source models), dedicated GPU clusters (H100 SXM, A100), and a managed fine-tuning service. Together MoE optimization cuts costs on large mixture-of-experts models. Function calling and JSON mode across all major models. Fine-tuning: LoRA and full fine-tune for Llama, Mistral, and Qwen. GPU cloud: H100 SXM and A100 clusters for large-scale training. Check api.together.ai/models for current per-token rates.

Strengths
  • 200+ open-source models — wide inference API catalog
  • Llama 4 Scout — competitive frontier-open pricing
  • Together MoE optimization reduces cost on large MoE models
  • Managed fine-tuning: LoRA and full fine-tune for Llama, Mistral, Qwen
  • Function calling and JSON mode across all major models
  • GPU cloud + inference API from one vendor
Weaknesses
  • GPU cloud side less polished than pure-play providers like CoreWeave
  • Inference API latency trails Fireworks and Cerebras on speed benchmarks
  • Enterprise SLAs require custom contracts — not self-serve
Best For
Teams wanting inference API + GPU training from one vendorFine-tuning Llama, Mistral, or Qwen at scaleCost-conscious teams replacing OpenAI with open-source modelsResearchers needing broad model access without self-hosting
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Key differences

GPUaaS

GPU as a Service

Together AI

Full-stack AI platform — 200+ models, GPU cloud, fine-tuning, and Together MoE optimization

AttributeGPUaaSTogether AI
Provider typespecialistspecialist
Billing modelOn-demandPer-token (inference), On-demand / Reserved (GPU cloud)
Min commitmentNoneNone
Regions1 (EU)2 (US-East, US-West)
Support tierStandardCommunity → Enterprise
Best forTeams avoiding infrastructure, European AI workloadsTeams wanting inference API + GPU training from one vendor, Fine-tuning Llama, Mistral, or Qwen at scale
Founded / HQ2022 · Europe2022 · San Francisco, CA