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Together AI vs Wafer
Wafer uses agents to tune inference stacks and reports 2x to 2.8x speedups over stock engines. Together offers a far broader platform built by FlashAttention researchers.
By The Subconscious Team · Updated
Together AI vs Wafer: key differences
Wafer's pitch is that most hosts run stock vLLM or SGLang and leave speed on the table. Its agents profile a workload, try configurations across batching, decoding, quantization, kernels and hardware, and deploy the winner, then keep re-tuning. Wafer reports its Qwen 3.5 397B running 2.8x faster than stock SGLang, and GLM 5.1 and DeepSeek V4 Pro 2x faster than vLLM baselines. Those are self-reported numbers against stock setups, and Together's stack is not stock, since the researchers behind FlashAttention and Medusa shape it. Any buyer should benchmark the two side by side.
Scope separates them more clearly than speed. Wafer is a very young company with a small hosted catalog, per-token serverless access for coding tools, and dedicated deployments tuned to an SLO on NVIDIA or AMD. Together covers serverless, batch, provisioned, dedicated, clusters and fine-tuning. Wafer suits solo developers who want fast open models in Claude Code or Cline, and teams with a strict latency target and no kernel engineers. Together suits teams that need breadth and training.
What Together AI and Wafer 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 GLM 5.3, DeepSeek V4.1 Flash, DeepSeek V4 Pro 0813, Kimi K3, 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, GLM 5.3, DeepSeek V4.1 Flash
Full Together AI profileWafer
Wafer builds AI agents that act as GPU performance engineers, then sells inference on the stacks those agents tune. The company came out of Y Combinator's Summer 2025 batch as a "Cursor for CUDA" that turned slow PyTorch into custom kernels. Founders Emilio Andere and Steven Arellano are based in San Francisco. Wafer raised a $4M seed led by Fifty Years in April 2026 and a $40M Series A co-led by Marathon and Chemistry in September 2026 at a $220M post-money valuation, with AMD Ventures and Wing joining. Its agents profile a workload, generate candidate configs across batching, decoding, quantization, engines, kernels and hardware, measure each one and deploy the winner.
Example models: GLM-5.3, DeepSeek V4.1 Flash
Full Wafer profileShould you choose Together AI or Wafer?
Together AI
Choose Together AI for
- A broad catalog including media and embeddings
- Fine-tuning and RL before serving
- Established production rollout tooling
Wafer
Choose Wafer for
- Per-token access to hosted models in coding harnesses
- Dedicated endpoints tuned to a strict latency SLO
- Hedging GPU supply across NVIDIA and AMD
Together AI vs Wafer at a glance
| Attribute | Together AI | Wafer |
|---|---|---|
| Model access | Open weights | Open weights |
| Flagship models | Kimi K3, GLM 5.3, DeepSeek V4.1 Flash, Qwen 3.8 | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | 0.99s TTFT on DeepSeek V4 Pro | ~176 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | Parity with Fireworks and Baseten | Per token, prepaid credits |
| Customization | LoRA and full SFT; RL in beta | Agent-tuned dedicated deployments |
| Deployment | Serverless, dedicated, GPU clusters | Serverless, dedicated |
| Long context | 512K on DeepSeek V4 Pro 0813 | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
Frequently asked questions
What is the difference between Together AI and Wafer?
Wafer uses agents to tune inference stacks and reports 2x to 2.8x speedups over stock engines. Together offers a far broader platform built by FlashAttention researchers.
When should I choose Together AI over Wafer?
A broad catalog including media and embeddings; Fine-tuning and RL before serving; Established production rollout tooling.
When should I choose Wafer over Together AI?
Per-token access to hosted models in coding harnesses; Dedicated endpoints tuned to a strict latency SLO; Hedging GPU supply across NVIDIA and AMD.
Is Together AI or Wafer cheaper?
Together AI: Parity with Fireworks and Baseten. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, Together AI or Wafer?
Together AI: 512K on DeepSeek V4 Pro 0813. Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash).
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.