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Why Teams Leave Together.ai
Together.ai offers convenient API access to open source models, but the per-token pricing adds up fast at scale. Teams processing millions of tokens daily often find that dedicated GPU hosting costs 60-80% less while delivering better latency and full data control.
This guide breaks down when it makes sense to self-host your open source LLMs instead of using an API provider. For more provider comparisons, browse our alternatives category.
Cost Comparison: API vs Self-Hosted
Together.ai charges per million tokens. A dedicated GPU server charges a flat monthly rate regardless of usage.
| Monthly volume | Together.ai (LLaMA 3 8B) | Dedicated RTX 3090 | Savings |
|---|---|---|---|
| 10M tokens/mo | ~$2 | Higher (server cost) | API wins at low volume |
| 100M tokens/mo | ~$20 | Lower (fixed cost) | Break-even zone |
| 1B tokens/mo | ~$200 | Much lower | Self-host saves 50-70% |
| 10B tokens/mo | ~$2,000 | Much lower | Self-host saves 70-85% |
The break-even point is typically around 100-500M tokens per month. Use our GPU vs API cost comparison calculator to find your exact break-even. For per-GPU cost data, see cost per million tokens.
Calculate Your Savings
See exactly how much you’d save by self-hosting your LLM workload on dedicated hardware.
LLM Cost CalculatorPerformance Comparison
Self-hosted inference on a dedicated RTX 3090 with vLLM delivers:
| Metric | Together.ai API | Dedicated RTX 3090 |
|---|---|---|
| Time to first token | 200-500ms (network + queue) | 50-100ms (local) |
| Throughput (LLaMA 8B) | Shared capacity | 42 tok/s dedicated |
| Availability | Rate limited | No limits — your hardware |
| Cold starts | Possible | None — model stays in VRAM |
For detailed throughput data across all GPUs, see our tokens per second benchmark.
Control & Privacy
With private AI hosting on dedicated hardware:
- Data never leaves your server — critical for GDPR, healthcare, legal workloads
- No vendor lock-in — switch models instantly without changing API providers
- Custom models — deploy fine-tuned models that API providers don’t support
- Full logging — complete visibility into every request and response
How to Migrate
Moving from Together.ai to self-hosted is straightforward:
- Deploy a dedicated GPU server (RTX 3090 for most workloads)
- Install vLLM or Ollama — both provide OpenAI-compatible API endpoints
- Download the same model from Hugging Face
- Point your application to your server’s API endpoint instead of Together.ai’s
The API format is identical. Most applications need only a URL change. See our self-hosting LLM guide for the full walkthrough.
Verdict
Stay on Together.ai if: You process fewer than 100M tokens/month and don’t need data privacy controls.
Self-host on dedicated GPUs if: You process 100M+ tokens/month, need GDPR compliance, want consistent latency, or run custom/fine-tuned models.
See our Together.ai alternative page for a quick overview, or browse GPU servers to get started.
