Together AI

Together AI provides infrastructure for building on open and frontier-adjacent models: serverless inference APIs, dedicated GPU deployments, on-demand clusters, fine-tuning, and sandboxed code execution.

It overlaps with Fireworks AI, Groq, Fal.ai, OpenRouter endpoint. It is closer to an AI compute platform for teams that train, tune, evaluate, and serve models.

When to pick Together AI

• You need open-model economics. Frontier APIs are convenient, but open models can be cheaper and more controllable at volume.
• You want fine-tuning and serving in one place. Fine-tune, deploy, and monitor without moving artifacts across vendors.
• You need dedicated throughput. Dedicated inference gives stronger predictability than shared serverless routes.
• You want GPU flexibility. On-demand and reserved GPU clusters cover training, eval, and batch workloads.
• You are building code-execution workflows. The code interpreter and sandbox pricing can simplify agent tooling.

When to pick something else

• Model-router convenience: OpenRouter is easier when you mostly want one API key for many providers.
• Ultra-low-latency: Groq is sharper for supported open models when latency is the deciding metric.
• Image/video model breadth: Fal.ai and Replicate are stronger media-model catalogs.
• No-code AI workflows: Gumloop, n8n, or Dust will be more accessible.

Pricing

Together AI pricing is usage-based across five dimensions. Verified 2026-06-12 against together.ai/pricing:

• (per 1M tokens): Kimi K2.6 $1.20 in / $4.50 out · Qwen3.6-Plus $0.50 / $3.00 · Qwen3 235B throughput $0.20 / $0.60 · Llama 3.3 70B $1.04 / $1.04 · gpt-oss-20B $0.05 / $0.20 · Gemma 3n E4B $0.06 / $0.12. Image: FLUX.2 [pro] $0.03/image, Nano Banana Pro $0.134/image, Imagen 4.0 Ultra $0.06/image. Audio: Whisper Large v3 $0.0015/audio minute. Embeddings: Multilingual e5 large at $0.02/MTok.
• Dedicated inference (single-tenant, hourly): H100 80GB $6.49 · H200 140GB contact sales · HGX B200 180GB $11.95.
• GPU clusters: On-demand HGX H100 $5.49/hr, HGX H200 $6.79/hr, HGX B200 $9.95/hr. Reserved 7 to 30 days starts at H100 $4.99, H200 $5.95, and B200 $9.65; 91 to 180 day reserved pricing drops to H100 $3.99, H200 $4.55, and B200 $9.09.
• Fine-tuning (per 1M tokens): Standard models scale by size: up to 16B at $0.48 SFT / $0.54 DPO; 17B to 69B at $1.50 / $1.65; 70 to 100B at $2.90 / $3.20. Specialized models like DeepSeek-R1 and GLM-5 run $10 to $100+ with minimum charges of $6 to $60.
• Code sandbox: VM compute $0.0446/vCPU/hour + $0.0149/GiB RAM/hour. Code Interpreter sessions $0.03 per 60 minutes.
• Storage: Shared filesystem $0.16/GiB/month.

Budgeting needs workload detail. A small product can stay inexpensive on serverless inference. A team reserving large GPU capacity or tuning bigger models should model the full bill across all five dimensions before migration.

Buyer fit

Together AI makes the most sense when the team is past experimentation and is trying to control model-serving economics. If the workload is a small internal chatbot, a frontier API or model router may be simpler. If the workload is a production product with meaningful traffic, model choice, latency, and fine-tuning needs, Together becomes more relevant.

The strongest fit is a developer team that wants to:

• benchmark multiple open models on the same product task
• move successful prototypes into dedicated inference
• fine-tune smaller models for domain-specific behavior
• run batch or eval jobs on reserved GPU capacity
• add code-execution sandboxes to agent workflows
• avoid tying the whole stack to one proprietary model provider

The weaker fit is a non-technical team that just wants an assistant UI. Together is infrastructure. It needs an app layer, evals, monitoring, secrets handling, and a clear owner for model operations.

Procurement questions

Ask these before migrating production traffic:

• Which models are actually needed, and which can be served cheaper elsewhere?
• What latency and throughput target must dedicated endpoints meet?
• How will fine-tuned models be evaluated before release?
• What happens if GPU inventory, region support, or model availability changes?
• Who owns prompt/version rollback when model behavior changes?
• How are logs, customer data, and sandboxed code-execution outputs retained?

The best Together AI deployment usually starts with a benchmark matrix. Compare the current provider, a cheaper open model, a fine-tuned model, and a dedicated endpoint on the same prompts, traffic shape, and failure cases.