Wire format

The SDK is a thin, typed wrapper over a plain HTTP API. This page documents the actual JSON on the wire so you can debug requests, build a binding for a language we don’t ship yet, or call the platform directly with curl. Field names are snake_case in JSON, regardless of the casing the SDK exposes in each language.

Nota

You almost never need this page to use the SDK. Reach for it when you’re inspecting traffic, integrating from an unsupported runtime, or binding the C ABI yourself. Everything the SDK does maps onto the requests below.

Two planes, two tokens

There are two independent surfaces, each gated by a different token type. See Tokens for the full model.

Control plane

Provision and reconcile workloads. Authenticated with an ik_sdk_ token, scoped to one project. Cannot call inference. Used by ManagementClient.

Data plane

Call inference against a workload. Authenticated with an ik_live_ token, passed per workload. Cannot provision. Used by DataClient. OpenAI-compatible.

Base URL comes from INFERENCEKEY_BASE_URL (or the explicit client config). All control-plane paths below are relative to it.

Authentication header

Both planes use a bearer token in the Authorization header. The token prefix decides which plane you’re allowed to touch.

Control plane

Authorization: Bearer ik_sdk_xxxxxxxxxxxxxxxxxxxxxxxx
Content-Type: application/json

Data plane

Authorization: Bearer ik_live_xxxxxxxxxxxxxxxxxxxxxxxx
Content-Type: application/json

If you present the wrong prefix for the route, you get a 403 with a typed error code (see Wrong-token errors).

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Control plane

The control plane is where mgmt.ensure(...) does its work. Under the hood, ensure() is idempotent by the explicit slug: it lists/looks up the workload, then issues a POST to create it or a PATCH to reconcile drift (driven by OnDrift, default RECONCILE).

Routes

Method	Path	Purpose
POST	/api/projects/:project_id/workloads	Create a workload
PATCH	/api/workloads/:id	Update / reconcile a workload
GET	/api/projects/:project_id/workloads	List workloads in a project

:project_id is the project slug the ik_sdk_ token is scoped to. :id is the workload’s server-assigned id (or slug) returned by create/list.

CreateWorkloadRequest

Body for POST /api/projects/:project_id/workloads.

Field	Type	Required	Notes
name	string	yes	Human-readable name.
description	string	no	Free text.
task_type	string	yes	Modality. Defaults to text2text if omitted. See Task types.
backend	string	yes	One of ollama, vllm, vllm-omni, sglang.
model_name	string	yes	Model identifier the backend serves.
config	object	no	Backend-specific. For vllm/vllm-omni: { command, vllm_version? }.
worker_id	string	no	Pin to a specific worker.
gpu_resource_id	string	no	Pin to a specific GPU resource.

Precaución

There is no provider and no min_vram_gb field. Placement (which worker, which GPU) is the platform’s job — you only optionally pin with worker_id / gpu_resource_id.

POST /api/projects/acme/workloads — vLLM text2text

{
  "name": "Support Bot",
  "description": "Customer support assistant",
  "task_type": "text2text",
  "backend": "vllm",
  "model_name": "meta-llama/Llama-3.1-8B-Instruct",
  "config": {
    "command": "vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 8192",
    "vllm_version": "0.6.3"
  }
}

POST /api/projects/acme/workloads — Ollama embedding

{
  "name": "Billing Embeddings",
  "task_type": "embedding",
  "backend": "ollama",
  "model_name": "nomic-embed-text"
}

task_type values

There are 12 modalities. The default is text2text.

text2text, embedding, text2image, text2audio, audio2text,
reranker, classification, reward, ...

Nota

reranker, classification, and reward are async-only: they have no synchronous OpenAI-compatible route on the data plane. The OpenAI-compatible endpoints below cover text2text (chat/completions) and embedding.

backend and config

backend	config shape
ollama	(none required)
vllm	{ command, vllm_version? }
vllm-omni	{ command, vllm_version? }
sglang	backend-specific

In the SDK the Backend enum maps to these wire strings: Backend.Ollama → "ollama", Backend.Vllm → "vllm", Backend.VllmOmni → "vllm-omni", Backend.Sglang → "sglang".

UpdateWorkloadRequest

Body for PATCH /api/workloads/:id. Same fields as create, but all optional — send only what changes. This is what OnDrift.RECONCILE emits when ensure() detects the live workload differs from your spec.

PATCH /api/workloads/wl_01h... — reconcile the launch command

{
  "config": {
    "command": "vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 16384",
    "vllm_version": "0.6.3"
  }
}

PATCH /api/workloads/wl_01h... — change task type and description

{
  "description": "Now also serves the docs assistant",
  "task_type": "text2text"
}

Workload response shape

Returned by POST (the created workload), PATCH (the updated workload), and as each element of the GET list. The SDK surfaces project_slug and workload_slug from this on the returned ref.

Workload response

{
  "id": "wl_01h8x6m3q2k9z7v4t0n5b1c2d3",
  "name": "Support Bot",
  "description": "Customer support assistant",
  "slug": "support-bot",
  "project_id": "proj_01h8...",
  "project_slug": "acme",
  "workload_slug": "support-bot",
  "task_type": "text2text",
  "backend": "vllm",
  "model_name": "meta-llama/Llama-3.1-8B-Instruct",
  "config": {
    "command": "vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 8192",
    "vllm_version": "0.6.3"
  },
  "worker_id": null,
  "gpu_resource_id": null,
  "created_at": "2026-06-15T09:12:44Z",
  "updated_at": "2026-06-15T09:12:44Z"
}

GET /api/projects/:project_id/workloads returns an array of these objects.

curl example

1. Create (or look up) the workload with your ik_sdk_ token:

   Create a workload

   curl -X POST "$INFERENCEKEY_BASE_URL/api/projects/acme/workloads" \
     -H "Authorization: Bearer $INFERENCEKEY_SDK_TOKEN" \
     -H "Content-Type: application/json" \
     -d '{
       "name": "Support Bot",
       "task_type": "text2text",
       "backend": "vllm",
       "model_name": "meta-llama/Llama-3.1-8B-Instruct",
       "config": {
         "command": "vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 8192"
       }
     }'

2. Grab workload_slug (and project_slug) from the response — you’ll need them to build the data-plane URL below.

This is exactly what mgmt.ensure(...) does:

Python

ensure() → POST/PATCH under the hood

from inferencekey import ManagementClient, WorkloadSpec, Backend

mgmt = ManagementClient.from_env(project="acme")  # reads INFERENCEKEY_SDK_TOKEN
ref = mgmt.ensure(WorkloadSpec(
    name="support-bot",
    slug="support-bot",
    model="meta-llama/Llama-3.1-8B-Instruct",
    backend=Backend.VLLM,
    command="vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 8192",
))
print(ref.project_slug, ref.workload_slug)

TypeScript

ensure() → POST/PATCH under the hood

import { ManagementClient, Backend } from "@inferencekey/sdk";

const mgmt = ManagementClient.fromEnv({ project: "acme" });
const ref = await mgmt.ensure({
  name: "support-bot",
  slug: "support-bot",
  model: "meta-llama/Llama-3.1-8B-Instruct",
  backend: Backend.Vllm,
  command: "vllm serve meta-llama/Llama-3.1-8B-Instruct --max-model-len 8192",
});
console.log(ref.projectSlug, ref.workloadSlug);

---

Data plane

The data plane is OpenAI-compatible. Every workload exposes a versioned base under its project/workload slugs:

/endpoint/:projectSlug/:workloadSlug/v1/...

Authenticate with an ik_live_ token. This is what DataClient.endpoint(...) targets.

Routes

Method	Path (relative to …/:workloadSlug/v1)	Maps to SDK method
POST	/chat/completions	generate_text(...)
POST	/embeddings	embed(...)

These are the OpenAI Chat Completions and Embeddings shapes. Other modalities (reranker, classification, reward) are async-only and not served here.

Chat completions

POST /endpoint/acme/support-bot/v1/chat/completions

{
  "model": "support-bot",
  "messages": [
    { "role": "user", "content": "Hola" }
  ],
  "temperature": 0.2,
  "max_tokens": 300
}

Response

{
  "id": "chatcmpl-9f2c...",
  "object": "chat.completion",
  "created": 1750000000,
  "model": "meta-llama/Llama-3.1-8B-Instruct",
  "choices": [
    {
      "index": 0,
      "message": { "role": "assistant", "content": "¡Hola! ¿En qué puedo ayudarte?" },
      "finish_reason": "stop"
    }
  ],
  "usage": { "prompt_tokens": 7, "completion_tokens": 11, "total_tokens": 18 }
}

The SDK’s out.text is choices[0].message.content; out.model is the response model field.

Streaming

Set "stream": true to receive Server-Sent Events. Each event is a data: line carrying a partial chat.completion.chunk, terminated by a literal data: [DONE].

POST .../v1/chat/completions (stream: true)

data: {"id":"chatcmpl-9f2c...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"¡Hola"},"finish_reason":null}]}

data: {"id":"chatcmpl-9f2c...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{"content":"!"},"finish_reason":null}]}

data: {"id":"chatcmpl-9f2c...","object":"chat.completion.chunk","choices":[{"index":0,"delta":{},"finish_reason":"stop"}]}

data: [DONE]

Embeddings

POST /endpoint/acme/billing/v1/embeddings

{
  "model": "billing",
  "input": ["a", "b"]
}

Response

{
  "object": "list",
  "model": "nomic-embed-text",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0123, -0.0456, "..."] },
    { "object": "embedding", "index": 1, "embedding": [0.0789, -0.0011, "..."] }
  ],
  "usage": { "prompt_tokens": 2, "total_tokens": 2 }
}

The SDK’s emb.embeddings is the list of data[*].embedding vectors.

curl example

Chat completion against a workload

curl -X POST \
  "$INFERENCEKEY_BASE_URL/endpoint/acme/support-bot/v1/chat/completions" \
  -H "Authorization: Bearer $SUPPORT_IK_LIVE" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "support-bot",
    "messages": [{ "role": "user", "content": "Hola" }],
    "temperature": 0.2,
    "max_tokens": 300
  }'

And the same call through the SDK:

Python

DataClient → POST /chat/completions

from inferencekey import DataClient

data = DataClient.from_env(project="acme")
ep = data.endpoint("support-bot", api_key="ik_live_...")
out = ep.generate_text(prompt="Hola", temperature=0.2, max_tokens=300)
print(out.text, out.model)

TypeScript

DataClient → POST /chat/completions

import { DataClient } from "@inferencekey/sdk";

const data = DataClient.fromEnv({ project: "acme" });
const ep = data.endpoint("support-bot", { apiKey: process.env.SUPPORT_IK_LIVE });
const out = await ep.generateText({ prompt: "Hola", temperature: 0.2, maxTokens: 300 });
console.log(out.text, out.model);

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Wrong-token errors

Presenting the wrong token type — or the right type with insufficient scope — returns a 403 with a typed code. The SDK turns these into PermissionDenied. See Common errors.

code	Meaning
wrong_credential_type	ik_live_ on a control route, or ik_sdk_ on a data route.
project_scope_mismatch	Token is scoped to a different project than the path.
scope_insufficient	Token lacks the scope required for the operation.

403 — wrong credential type

{
  "error": {
    "code": "wrong_credential_type",
    "message": "This route requires an ik_sdk_ control-plane token."
  }
}

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See also

Tokens ik_sdk_ vs ik_live_, scopes, and precedence.

OnDrift How ensure() decides between POST, PATCH, and failing.

Backends & policies Backend wire strings and execution policies.

Common errors Typed error codes and how the SDK maps them.

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