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TypeSafe AI vs Wafer
Wafer makes open LLMs run faster; TypeSafe replaces LLM calls on decision-shaped tasks entirely. Both cut latency, from opposite directions.
By The Subconscious Team · Updated
TypeSafe AI vs Wafer: key differences
Wafer and TypeSafe both sell speed, by very different means. Wafer's agents tune inference stacks so open models run faster on the same weights, and it reports its Qwen 3.5 397B at 2.8x the speed of stock SGLang, a self-reported figure. TypeSafe skips generation for decision-shaped work. Its Jev model evaluates every option in one pass and returns a typed answer with calibrated confidence in about 100ms, which TypeSafe says is roughly 40 to 200x faster than an LLM. Wafer speeds up the text generator. TypeSafe removes the generator where only a decision is needed.
A team could use both. Wafer Pass, from $10 a week, puts big open models into Claude Code, Cline and OpenHands at interactive speed, and dedicated Wafer deployments target a customer's latency SLO on NVIDIA or AMD. Jev can sit in the same harness as the router or guardrail, deciding what reaches the LLM. Both are young. Wafer has a small hosted catalog and self-reported benchmarks, and Jev is early access with text-only input and a programming model teams have to learn.
What TypeSafe AI and Wafer do
TypeSafe AI
TypeSafe AI builds decision models instead of text generators. Founder Diogo Almeida co-invented RLHF and InstructGPT at OpenAI and later worked at Google Brain. After two years in stealth the company released its first System One Model, Jev, in early access. The name nods to Kahneman's fast System 1 thinking, and the model is built for machines to call, not people to chat with.
Example models: Jev, jev-1.13
Full TypeSafe AI 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 TypeSafe AI or Wafer?
TypeSafe AI
Choose TypeSafe AI for
- Replacing LLM calls for classification and routing
- Guardrails that need an answer in about 100ms
- Outputs that must match a schema exactly
Wafer
Choose Wafer for
- Faster open-model generation for coding agents
- Flat-rate access through Wafer Pass
- Continually re-tuned deployments on NVIDIA or AMD
TypeSafe AI vs Wafer at a glance
| Attribute |  TypeSafe AI | Wafer |
|---|---|---|
| Model access | Decision models | Open weights |
| Flagship models | Jev, jev-1.13 | Qwen 3.5 397B Turbo, GLM 5.1 Turbo |
| Speed | ~100ms per call | 2–2.8x vs stock vLLM or SGLang |
| Price | A fraction of an LLM call | Wafer Pass from $10 a week |
| Customization | Unknown | Agent-tuned dedicated deployments |
| Deployment | Early-access API | Serverless pass, dedicated |
| Long context | Unknown | Varies by model |
Frequently asked questions
What is the difference between TypeSafe AI and Wafer?
Wafer makes open LLMs run faster; TypeSafe replaces LLM calls on decision-shaped tasks entirely. Both cut latency, from opposite directions.
When should I choose TypeSafe AI over Wafer?
Replacing LLM calls for classification and routing; Guardrails that need an answer in about 100ms; Outputs that must match a schema exactly.
When should I choose Wafer over TypeSafe AI?
Faster open-model generation for coding agents; Flat-rate access through Wafer Pass; Continually re-tuned deployments on NVIDIA or AMD.
Is TypeSafe AI or Wafer cheaper?
TypeSafe AI: A fraction of an LLM call. Wafer: Wafer Pass from $10 a week. The cheaper choice depends on the model and workload.
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.