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

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

Overview

Provider type
Specialist
Specialist
Founded
2022
2014
Headquarters
San Francisco, CA
Los Angeles, CA
Billing model
Per-token (inference), On-demand / Reserved (GPU cloud)
On-demand
Min commitment
None
None
Support tier
Community → Enterprise
Standard → Enterprise
Regions
2 regions
6 regions

Strengths & Best For

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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Zenlayer

Zenlayer is an edge cloud provider offering GPU instances and VPS across emerging markets in Asia, Middle East, Africa, and Latin America — regions underserved by major hyperscalers. On-demand GPU rental with competitive pricing makes it a practical choice for teams that need GPU compute close to users in emerging markets, or for AI inference workloads requiring low latency in APAC, MENA, or LATAM regions.

Strengths
  • Emerging market coverage — APAC, MENA, LATAM, Africa
  • Edge GPU instances close to end users
  • Competitive pricing in underserved regions
  • VPS + GPU in one provider
Weaknesses
  • Smaller GPU catalog vs major providers
  • Less mature ML tooling
  • Support quality varies by region
Best For
AI inference serving users in emerging marketsTeams needing GPU compute in APAC/MENA/LATAMLow-latency inference for global user bases
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Live GPU Pricing

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Region Coverage

Zenlayer — 6 regions
APACMELATAMAfricaUSEU

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Key differences

Together AI

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

Zenlayer

Edge GPU cloud with VPS and GPUs across emerging markets

AttributeTogether AIZenlayer
Provider typespecialistspecialist
Billing modelPer-token (inference), On-demand / Reserved (GPU cloud)On-demand
Min commitmentNoneNone
Regions2 (US-East, US-West)6 (APAC, ME, LATAM, +3)
Support tierCommunity → EnterpriseStandard → Enterprise
Best forTeams wanting inference API + GPU training from one vendor, Fine-tuning Llama, Mistral, or Qwen at scaleAI inference serving users in emerging markets, Teams needing GPU compute in APAC/MENA/LATAM
Founded / HQ2022 · San Francisco, CA2014 · Los Angeles, CA