Beam vs Together AI: Pricing, Specs, Strengths & Weaknesses
Side-by-side comparison of GPU compute pricing, regions, billing models, and strengths for Beam and Together AI. Rates sourced from public APIs — verify current availability and billing terms before purchasing.
Overview
Strengths & Best For
Beam is a serverless GPU platform that lets developers deploy AI models and run H100, A100, and T4 compute jobs with automatic scaling and per-second pay-per-use billing — no infrastructure management required. A Python-native SDK (`beam deploy`) and fast cold starts make it easy to build and ship LLM inference APIs, batch ML pipelines, and AI model serving endpoints quickly. Beam handles container builds, GPU provisioning, and scaling automatically. Free tier available. A strong choice for Python-first teams that want serverless GPU infrastructure with predictable, usage-based pricing.
- Python-native SDK — `beam deploy` ships a GPU endpoint in minutes
- Auto-scaling with fast cold starts — no idle cost
- Per-second billing — pay only for actual compute time
- Free tier available for prototyping
- Handles container builds and GPU provisioning automatically
- Less control over underlying infrastructure vs raw VMs
- Smaller GPU catalog than dedicated cluster providers
- Not suitable for multi-node distributed training
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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
Beam
Serverless GPU infrastructure — Python-native, auto-scaling, per-second billing
Together AI
Full-stack AI platform — 200+ models, GPU cloud, fine-tuning, and Together MoE optimization