Together AI

Paid

Together AI is a Developer Tools cloud platform for running open-source AI models without managing GPU servers yourself. You get serverless inference, fine-tuning, and raw GPU clusters through one API.

The trade-off is clear: you save hours on infrastructure, but you bring your own code, databases, and orchestration. This is not a finished app. It is plumbing for builders.

Pricing runs pay-as-you-go for tokens and hourly GPU rates. No free tier. You buy credits up front, then burn them down.

Open it if you ship AI features and want cheap open-source inference without DevOps. Skip it if you need a no-code product or predictable monthly bills.

You fit here if this matches your team and the work ahead.

• Teams needing broad model catalog on day one: You get 100+ open-source models ready to call. No setup, no downloading weights.
• Fine-tuning heavy users especially at large parameter counts: LoRA fine-tuning works up to 405B parameters. You iterate on proprietary data without building training rigs.
• RL training workflows beyond standard inference/fine-tuning: The Meta PyTorch partnership gives you reinforcement learning tooling, not just basic fine-tuning.
• AI researchers and data scientists: You run experiments on scarce hardware without waiting for cluster time.
• Teams training massive models from scratch: On-demand NVIDIA B200s and H100 clusters sit ready for distributed training jobs.
• AI-native companies needing production-grade open-source inference at scale: Serverless endpoints handle spikes. You pay per token, not per provisioned hour.
• Users switching from OpenAI seeking cost reduction with minimal code changes: The API is OpenAI-compatible. Swap the base URL and key. Reports show 60% savings.
• Developers building voice agents or latency-sensitive applications: FlashAttention-3 optimizations keep inference snappy for real-time use.
• Organizations fine-tuning open-source models on proprietary data: You own the trained weights. Nothing leaves your account.
• Startups with high token volumes where per-call cost matters: Async Batch API handles 30 billion token backfills cheaply.

These are the parts you notice after running real workloads.

• OpenAI-compatible API: Migration takes minutes, not days. Your existing SDK code just works.
• Massive cost savings: Users report 60% less spend than closed APIs for comparable quality open models.
• Fine-tuning and deployment in one place: Train a model, then serve it immediately. No export drama.
• Async Batch API for large-scale workloads: Backfill millions of tokens overnight without babysitting queues.
• Transparent GPU rental pricing: $1.99 per hour for preemptible H100s. You know the burn rate before you start.
• Cache hit rate data: You estimate real costs for agentic workflows before you ship.

Skip it when these limits block your path.

• No free tier; prepay required: You buy $5 minimum credits before your first API call. If you need to try before you buy, this blocks you.
• Inference-only platform: Databases, storage, and orchestration live elsewhere. You maintain a split stack.
• Best-effort serverless with rate limits: 429 errors spike during demand surges. No fixed limit table to engineer against.
• Steep learning curve: Non-technical teams cannot use this. You need real MLOps skills.
• Billing support headaches: Users report ignored tickets, cards locked in accounts, and no self-serve cancellation.