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Parasail vs Wafer
Parasail spreads work across aggregated GPUs for price and flexibility. Wafer tunes stacks with AI agents for speed. Cost-first against speed-first open hosting.
By The Subconscious Team · Updated
Parasail vs Wafer: key differences
Both host open models on GPUs they did not design, but they optimize different things. Parasail aggregates capacity across many providers, runs any Hugging Face model and competes on price, with half-price batch and per-parameter rates. Wafer's agents profile a workload and tune batching, decoding, quantization, kernels and hardware, 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 each 2x faster than vLLM. Those gains are self-reported against stock baselines.
Consistency is a question for each. Parasail's downside is that performance depends on the underlying providers. Wafer is very young and runs a small hosted catalog. On pricing, Wafer Pass is a flat subscription from $10 a week for agentic coding tools, while Parasail uses commit-to-spend drawn down across any model or hardware. For dedicated endpoints with a latency SLO and no in-house kernel engineers, Wafer's tuning is the draw. For evals and offline processing, Parasail's batch is cheaper and broader.
What Parasail and Wafer do
Parasail
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 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. 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: Qwen 3.5 397B Turbo, GLM 5.1 Turbo
Full Wafer profileShould you choose Parasail or Wafer?
Parasail vs Wafer at a glance
| Attribute | Parasail | Wafer |
|---|---|---|
| Model access | Any Hugging Face model | Open weights |
| Flagship models | GTE-Qwen2, Qwen3-VL-8B-Instruct | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | 600ms p99 real-time budget | 2–2.8x vs stock vLLM or SGLang |
| Price | Per-parameter rates; batch 50% off | Wafer Pass from $10 a week |
| Customization | Private Hugging Face repos | Agent-tuned dedicated deployments |
| Deployment | Serverless, elastic, dedicated, batch | Serverless pass, dedicated |
| Long context | Varies by model | Varies by model |
Frequently asked questions
What is the difference between Parasail and Wafer?
Parasail spreads work across aggregated GPUs for price and flexibility. Wafer tunes stacks with AI agents for speed. Cost-first against speed-first open hosting.
When should I choose Parasail over Wafer?
Cheap batch on any Hugging Face model; Evals, embeddings and data processing; Spend commitments spanning many models.
When should I choose Wafer over Parasail?
Faster serving of large open models; Flat-rate access for coding agents; Deployments tuned on NVIDIA or AMD.
Is Parasail or Wafer cheaper?
Parasail: Per-parameter rates; batch 50% off. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
Which has more context, Parasail or Wafer?
Parasail: Varies by model. Wafer: Varies by model.
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.