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

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

Overview

Provider type
Specialist
Specialist
Founded
2025
2022
Headquarters
United States
San Francisco, CA
Billing model
On-demand hourly and monthly
Per-token (inference), On-demand / Reserved (GPU cloud)
Min commitment
None
None
Support tier
Standard
Community → Enterprise
Regions
2 regions
2 regions

Strengths & Best For

Packet AI

Packet AI offers dedicated single-tenant and dynamic scheduler-managed shared GPU instances for AI workloads. Both deployment options are available with on-demand hourly and monthly billing. Packet AI also provides Token Factory, a managed LLM inference API with an OpenAI-compatible endpoint for teams that want to serve models without operating their own inference stack.

Strengths
  • Dedicated and dynamic GPU instances
  • Hourly and monthly billing
  • Managed OpenAI-compatible LLM inference API
  • Single-tenant deployment option
Weaknesses
  • Smaller ecosystem than hyperscale clouds
  • Some GPU configurations are region-specific
Best For
Dedicated GPU workloadsScheduler-managed GPU capacityManaged LLM 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

Packet AI

Dedicated and dynamic GPU instances with managed LLM inference

Together AI

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

AttributePacket AITogether AI
Provider typespecialistspecialist
Billing modelOn-demand hourly and monthlyPer-token (inference), On-demand / Reserved (GPU cloud)
Min commitmentNoneNone
Regions2 (US, UK)2 (US-East, US-West)
Support tierStandardCommunity → Enterprise
Best forDedicated GPU workloads, Scheduler-managed GPU capacityTeams wanting inference API + GPU training from one vendor, Fine-tuning Llama, Mistral, or Qwen at scale
Founded / HQ2025 · United States2022 · San Francisco, CA