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Jev vs Llama: Hosted Verdicts vs Self-Host
Jev vs Llama for open model classification: what hosted verdicts cost, what self-hosting costs, and when running Llama yourself wins.
The Jev vs Llama question is rarely about quality. It's about where the model runs and who carries the pager. Jev is TypeSafe AI's hosted decision model: you send a closed question, you get back probabilities over the answers, and someone else runs the GPUs. Llama is Meta's family of open-weight models: you download them, serve them, and own everything that happens next, including the 3 a.m. part.
Both can do open model classification in the loose sense. They make you pay in different currencies.
Jev vs Llama: what you're actually comparing
Jev is a single-purpose hosted model. It answers closed-set questions (yes, no, which of these categories) and, per ecosystem documentation, returns a probability per choice, which you use as a confidence signal. It doesn't generate text. You don't pick hardware, tune a server, or patch anything.
Llama is a general open-weight model family. It generates text, follows instructions, and can classify if you ask it to and constrain its output. You pick a size, a quantization, a serving stack, and a machine. Because it's generative, getting a clean closed answer out of it takes work: constrained decoding, reading token probabilities, or parsing and retrying.
That second point is where the ecosystem got interesting. The open interface for typed decisions over self-hosted models is TypeLLM, and that page owns the details. One community build, llamacpp-jev, goes further and puts a Jev-shaped typed-question interface (questions in, probabilities out, text plus vision, per its author) in front of an unmodified llama-server. Neither claims Jev's speed or calibration; those depend on the model and hardware you bring.
The cost table
No official per-verdict price exists on this site for either side, and we won't invent one. Check docs.typesafe.ai for Jev's current pricing and your cloud provider for GPU rates. What we can compare is where the money goes.
| Cost line | Hosted Jev | Self-hosted Llama |
|---|---|---|
| Per-verdict fee | Yes, reported at fractions of a cent | None |
| GPU or server bill | None | Yes, whether busy or idle |
| Setup time | An API key and an afternoon | Serving stack, sizing, tuning |
| Structured output | Native closed answers | Your constraint layer |
| Ops and on-call | The vendor's | Yours |
| Scaling up for a spike | The vendor's problem | Your capacity plan |
| Data leaves your network | Yes | No |
For scale on the hosted side, builder receipts: 500 emails classified for 3.5 cents (build, as reported) and 3,282 posts at eight questions each, about 26,000 verdicts, for $0.1282 (build, as reported). The rest of the cost table follows the same pattern.
The napkin rule: at those reported rates, a self-hosted GPU has to stay busy on a lot of verdicts before its monthly bill beats hosted per-verdict pricing. Most teams don't have that volume, and most that think they do haven't counted idle hours.
When self-hosting Llama wins
- Data can't leave. Air-gapped environments, strict residency rules, or contracts that forbid third-party processing. Hosted Jev has no local option (can Jev run locally? covers why), so this is the clean win.
- Enormous, steady volume. If your GPUs would run near capacity around the clock, owned compute can undercut per-call fees. Do the math with real utilization, not peak.
- You need generation too. If the same pipeline must also write summaries or replies, a general model is doing two jobs. You may still want a decision model in front of it for the closed calls.
- Vendor independence is a real requirement. Sometimes the strategic comfort is worth a line item, and that's a legitimate call.
When hosted Jev wins
Almost everywhere else, especially early. No hardware, no serving stack, native closed answers, and reported per-verdict costs small enough that the bill isn't the conversation. The broader field (small chat models, trained classifiers, embeddings, rules) is compared in Jev alternatives.
The pragmatic path: prototype on hosted, keep your question sets and gold data versioned, and treat the self-host route as a documented exit. Questions, choice sets, and calibration data move between the two worlds; only the endpoint and serving change.
Frequently asked questions
Is Jev better than Llama for classification?
They're different products. Jev is a hosted decision model with native closed answers; Llama is an open-weight general model you serve and constrain yourself. Test both on your own labeled data before deciding.
Is self-hosting Llama cheaper than Jev?
Only at high, steady volume where your GPUs stay busy. At the per-verdict costs builders report for Jev, idle GPU hours usually cost more than the hosted calls would have.
Can I get Jev-style typed answers from a local Llama?
Community tools like TypeLLM and llamacpp-jev offer that interface over self-hosted models. Speed and calibration depend on your model and hardware, not on Jev.
Can Jev run on my own servers?
No. It's hosted only, with no published weights; see is Jev open source? for details.
Numbers throughout are as reported by the build authors, not verified by shipwithjev. Code-shaped examples are pseudocode; the official docs live at docs.typesafe.ai.