kettei vs Strands Decider 2B

kettei

OneHot returns probabilities for up to 128 typed questions a call at $0.02 to $0.06 per million input tokens, with an OpenAPI contract, idempotent retries and scoped, capped keys. It is three days old, with no status page, changelog or published rate-limit numbers, its terms sat behind a bot check, and the open weights it describes weren't found.

Strands Decider 2B

A 1.9B-parameter Apache-2.0 decision model that runs on a laptop GPU, an Apple silicon Mac or a CPU, with its training data, recipe and per-version results published. It's an experimental 0.1.0 release with a 4,096-token window that cuts long states by default, and its local server has no authentication.

kettei

1. Send context and decisions to POST https://api.sqwish.ai/v1/decide with a bearer key. Leaving out model gets kettei-core, which reads text only
2. Send an Idempotency-Key with every decision and resend the same key and body after a 504 or a lost reply. Replays aren't charged
3. Branch on error.code and error.retryable, not the status. Wait for Retry-After on 429 and retryable 503s
4. Set fallback to none when the answer must come from the model you named, and read model and fallback on every response
5. Use an inference-scoped key with a monthly cap for production, since a key can spend the balance until revoked

Strands Decider 2B

1. Pin the checkpoint by its full name, such as StrandsAgents/strands-decider-2B-hobson-v21, since each version is a separate Hugging Face repository
2. Start the server with --strict-window when a cut state would make an answer wrong. It then returns 422 naming the window
3. Ask every question about one state in one request. The state is read once and each question adds only its own tokens
4. Keep the server on 127.0.0.1 or put an authenticating proxy in front. It has no key option
5. Measure thresholds on your own traffic before acting automatically. The card says confidence bands hold for short classification only