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Together AI vs Luminal
Together AI is a broad open-model platform with fine-tuning and clusters. Luminal is an early compiler startup chasing more throughput per GPU.
By The Subconscious Team · Updated
Together AI vs Luminal: key differences
Together serves Kimi K3, DeepSeek V4, GLM 5.2, Qwen 3.8 and many more on serverless and dedicated endpoints, rents GPU clusters, and runs LoRA, full SFT and RL fine-tuning on one bill. Luminal covers a narrower slice. Its open-source compiler turns a model into native kernels ahead of time, and the company serves those compiled models serverless in early access or licenses the engine for on-prem.
Together is the pick for breadth, training and a known production track record. Luminal is a bet on engine speed: it reports GPT-OSS 120B at 36K tokens per second on 8 H100s against 26K for vLLM, measured by Luminal. Teams with their own custom model and GPUs may find Luminal's compiler worth testing; most teams shopping for a model catalog will start with Together.
What Together AI and Luminal 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 profileLuminal
Luminal builds an inference compiler. Where vLLM and SGLang interpret a model at runtime, Luminal compiles it ahead of time into native kernels for GPUs and ASICs. Models get lowered to a small graph of 15 primitive ops, and the compiler searches over fusion, tiling, memory and scheduling choices instead of relying on hand-written rules, which it says can find optimizations like Flash Attention on its own. The compiler is open source in Rust under Apache 2.0 or MIT, runs on CUDA and Metal with ROCm on the roadmap, and works as a torch.compile backend.
Example models: GPT-OSS 120B, Llama 3 8B
Full Luminal profileShould you choose Together AI or Luminal?
Together AI
Choose Together AI for
- A broad open-model catalog with serverless pricing
- Fine-tuning and RL on one platform
- Rentable GPU clusters
Luminal
Choose Luminal for
- Compiling a custom model into fast native GPU code
- On-prem deployments with custom kernel work and SLAs
- An open-source engine teams can run on their own hardware
Together AI vs Luminal at a glance
| Attribute | Together AI | Luminal |
|---|---|---|
| Model access | Open weights | Bring your own weights |
| Flagship models | Kimi K3, DeepSeek V4, GLM 5.2, Qwen 3.8 | No public catalog |
| Speed | 0.99s TTFT on DeepSeek V4 Pro | 36K tok/s on GPT-OSS 120B, 8xH100 (vendor) |
| Price | Parity with Fireworks and Baseten | Pay per use; rates not published |
| Customization | LoRA and full SFT; RL in beta | Compiles any PyTorch or HF model |
| Deployment | Serverless, dedicated, GPU clusters | Serverless (early access), on-prem license |
| Long context | 512K on DeepSeek V4 Pro | Unknown |
Frequently asked questions
What is the difference between Together AI and Luminal?
Together AI is a broad open-model platform with fine-tuning and clusters. Luminal is an early compiler startup chasing more throughput per GPU.
When should I choose Together AI over Luminal?
A broad open-model catalog with serverless pricing; Fine-tuning and RL on one platform; Rentable GPU clusters.
When should I choose Luminal over Together AI?
Compiling a custom model into fast native GPU code; On-prem deployments with custom kernel work and SLAs; An open-source engine teams can run on their own hardware.
Is Together AI or Luminal cheaper?
Together AI: Parity with Fireworks and Baseten. Luminal: Pay per use; rates not published. The cheaper choice depends on the model and workload.
Related comparisons
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.