Northflank vs Together AI: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Northflank and Together AI. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
Northflank is a developer-focused PaaS platform offering GPU compute (L4, A100 40GB, A100 80GB, H100 80GB, RTX PRO 6000) alongside managed containers, CI/CD pipelines, databases, and cron jobs — all in one platform. GPU instances are available on-demand in US and EU regions. The platform is designed for teams that want to run GPU workloads as part of a broader application stack without managing raw cloud infrastructure. A strong choice for ML engineers who want GPU compute tightly integrated with deployment, scheduling, and application services.
- GPU compute integrated with PaaS — containers, CI/CD, databases in one platform
- H100 80GB and RTX PRO 6000 availability
- US and EU regions
- Developer-friendly platform with managed services
- On-demand billing with no minimum commitment
- PaaS model — less control than raw GPU clouds
- Higher per-GPU cost than bare infrastructure
- Smaller GPU catalog than specialist GPU clouds
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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.
- 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
- 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
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Key differences
Northflank
PaaS GPU compute — L4, A100, H100, RTX PRO 6000 with managed containers and CI/CD
Together AI
Full-stack AI platform — 200+ models, GPU cloud, fine-tuning, and Together MoE optimization