Cerebras vs Together AI: wafer-scale inference vs serverless GPU (2026)
Cerebras runs Llama on the largest chip in the world (the WSE-3), claiming the fastest inference on the planet. Together AI runs the same models on commodity NVIDIA GPUs at competitive prices and a much wider catalog. Here is how they compare on speed, cost, and model selection.
Cerebras vs Together AI — at a glance
| Dimension | Cerebras | Together AI |
|---|---|---|
| Hardware | WSE-3 (wafer-scale) | NVIDIA H100/H200 GPUs |
| Llama 3.3 70B speed | ~2,200 tok/s | ~120 tok/s |
| Llama 3.3 70B price | ~$0.85 / $1.20 per 1M tok | ~$0.88 / $0.88 per 1M tok |
| Model catalog | Llama family + a few others | ~200 public models |
| Fine-tuning | Limited | LoRA + full fine-tune |
| Function calling | Yes (Llama 3.3) | Yes (most models) |
| Best for | Lowest latency, voice, real-time agents | Wide catalog, fine-tunes, image/video |
Pick Cerebras or Together AI?
When to choose Cerebras
Choose Cerebras when latency is the product: real-time voice agents, interactive code completion, or any UX where token-per-second visibly outpaces user reading speed. Cerebras's WSE-3 produces tokens roughly 18x faster than commodity GPU serving, and the gap shows clearly on long completions. Pricing is competitive with serverless GPU on Llama 3.3 70B.
- ~2,200 tok/s on Llama 3.3 70B — fastest public inference in 2026
- Wafer-scale WSE-3 chip with 900,000 cores and integrated memory
- Llama 3.3 70B at ~$0.85/$1.20 per 1M tok — price-competitive
- Best UX for voice agents and real-time code assistants
- OpenAI-compatible API
When to choose Together AI
Choose Together AI when you need a broad catalog of open models, fine-tuning support, and competitive per-token prices on a mature platform. Together hosts ~200 models including Llama, DeepSeek V3, Mixtral, Qwen 2.5, FLUX image generation, and Whisper variants. Their fine-tuning API supports LoRA and full fine-tunes, and they ship OpenAI-compatible Chat and Completions endpoints.
- ~200 public open-source models across LLMs, image, audio
- Llama 3.3 70B at ~$0.88/$0.88 per 1M tok
- LoRA and full fine-tuning available via API
- Strong ecosystem: LangChain, LlamaIndex, function calling
- Best for developers wanting wide model selection
VerticalAPI verdict
Pick Cerebras when token-per-second is the product — voice agents, real-time code, anything user-facing. Pick Together AI when catalog breadth, fine-tuning, and ecosystem maturity matter more than raw speed. Both run Llama 3.3 70B at similar list prices, so it is really a UX vs. flexibility decision.
Frequently asked questions
Is Cerebras really 18x faster than Together AI?
On Llama 3.3 70B Cerebras advertises around 2,200 tok/s versus Together AI's around 120 tok/s on NVIDIA H100/H200 — roughly an 18x gap. The advantage holds across most prompt lengths because Cerebras keeps the full model in on-chip memory. The difference is most visible on long-completion workloads (code, reasoning, voice).
How does pricing compare on Llama 3.3 70B?
Cerebras prices Llama 3.3 70B at approximately $0.85 per 1M input tokens and $1.20 per 1M output. Together AI prices the same model at approximately $0.88/$0.88 per 1M. List prices are roughly comparable; Cerebras's higher output cost reflects its premium on speed, while Together has a more symmetric input/output ratio.
Which has a broader model catalog?
Together AI is significantly broader — about 200 public models including DeepSeek V3, Mixtral, Qwen 2.5, FLUX (image), Whisper variants, and embeddings. Cerebras concentrates on the Llama family (3.1, 3.3, and a small set of partner models). For multi-model agents or image generation, Together is the better fit.
Can I fine-tune on Cerebras?
Cerebras's primary product in 2026 is inference, not fine-tuning. They offer custom training arrangements for enterprise customers but no self-service fine-tuning API. Together AI offers self-service LoRA and full fine-tuning via API across most of its open-source catalog, which makes it the practical choice for teams that want to customize models.
Limitations of this comparison
- Cerebras throughput numbers are vendor-published; independent third-party benchmarks consistently land in the 1,800-2,200 tok/s range but with variance.
- Together AI throughput varies by model and load; Llama 3.3 70B can drop to 60-80 tok/s during peak hours.
- Cerebras hosts a small set of models — workloads needing DeepSeek V3, Mixtral, FLUX, or Whisper require Together or another provider.
- Cerebras availability is constrained by physical WSE-3 capacity; rate limits are tighter than on commodity-GPU providers.
- Per-token pricing for both providers has been falling roughly 30-50% per year — figures here reflect mid-2026.
What may change in 12-24 months
- Cerebras WSE-4 is expected to widen the speed gap further and add multimodal model support.
- Together AI is expected to roll out dedicated capacity (akin to Anyscale Endpoints) for customers needing predictable cost.
- Per-token prices on Llama 3.3 70B-class models will likely fall another 30-40% in the next 12 months.
- Verify provider access, current pricing and compatibility separately before integration.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- Is Cerebras faster than Groq on Llama 3.3 70B in 2026?
- Is Together AI cheaper than Fireworks for fine-tuned Llama?
- How do I run real-time voice agents on Cerebras through OpenAI-compatible API?
- Can I serve a Together AI fine-tune on Cerebras hardware?
- Which provider gives the lowest cost per completed agent task?
More head-to-head provider comparisons
Who's the fastest LLM provider in 2026?
The two serverless GPU heavyweights
LPU speed vs open-weight breadth
Wafer-scale vs flexible inference
Direct provider control vs managed multi-model access