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Inference.net vs Wafer
Wafer tunes stacks for fast real-time open models. Inference.net runs cheap batch and builds custom models. Speed versus throughput economics.
By The Subconscious Team · Updated
Inference.net vs Wafer: key differences
Wafer and Inference.net both make open models cheaper to run, but they aim at different constraints. Wafer uses AI agents to tune serving stacks, reports 2x to 2.8x speedups over stock vLLM or SGLang, and sells dedicated deployments tuned to a customer's SLO plus per-token serverless access for coding agents. Inference.net aggregates spare GPU capacity for batch work with 24-hour to 7-day windows, and its profile says that capacity suits batch better than strict real-time SLAs.
Customization splits them too. Wafer customizes the serving stack around your model. Inference.net customizes the model itself, capturing gateway traffic and fine-tuning a task-specific version for a dedicated GPU. Each is light on independent proof: Wafer's speedups are self-reported against stock baselines, and Inference.net has few independent benchmarks. Interactive coding agents on big open models fit Wafer. Offline jobs and distillation fit Inference.net.
What Inference.net and Wafer do
Inference.net
Inference.net started as a buyer of last resort for idle GPU time. Its scheduler aggregates small unused chunks of capacity across data centers and runs models on them, and it passes the steep discounts it gets from those data centers on to customers. That origin still shows in its OpenAI compatible Batch API, which takes up to 1M requests per file with completion windows from 24 hours to 7 days and far higher headroom than synchronous limits.
Example models: GLM-5.2, Kimi K2.6, Schematron, customer fine-tunes
Full Inference.net 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. Wafer raised a $4M seed led by Fifty Years in April 2026 and a $40M Series A co-led by Marathon and Chemistry in September 2026 at a $220M post-money valuation, with AMD Ventures and Wing joining. 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: GLM-5.3, DeepSeek V4.1 Flash
Full Wafer profileShould you choose Inference.net or Wafer?
Inference.net
Choose Inference.net for
- Offline batch with day-scale windows
- Distilling production traffic into a custom model
- Gateway routing across open and closed models
Wafer
Choose Wafer for
- Interactive coding agents on large open models
- Per-token access for coding agents
- Dedicated endpoints tuned to a strict latency SLO
Inference.net vs Wafer at a glance
| Attribute | Inference.net | Wafer |
|---|---|---|
| Model access | Open, closed and custom | Open weights |
| Flagship models | Schematron, ClipTagger, customer fine-tunes | GLM-5.3, DeepSeek V4.1 Flash |
| Speed | Batch windows of 24h to 7 days | ~168 tok/s on GLM-5.3; 2–2.8x vs stock vLLM or SGLang (vendor) |
| Price | Free pay-as-you-go; Growth $250/mo; B200 $9.98/hr | Per token, prepaid credits |
| Customization | Distill traces into custom models | Agent-tuned dedicated deployments |
| Deployment | Batch API, gateway, dedicated GPUs | Serverless, dedicated |
| Long context | Varies by model | Up to 1M (GLM-5.3, DeepSeek V4.1 Flash) |
Frequently asked questions
What is the difference between Inference.net and Wafer?
Wafer tunes stacks for fast real-time open models. Inference.net runs cheap batch and builds custom models. Speed versus throughput economics.
When should I choose Inference.net over Wafer?
Offline batch with day-scale windows; Distilling production traffic into a custom model; Gateway routing across open and closed models.
When should I choose Wafer over Inference.net?
Interactive coding agents on large open models; Per-token access for coding agents; Dedicated endpoints tuned to a strict latency SLO.
Is Inference.net or Wafer cheaper?
Inference.net: Free pay-as-you-go; Growth $250/mo; B200 $9.98/hr. Wafer: Per token, prepaid credits. The cheaper choice depends on the model and workload.
Which has more context, Inference.net or Wafer?
Inference.net: Varies by model. Wafer: Up to 1M (GLM-5.3, DeepSeek V4.1 Flash).
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.