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Cerebras vs Parasail
Parasail aggregates GPUs and wins on cheap batch for any Hugging Face model. Cerebras wins on real-time speed for a handful of models.
By The Subconscious Team · Updated
Cerebras vs Parasail: key differences
Parasail and Cerebras solve opposite problems. Parasail aggregates GPUs from many hardware providers and sells them through one OpenAI-compatible API. Its standout is batch: any Hugging Face model, private repos included, at half of serverless pricing, with a 4B to 8B model at $0.03 in and $0.06 out at FP4. Cerebras owns its hardware, a wafer-scale chip, and uses it to serve GPT-OSS 120B near 3,000 tokens per second. Parasail will run nearly any checkpoint cheaply. Cerebras will run a very small set faster than any other public host.
For real-time traffic, Parasail designs around a 600ms p99 budget, but its consistency depends on the underlying providers. Cerebras runs on its own silicon, though the speed does little when an agent mostly waits on tools. Parasail's commit-to-spend model and ZDR agreements suit startups moving off closed APIs. Evals, embeddings and offline processing fit Parasail. Voice and live autocomplete fit Cerebras.
What Cerebras and Parasail do
Cerebras
Cerebras builds a single chip the size of a silicon wafer, and its inference cloud is the fastest public host on the models it serves. Its developer table lists GPT-OSS 120B near 3,000 tokens per second, about six times Groq on the same weights, at $0.35 in and $0.75 out per million. The public shared catalog is thin, just GPT-OSS 120B and Gemma 4 31B as of August 2026. More model families live on dedicated endpoints and through partners like OpenRouter, Hugging Face, Vercel and AWS Marketplace.
Example models: GPT-OSS 120B, Gemma 4 31B
Full Cerebras profileParasail
Parasail calls itself the inference cloud for AI-native startups. Instead of owning data centers, it aggregates GPUs from many hardware providers and sells them through one OpenAI-compatible API. Customers choose serverless per-token endpoints, Elastic Endpoints that scale with traffic and bill only for tokens used, dedicated deployments with negotiated latency SLAs, or batch. Its commit-to-spend model lets one commitment draw down across any model or hardware.
Example models: GTE-Qwen2, Qwen3-VL-8B-Instruct
Full Parasail profileShould you choose Cerebras or Parasail?
Cerebras
Choose Cerebras for
- Latency-critical voice and autocomplete
- Fast long outputs on GPT-OSS 120B
- Workloads where generation time is the user's wait
Parasail
Choose Parasail for
- Batch on any Hugging Face model, including private ones
- Evals and embeddings at per-parameter rates
- Startups shifting traffic from closed APIs under ZDR terms
Cerebras vs Parasail at a glance
| Attribute | Cerebras | Parasail |
|---|---|---|
| Model access | Open weights | Any Hugging Face model |
| Flagship models | GPT-OSS 120B, Gemma 4 31B | GTE-Qwen2, Qwen3-VL-8B-Instruct |
| Speed | ~3,000 tok/s on GPT-OSS 120B | 600ms p99 real-time budget |
| Price | $0.35 in, $0.75 out (GPT-OSS 120B) | Per-parameter rates; batch 50% off |
| Customization | Unknown | Private Hugging Face repos |
| Deployment | Shared API, dedicated, partners | Serverless, elastic, dedicated, batch |
| Long context | Unknown | Varies by model |
Frequently asked questions
What is the difference between Cerebras and Parasail?
Parasail aggregates GPUs and wins on cheap batch for any Hugging Face model. Cerebras wins on real-time speed for a handful of models.
When should I choose Cerebras over Parasail?
Latency-critical voice and autocomplete; Fast long outputs on GPT-OSS 120B; Workloads where generation time is the user's wait.
When should I choose Parasail over Cerebras?
Batch on any Hugging Face model, including private ones; Evals and embeddings at per-parameter rates; Startups shifting traffic from closed APIs under ZDR terms.
Is Cerebras or Parasail cheaper?
Cerebras: $0.35 in, $0.75 out (GPT-OSS 120B). Parasail: Per-parameter rates; batch 50% off. The cheaper choice depends on the model and workload.
Related comparisons
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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.