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Together AI vs Inference.net
Inference.net runs cheap batch on spare GPU capacity and distills production traces into custom models. Together is a general open-model platform with real-time serving and training.
By The Subconscious Team · Updated
Together AI vs Inference.net: key differences
Inference.net was built on idle GPU time. Its scheduler stitches together unused capacity across data centers and passes the discount on, which shows in a Batch API that accepts up to 1M requests per file with windows from 24 hours to 7 days. Around that it has a gateway that routes open, closed or custom models under one key and captures traffic as eval and training data, then fine-tunes a task-specific model and serves it on a dedicated GPU with a 99.99% uptime target. Together offers batch at up to 50% off plus real-time serverless, provisioned and dedicated serving across thirty-plus models.
The two approach fine-tuning from different ends. Inference.net starts from your production traces and aims to replace a narrow GPT-class workload with a smaller distilled model. Together hands you the training tools directly: LoRA and full SFT from $0.48 per million training tokens, an RL beta and reserved clusters. Inference.net's spare capacity suits batch better than strict real-time SLAs, and it has few independent benchmarks. Together is the broader choice for mixed real-time traffic; Inference.net is the specialist for bulk jobs and trace-driven distillation.
What Together AI and Inference.net do
Together AI
Together AI is the broadest open-model platform in the category. One bill covers per-token serverless inference, batch at up to 50% off, provisioned throughput with a 99% SLA, dedicated deployments, raw GPU clusters, managed fine-tuning and code sandboxes for agents. The text catalog runs past thirty open models, including DeepSeek V4, Kimi K3, GLM 5.2, Qwen 3.8 and MiniMax M3, plus image, video, speech and embedding models. Token prices sit at parity with Fireworks and Baseten.
Example models: Kimi K3, DeepSeek V4 Pro
Full Together AI profileInference.net
Inference.net started as a buyer of last resort for idle GPU time. Its scheduler aggregates small unused chunks of capacity across data centers and runs models on them, and it passes the steep discounts it gets from those data centers on to customers. That origin still shows in its OpenAI-compatible Batch API, which takes up to 1M requests per file with completion windows from 24 hours to 7 days and far higher headroom than synchronous limits.
Example models: open catalog models plus customer fine-tunes served on dedicated GPUs
Full Inference.net profileShould you choose Together AI or Inference.net?
Together AI
Choose Together AI for
- Real-time serving across many open models
- Hands-on SFT and RL with your own datasets
- Reserved clusters for mid-training experiments
Inference.net
Choose Inference.net for
- Million-request batch files with multi-day windows
- Turning captured gateway traffic into a distilled model
- Routing open, closed and custom models under one key
Together AI vs Inference.net at a glance
| Attribute | Together AI | Inference.net |
|---|---|---|
| Model access | Open weights | Open, closed and custom |
| Flagship models | Kimi K3, DeepSeek V4, GLM 5.2, Qwen 3.8 | Customer fine-tunes |
| Speed | 0.99s TTFT on DeepSeek V4 Pro | Batch windows of 24h to 7 days |
| Price | Parity with Fireworks and Baseten | Discounted spare GPU capacity |
| Customization | LoRA and full SFT; RL in beta | Distill traces into custom models |
| Deployment | Serverless, dedicated, GPU clusters | Batch API, gateway, dedicated GPUs |
| Long context | 512K on DeepSeek V4 Pro | Varies by model |
Frequently asked questions
What is the difference between Together AI and Inference.net?
Inference.net runs cheap batch on spare GPU capacity and distills production traces into custom models. Together is a general open-model platform with real-time serving and training.
When should I choose Together AI over Inference.net?
Real-time serving across many open models; Hands-on SFT and RL with your own datasets; Reserved clusters for mid-training experiments.
When should I choose Inference.net over Together AI?
Million-request batch files with multi-day windows; Turning captured gateway traffic into a distilled model; Routing open, closed and custom models under one key.
Is Together AI or Inference.net cheaper?
Together AI: Parity with Fireworks and Baseten. Inference.net: Discounted spare GPU capacity. The cheaper choice depends on the model and workload.
Which has more context, Together AI or Inference.net?
Together AI: 512K on DeepSeek V4 Pro. Inference.net: Varies by model.
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Running long-horizon agents?
If your agents run past 200K tokens, compare both against Subconscious. Our inference stack treats a long-horizon trace as the primary workload, so speed, cost, and accuracy hold up deep into the trace.