the short answer
No official downloadable Jev weights or self-hosted inference package was identified as of October 2, 2026. TypeSafe’s SDKs call a hosted model, and Cloudflare offers another managed route. For local inference, Ollama lists Nimble and Tev1, while SGLang documents a decision endpoint for supported chat models; Ollaya is another runtime. These routes do not serve official Jev weights. Check each artifact and license, then remeasure quality and calibration before substituting a local model.
- Official weights
- No public release identified as of October 2, 2026
- Official SDKs
- Clients, not inference runtimes
- Cloudflare route
- Managed provider access
- Local examples
- Ollama serves independent Nimble and Tev1 models
- Migration requirement
- Rebenchmark behavior and calibration
Self-Hosting Means Controlling the Inference Runtime
A self-hosted model runs on infrastructure your organization controls, with weights and serving software available under suitable terms. Installing an SDK locally does not meet that definition when the SDK sends the request to a remote API. A proxy, gateway or private network connection can improve control without becoming self-hosted inference.
Use precise deployment language in architecture and procurement documents: direct hosted API, managed provider, customer-controlled gateway, private connectivity, or customer-run model. Each creates different security and operational responsibilities.
Distinguish the Available Deployment Shapes
| Shape | Where inference runs | Is official Jev self-hosted? |
|---|---|---|
| TypeSafe API through official SDK | TypeSafe service | No |
| Cloudflare Workers AI route | Cloudflare-managed service | No |
| Application-owned API gateway | Gateway local; model remains remote | No |
| Community Jev-compatible server | Where operator deploys it | No; it is a separate implementation |
| Ollama with Nimble or Tev1 | Customer hardware through local /v1/systemone | No; Nimble and Tev1 are separate models |
| Official weights on customer hardware | Customer environment | Not publicly identified |
Check the Model Behind a Local Serving Route
Ollama’s Nimble and Tev1 listings show local /v1/systemone examples and require Ollama 0.35 or later. SGLang documents native /v1/decisions and /v1/systemone routes for supported chat models; its docs say to use a nightly build until a release includes them. Ollaya is a separate runtime. None is an official Jev deployment, and a listed context window does not validate answer quality across that length.
Pin the exact model tag, downloaded artifact, runtime version and hardware. Verify that required weights are present and inference works without an outbound dependency. Use the open-model guide to test protocol conformance and the benchmark protocol to test behavior.
Translate “Self-Hosted” into the Real Requirement
Teams often mean data residency, no public internet, fixed cost, offline operation, custom throughput, model inspection or vendor independence. A managed provider may satisfy some requirements through regional processing or private networking; only downloadable weights satisfy offline independent inference.
Write each requirement as a testable control. “Private” is too vague; “request bodies never leave region X and are deleted within Y days under contract” can be verified. See the privacy and retention checklist.
Choose the Deployment Shape from the Constraint
This prevents a costly category mistake. A private gateway can centralize credentials and logs but still sends state to remote inference. A managed regional endpoint can meet some residency needs but is still not customer-run. Only an available model artifact and runtime under acceptable licenses enable independent offline inference.
| Hard requirement | Viable starting point |
|---|---|
| Offline or air-gapped inference | A separately licensed downloadable model; official hosted Jev does not satisfy this |
| Keep application traffic behind one gateway | Customer-controlled gateway calling a hosted Jev route |
| Use an existing edge platform | Evaluate the documented Cloudflare Workers AI route |
| Inspect or modify client behavior | Pin and review TypeSafe’s public SDK source |
| Avoid a single model vendor | Build a provider adapter and validate another implementation independently |
| Meet regional or retention controls | Obtain route-specific contractual evidence; do not infer it from “hosted” or “private” |
A Local Alternative Must Be Revalidated as a New Evaluator
A model that implements Choice, Score and Noul-shaped responses is not automatically Jev. It can differ in weights, training objective, probability meaning, language coverage, adversarial behavior and latency. Preserve the interface adapter, but treat the implementation identifier as part of the evaluator version.
Run the same blind labels, calibration metrics, stress cases and operating thresholds used for hosted Jev. Do not transfer a 0.8 threshold across models. The evaluator comparison protocol and open-source status explain the evidence boundary.
Local Control Adds Operating Responsibility
| Concern | Hosted service | Self-hosted alternative |
|---|---|---|
| Capacity | Provider-managed within documented limits | You size accelerators, batching and queues |
| Updates | Provider publishes versions and aliases | You test, deploy and roll back weights |
| Security | Shared with provider and route | You patch runtime, images and dependencies |
| Observability | Provider metrics plus application telemetry | You own inference and application telemetry |
| Economics | Usage pricing | Hardware, idle capacity and operations |
Model Local Capacity Before Choosing on Token Price
A self-hosted service needs a peak-arrival model, not only an average requests-per-second estimate. Measure input-length and question-count distributions, service-time percentiles, batching behavior, accelerator memory, queue limits and timeout budget. Include redundancy and deployment headroom. An idle accelerator can cost more than hosted inference; an overloaded one can turn a fast model into a slow application.
Benchmark the complete request path with representative concurrency and record rejected, timed-out and retried calls. Compare hosted and local options at the same quality and automation coverage, including engineering, monitoring and incident-response effort. The pricing guide covers complete workflow cost and the retry guide defines failure-budget accounting.
Self-Hosting Moves—Not Removes—the Security Boundary
Local inference can reduce exposure to an external processor, but the operator now owns every control in this table. The semantic model also remains vulnerable to bad or adversarial state; deployment location does not convert its output into authorization. The privacy review maps data flow, and known limitations covers adversarial inputs.
| Control | Customer-run responsibility |
|---|---|
| Artifact integrity | Verify weight, container and dependency provenance and checksums |
| Isolation | Separate tenants, credentials, queues and cached state |
| Network | Restrict ingress, egress and administrative paths |
| Logging | Redact payloads and enforce retention and access policy |
| Updates | Patch the runtime and regression-test new checkpoints before promotion |
| Abuse and overload | Rate-limit callers and protect shared capacity from denial of service |
Verify a Self-Hosting Claim Before Publishing It
- Locate the official weight files and serving instructions.
- Read the model and code licenses for the intended use.
- Confirm architecture, tokenizer, context and supported primitives.
- Run inference without an outbound dependency.
- Benchmark calibration and limitations independently.
- Name community implementations separately from TypeSafe Jev.
FAQ
Does the Python SDK run Jev locally?
No. The public SDK is client software; it does not establish that official model weights run locally.
Is Cloudflare Workers AI self-hosting?
No. It is a managed provider route, even if your application runs in a Worker.
Can I use a Jev-compatible open model?
Potentially, but treat it as a distinct model and verify license, security, accuracy, calibration and failure behavior.
Could TypeSafe add self-hosting later?
Possibly, but no future availability should be inferred. Recheck official documentation and repositories for a dated decision.
Sources
Checked against the sources below on October 2, 2026. Model versions, prices and limits change.
- TypeSafe AI docs: Models
- TypeSafe AI: Official GitHub organization
- TypeSafe AI: Official Python SDK
- TypeSafe AI: Official JavaScript SDK
- Cloudflare Workers AI: TypeSafe Jev
- Ollama: Nimble local serving
- Ollama: Tev1 local serving
- Ollaya: local decision-model runtime
- SGLang: decision-model API documentation
Change note: Added source-checked local serving routes for independent decision models.