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Groq vs Parasail
Parasail runs any Hugging Face model on aggregated GPUs, with half-price batch. Groq runs a fixed short list on its own chip, much faster.
By The Subconscious Team · Updated
Groq vs Parasail: key differences
Parasail does not own hardware. It aggregates GPUs from many providers behind an OpenAI-compatible API and lets customers run any Hugging Face model, private repos included, on serverless, elastic, dedicated or batch tiers. Batch costs half of serverless, with a 4B to 8B model at $0.03 in and $0.06 out per million at FP4. Groq owns its LPU stack end to end and serves only what it chooses to, currently GPT-OSS and Qwen 3.6 among a few others. Groq's speed and tail-latency consistency are the payoff for that control, while Parasail designs for a 600ms p99 budget and depends on the underlying providers for consistency.
Model freedom is Parasail's clearest win. Private or fine-tuned models run there; Groq hosts no fine-tunes. Parasail also suits evals and embeddings at scale, and it signs ZDR and SLA agreements for startups moving off closed APIs. Groq's small-model prices are near the floor too, with cache and Batch discounts, so cost alone may not decide it. If the model you need is on Groq's list and latency matters, Groq wins.
What Groq and Parasail do
Groq
Groq serves open models on its own chip, the LPU, which keeps model weights in on-chip SRAM and runs a deterministic schedule instead of waiting on GPU memory. Groq publishes 1,000 tokens per second on GPT-OSS 20B and 500 on GPT-OSS 120B. Latency stays tight between median and tail, which matters for strict SLAs. The API is OpenAI-compatible and also hosts Whisper for speech to text plus an agentic system called Groq Compound with built-in search and code execution.
Example models: GPT-OSS 120B, Qwen 3.6 27B
Full Groq 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 Groq or Parasail?
Groq
Choose Groq for
- Lowest latency on GPT-OSS and Qwen 3.6
- Voice apps with strict response budgets
- Consistent tail latency from owned hardware
Parasail
Choose Parasail for
- Running private or fine-tuned Hugging Face models
- Half-price batch for evals and embeddings
- One spend commitment across models and hardware
Groq vs Parasail at a glance
| Attribute | Groq | Parasail |
|---|---|---|
| Model access | Open weights | Any Hugging Face model |
| Flagship models | GPT-OSS 120B, Qwen 3.6 27B | GTE-Qwen2, Qwen3-VL-8B-Instruct |
| Speed | 500–1,000 tok/s | 600ms p99 real-time budget |
| Price | Near the floor on small models | Per-parameter rates; batch 50% off |
| Customization | No fine-tuned model hosting | Private Hugging Face repos |
| Deployment | GroqCloud API | Serverless, elastic, dedicated, batch |
| Long context | Around 131K max | Varies by model |
Frequently asked questions
What is the difference between Groq and Parasail?
Parasail runs any Hugging Face model on aggregated GPUs, with half-price batch. Groq runs a fixed short list on its own chip, much faster.
When should I choose Groq over Parasail?
Lowest latency on GPT-OSS and Qwen 3.6; Voice apps with strict response budgets; Consistent tail latency from owned hardware.
When should I choose Parasail over Groq?
Running private or fine-tuned Hugging Face models; Half-price batch for evals and embeddings; One spend commitment across models and hardware.
Is Groq or Parasail cheaper?
Groq: Near the floor on small models. Parasail: Per-parameter rates; batch 50% off. The cheaper choice depends on the model and workload.
Which has more context, Groq or Parasail?
Groq: Around 131K max. Parasail: Varies by model.
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