Nebius AI Cloud vs Together AI
Detailed side-by-side comparison to help you choose the right tool
Nebius AI Cloud
Automation & Workflows
Cloud infrastructure platform designed for AI workloads, offering scalable GPU clusters with NVIDIA hardware and optimized orchestration for training and inference.
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Starting Price
CustomTogether AI
🔴DeveloperAI Model Hosting & Inference
AI-native cloud for inference, fine-tuning, and dedicated GPU clusters, offering 200+ open-source and frontier-class models behind an OpenAI-compatible API plus reserved H100/H200/B200 capacity.
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Starting Price
$0.02/1M tokensFeature Comparison
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💡 Our Take
Choose Nebius if you need raw compute control — your own clusters, your own orchestration, your own framework stack — including the ability to train foundation models from scratch. Choose Together AI if you mainly want a managed inference API for open-source LLMs and fine-tuning without managing GPUs, clusters, or Kubernetes yourself.
Nebius AI Cloud - Pros & Cons
Pros
- ✓Reference Platform NVIDIA Cloud Partner status — a tier reserved for select partners operating large clusters built in coordination with NVIDIA's tested reference architecture
- ✓Access to cutting-edge NVIDIA GPUs including GB300 NVL72 and GB200 NVL72 in addition to H100 and H200
- ✓Verified customer cost savings — CentML reported 5x lower inference costs compared to other major providers
- ✓EU-based compute capacity (data center outside Helsinki) supports data-residency and regulatory compliance requirements
- ✓24/7 solution architect assistance for multi-node cases is included at no additional charge
- ✓Operates ISEG, the #19 most powerful supercomputer in the world, giving credible evidence of large-cluster capability
Cons
- ✗Pricing is not fully transparent on the homepage — custom quotes require contacting sales for enterprise configurations
- ✗Smaller global footprint than AWS, GCP, or Azure — limited regional options outside Europe may affect latency-sensitive workloads
- ✗Focused specifically on AI/ML compute rather than being a general-purpose cloud (no broad PaaS, serverless, or consumer-web services)
- ✗Advanced features like InfiniBand clusters and managed Slurm target experienced ML engineers rather than beginners
- ✗Smaller third-party ecosystem and marketplace compared to hyperscaler competitors
Together AI - Pros & Cons
Pros
- ✓Breadth of open-weight model catalog (200+) with one OpenAI-compatible API
- ✓One account spans serverless, dedicated endpoints, fine-tuning, and reserved GPU capacity
- ✓Transparent per-token pricing — easy to model unit economics against closed providers
- ✓InfiniBand-backed GPU Clusters are credible for real training, not just inference
Cons
- ✗Frontier-class reasoning still lags closed models on the hardest benchmarks
- ✗Fastest single-model latency is sometimes beaten by Groq or Cerebras
- ✗Many model variants means model selection itself becomes a project
- ✗Dedicated endpoint cost calculations require attention to GPU type and utilization
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