Decisions API

curl https://gateway.bud.studio/v1/decisions \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "my-decision-deployment",
    "input": "Customer: I was charged twice for my order #1234 and want a refund now!",
    "questions": [
      {
        "name": "is_refund_request",
        "type": "predicate",
        "instructions": "Is the customer asking for a refund?"
      },
      {
        "name": "team",
        "type": "choice",
        "instructions": "Which team should handle this?",
        "choices": [
          { "value": "billing" },
          { "value": "shipping" },
          { "value": "technical", "description": "bugs and outages" }
        ]
      },
      {
        "name": "urgency",
        "type": "score",
        "instructions": "How urgent is this?",
        "levels": [{ "label": "low" }, { "label": "medium" }, { "label": "high" }]
      }
    ]
  }'

# openai>=3.26.0 has client.decisions
from openai import OpenAI

client = OpenAI(base_url="https://gateway.bud.studio/v1", api_key="YOUR_API_KEY")

decision = client.decisions.create(
    model="my-decision-deployment",
    input="Customer: I was charged twice for my order #1234 and want a refund now!",
    questions=[
        {"name": "is_refund_request", "type": "predicate",
         "instructions": "Is the customer asking for a refund?"},
        {"name": "team", "type": "choice", "instructions": "Which team should handle this?",
         "choices": [{"value": "billing"}, {"value": "shipping"},
                     {"value": "technical", "description": "bugs and outages"}]},
        {"name": "urgency", "type": "score", "instructions": "How urgent is this?",
         "levels": [{"label": "low"}, {"label": "medium"}, {"label": "high"}]},
    ],
)

for answer in decision.answers:  # one per question, in question order
    print(answer)
print(decision.usage)

// openai >= 7.30.0 has client.decisions
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://gateway.bud.studio/v1",
  apiKey: "YOUR_API_KEY",
});

const decision = await client.decisions.create({
  model: "my-decision-deployment",
  input: "Customer: I was charged twice for my order #1234 and want a refund now!",
  questions: [
    { name: "is_refund_request", type: "predicate", instructions: "Is the customer asking for a refund?" },
    {
      name: "team",
      type: "choice",
      instructions: "Which team should handle this?",
      choices: [{ value: "billing" }, { value: "shipping" }, { value: "technical", description: "bugs and outages" }],
    },
    {
      name: "urgency",
      type: "score",
      instructions: "How urgent is this?",
      levels: [{ label: "low" }, { label: "medium" }, { label: "high" }],
    },
  ],
});

for (const answer of decision.answers) console.log(answer);
console.log(decision.usage);

{
  "model": "my-decision-deployment",
  "answers": [
    { "type": "predicate", "name": "is_refund_request", "probability": 1.0 },
    {
      "type": "choice",
      "name": "team",
      "choice": "billing",
      "confidence": 1.0,
      "probabilities": [
        { "value": "billing", "probability": 1.0 },
        { "value": "shipping", "probability": 0.0 },
        { "value": "technical", "probability": 0.0 }
      ]
    },
    {
      "type": "score",
      "name": "urgency",
      "score": 1.83,
      "confidence": 0.75,
      "probabilities": [
        { "value": 0, "label": "low", "probability": 0.01 },
        { "value": 1, "label": "medium", "probability": 0.15 },
        { "value": 2, "label": "high", "probability": 0.84 }
      ]
    }
  ],
  "usage": {
    "input_tokens": 547,
    "input_tokens_details": { "cached_tokens": 0, "cache_write_tokens": 0 },
    "output_tokens": 0,
    "output_tokens_details": { "reasoning_tokens": 0 },
    "total_tokens": 547
  }
}

{
  "model": "my-decision-deployment",
  "answers": [
    { "type": "refusal", "name": "steps_ok" },
    { "type": "predicate", "name": "is_harmful", "probability": 1.0 }
  ],
  "usage": {
    "input_tokens": 167,
    "input_tokens_details": { "cached_tokens": 0, "cache_write_tokens": 0 },
    "output_tokens": 0,
    "output_tokens_details": { "reasoning_tokens": 0 },
    "total_tokens": 167
  }
}

• predicate: the probability that a statement holds;
• choice: the chosen option, with the probability of every option;
• score: a position on an ordered scale, with the probability of every level.

​

Decision models

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Headers

​

Body

​

Input

{
  "model": "my-decision-deployment",
  "input": [
    { "role": "user", "content": [{ "type": "input_json", "data": { "order": 1234, "status": "charged twice" } }] }
  ],
  "questions": [{ "type": "predicate", "instructions": "Is this a duplicate charge?" }]
}

​

Questions

• instructions is required: a non-empty string, or an array of input_text, input_image and input_json parts.
• name is optional. When set it must be non-empty and unique across the request, and the answer carries it back. An unnamed question is answered with "name": null.
• Any other field inside a question, a choice or a level is refused with 400 unknown_parameter, and param names it (for example questions[1].choices[0].weight).

​

Response

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Limits

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Providers

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OpenAI and Azure OpenAI

​

System One providers

• No images. input_image parts in the input or in instructions are refused with 400 unsupported_input before the call.
• Conversations become text. An input of messages or tool items is sent as a plain-text transcript, one block per item (user: …, assistant: …, tool_call …, tool_result …). Roles survive as text, not as structure. A string input, or a single user message holding a single input_json part, is sent as is.
• Descriptions become text. Choice and level descriptions reach the provider as part of the option text; level probabilities are mapped back by index.
• Cache and safety fields have no effect. prompt_cache_key, prompt_cache_options, safety_identifier and other top-level fields are not sent.
• No refusals. System One providers always answer every question.
• Bearer authentication only. The provider is called with Authorization: Bearer; a server that needs another header is not supported.
• Usage is the provider’s input_tokens and output_tokens; cached_tokens, cache_write_tokens and reasoning_tokens are always 0.

​

TypeSafe

• Models: jev-latest (currently jev-1.14.0), and the pinned jev-1.14.0 and jev-1.13.0. The response model is your deployment’s name, so deploy a pinned version when you need to know which one answered.
• Up to 255 choices and 10 levels per question. TypeSafe sets no question limit of its own, so Bud’s 400 applies.
• Context: 64K tokens per request and 32K per question. TypeSafe’s long-context mode is not used.
• TypeSafe reports output tokens (about 20 per question) but does not charge for them: the catalog prices input tokens only (USD 0.042 per million at the time of writing), with output at 0.

​

Embedding Studio

• Model: laya. Up to 400 questions, 128 choices and 10 levels per question; no images.
• Priced per request, not per token. Embedding Studio charges per record whatever the number of questions (USD 0.002 in the catalog), and reports no token counts, so every usage field is 0. An Embedding Studio deployment is therefore priced per request only: a per-token price would cost every call nothing, and is refused. See Billing.
• choice is the option with the highest probability, and score is the expected level index computed from the returned distribution. confidence is Embedding Studio’s own.
• Abstention is not returned. Embedding Studio can mark an answer as abstained (with the set of options it could not separate, and a review route). The Decisions API has no field for that, so it is not passed back: you get the full distribution and can apply your own threshold. Embedding Studio projects (fitted heads) and templates are not reachable through Bud.
• Errors keep Embedding Studio’s status, with its message cut to 512 characters. A spent account balance is returned as 402 provider_payment_required.

​

Billing

• Cost is input tokens at the deployment’s input price (cached tokens at the cached-input price when one is set), plus output tokens at the output price. Decision models usually report no output tokens, or do not charge for them.
• OpenAI counts cache writes inside input_tokens; they are billed at the input price.
• A deployment without a price records no cost.
• A per-request price applies only to a deployment that serves nothing but decisions. A deployment that also serves chat is priced per token, so a per-request provider behind it stays unpriced. A provider that reports no tokens (Embedding Studio) is priced per request only.
• Every successful call counts toward the project’s usage limits. A call over a limit is refused with 402 before the provider is called.
• Responses are not cached: identical repeated calls are sent to the provider and billed each time.

​

Guardrails

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Not supported

• Streaming (stream: true).
• Request retries and fallback_models: a failed decisions call is not retried and does not fall back to another model.
• box questions, and images on System One providers.

​

Errors

{
  "error": {
    "message": "Question names must be unique within the request.",
    "type": "invalid_request_error",
    "param": "questions[1].name",
    "code": "invalid_value"
  }
}