Inference.net vs TypeSafe AI

Both cut the cost of narrow LLM tasks. TypeSafe AI does it with a typed decision model; Inference.net does it by distilling your own traffic.

By The Subconscious Team · Updated September 30, 2026

Inference.net vs TypeSafe AI: key differences

Take a workload like ticket routing or intent classification that runs on a GPT-class model today. Inference.net's answer is to capture that traffic through its gateway, fine-tune a smaller task-specific model on it and deploy it on a dedicated GPU. TypeSafe AI's answer is Jev, a decision model that takes a defined answer space and returns a typed answer with calibrated confidence in about 100ms, which TypeSafe says is roughly 40 to 200x faster than an LLM. The two aim at the same line on the bill, with very different methods.

Jev cannot return an answer outside its schema, but it is in early access, text-only and a new programming model to learn. Inference.net's distilled models can generate text, not just pick options, and its Batch API handles millions of offline requests. Its downside is thin independent benchmarking. Decision-shaped tasks with a fixed answer set fit Jev. Tasks that need generated output, or that run as huge offline batches, fit Inference.net.

What Inference.net and TypeSafe AI 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: open catalog models plus customer fine-tunes served on dedicated GPUs

Full Inference.net profile

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 profile

Should you choose Inference.net or TypeSafe AI?

Inference.net

Choose Inference.net for

Distilled models that still generate text
Million-request offline batches
Teams with production traces to train on

TypeSafe AI

Choose TypeSafe AI for

Fixed-answer decisions in about 100ms
Routing and guardrails with calibrated confidence
Schema-bound answers that cannot fall outside the options

Inference.net vs TypeSafe AI at a glance

Attribute	Inference.net	TypeSafe AI
Model access	Open, closed and custom	Decision models
Flagship models	Customer fine-tunes	Jev, jev-1.13
Speed	Batch windows of 24h to 7 days	~100ms per call
Price	Discounted spare GPU capacity	A fraction of an LLM call
Customization	Distill traces into custom models	Unknown
Deployment	Batch API, gateway, dedicated GPUs	Early-access API
Long context	Varies by model	Unknown

Frequently asked questions

What is the difference between Inference.net and TypeSafe AI?

Both cut the cost of narrow LLM tasks. TypeSafe AI does it with a typed decision model; Inference.net does it by distilling your own traffic.

When should I choose Inference.net over TypeSafe AI?

Distilled models that still generate text; Million-request offline batches; Teams with production traces to train on.

When should I choose TypeSafe AI over Inference.net?

Fixed-answer decisions in about 100ms; Routing and guardrails with calibrated confidence; Schema-bound answers that cannot fall outside the options.

Is Inference.net or TypeSafe AI cheaper?

Inference.net: Discounted spare GPU capacity. TypeSafe AI: A fraction of an LLM call. 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.

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