Cerebras vs Fireworks: wafer-scale inference vs developer-first serverless (2026)
Cerebras runs Llama on the largest chip ever made (WSE-3) and ships the fastest inference numbers in public benchmarks. Fireworks AI runs on NVIDIA H100/H200 with one of the most polished developer experiences for open-source models. Here is how they compare on speed, cost, and ecosystem.
Cerebras vs Fireworks — at a glance
| Dimension | Cerebras | Fireworks AI |
|---|---|---|
| Hardware | WSE-3 (wafer-scale) | NVIDIA H100/H200 |
| Llama 3.3 70B speed | ~2,200 tok/s | ~250 tok/s |
| Llama 3.3 70B price | ~$0.85 / $1.20 per 1M tok | ~$0.90 / $0.90 per 1M tok |
| Public model catalog | Llama family + a few | ~100 models |
| Fine-tuning | Limited | LoRA + full fine-tune |
| Function calling | Yes (Llama 3.3) | Yes (most models) |
| Best for | Lowest latency, real-time UX | Developer apps, agentic workloads, fine-tunes |
Pick Cerebras or Fireworks AI?
When to choose Cerebras
Choose Cerebras when latency is the UX. The WSE-3 chip's on-die memory and 900,000 cores produce tokens faster than humans can read, which transforms voice agents, code-completion UIs, and interactive reasoning. Pricing is competitive with serverless GPU on Llama 3.3 70B, so you get speed without paying a large premium.
- ~2,200 tok/s on Llama 3.3 70B — fastest public inference in 2026
- Wafer-scale WSE-3 chip with integrated memory
- Llama 3.3 70B at competitive ~$0.85/$1.20 per 1M tok
- Game-changer for voice agents and real-time code UX
- OpenAI-compatible API
When to choose Fireworks AI
Choose Fireworks AI when you want a developer-first serverless platform with strong function calling, structured output, and fine-tuning. Fireworks consistently ranks among the fastest commodity-GPU serverless providers (~250 tok/s on Llama 3.3 70B) and supports ~100 public models including DeepSeek V3, Mixtral, Qwen, and FLUX. The OpenAI-compatible API is one of the most polished.
- ~250 tok/s on Llama 3.3 70B — top three on commodity GPUs
- ~100 public models (DeepSeek, Mixtral, Qwen, FLUX, Whisper)
- Strong function calling, JSON mode, structured output
- LoRA and full fine-tuning available via API
- Best developer ergonomics among open-model providers
Route Cerebras and Fireworks AI through one endpoint
VerticalAPI exposes both providers through a single OpenAI-compatible endpoint. Same SDK, BYOK, zero markup on tokens — you pay each provider directly with your own keys.
from openai import OpenAI client = OpenAI(base_url="https://api.verticalapi.com/v1", api_key="vapi_...") # Cerebras via VerticalAPI BYOK resp_a = client.chat.completions.create( model="cerebras/llama-3.3-70b", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "csk-..."}, ) # Fireworks AI same SDK, different model + key resp_b = client.chat.completions.create( model="fireworks/llama-v3p3-70b-instruct", messages=[{"role": "user", "content": "Hello"}], extra_headers={"X-Provider-Key": "fw_..."}, )
VerticalAPI verdict
Pick Cerebras when token-per-second is the product — voice, real-time code, interactive reasoning. Pick Fireworks when you need a wide open-model catalog, fine-tuning, and polished developer APIs. Both run Llama 3.3 70B at similar list prices. Via VerticalAPI BYOK, route latency-critical traffic to Cerebras and the long tail to Fireworks with one model parameter switch.
Frequently asked questions
Is Cerebras really 9x faster than Fireworks?
On Llama 3.3 70B, Cerebras publishes around 2,200 tok/s and Fireworks AI clocks around 250 tok/s — roughly a 9x gap. The advantage is most visible on long completions (code, reasoning chains, voice transcripts) and on small batch sizes where commodity-GPU batching gives Fireworks less leverage. For shorter chat completions the perceived gap shrinks because both finish quickly.
How does the price 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. Fireworks prices the same model at approximately $0.90/$0.90 per 1M. List prices are roughly comparable; Cerebras has a higher output-to-input ratio reflecting its speed premium, while Fireworks has a symmetric input/output price.
Which has the broader model catalog?
Fireworks AI is significantly broader — about 100 public models covering DeepSeek V3, Mixtral, Qwen 2.5, FLUX, Whisper variants, embeddings, and reranking. Cerebras focuses on the Llama family plus a small set of partner models. For multi-model agents or image/audio generation, Fireworks is the better fit.
Which is easier for fine-tuning?
Fireworks offers self-service LoRA and full fine-tuning via API on most of its open-source catalog. Cerebras provides custom training arrangements for enterprise customers but no self-service fine-tuning API in 2026. For teams that want to customize models without sales conversations, Fireworks is the practical answer.
Can VerticalAPI route between Cerebras and Fireworks?
Yes. VerticalAPI exposes both providers through a single OpenAI-compatible BYOK endpoint at https://api.verticalapi.com/v1. You bring your Cerebras and Fireworks API keys, switch model parameters per request, and pay each provider directly. Common pattern: Cerebras for hot, latency-critical paths; Fireworks for the long tail and fine-tunes.
Limitations of this comparison
- Cerebras throughput is vendor-published; independent benchmarks land in the 1,800-2,200 tok/s range with variance.
- Fireworks throughput depends on load and batch size — typical Llama 3.3 70B can drop to 150-200 tok/s during peak hours.
- Cerebras's small catalog forces multi-provider setups for non-Llama workloads (image, audio, reranking).
- Cerebras availability is constrained by physical WSE-3 capacity; rate limits are tighter than on commodity GPU providers.
- Per-token pricing for both has been falling roughly 30-50% per year — figures reflect mid-2026.
What may change in 12-24 months
- Cerebras WSE-4 is expected to widen the speed gap further and add multimodal support.
- Fireworks will continue dropping Llama-class prices another 30-40% in the next 12 months as DeepInfra and Together compete.
- Fireworks is expected to extend LoRA fine-tuning to more recent base models including DeepSeek V4 and Qwen 3.
- Hybrid routing (Cerebras for hot, Fireworks for cold/long-tail) via VerticalAPI BYOK will become a standard playbook.
Related questions
ChatGPT, Perplexity and Gemini usually suggest these next.
- Is Cerebras cheaper than Groq for Llama 3.3 70B?
- How does Fireworks fine-tuning compare to Together AI's?
- What's the cheapest serverless provider for DeepSeek V3?
- Can I serve a Fireworks LoRA fine-tune on Cerebras hardware?
- Which provider gives the lowest cost per completed agent task on Llama 3.3?
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