SPARK AITalk to engineering ↗
DEVELOPERS / BUILD WITH SPARK

One API from experiment
to operated intelligence.

Prototype with a published model, evaluate alternatives, then move the same application onto dedicated or private capacity. The interface stays stable while the operating path matures.

View quickstart ↓
PRIVATE PREVIEW / OPENAI-COMPATIBLE

Change the route.
Keep the application.

API access is currently issued after workload and security review; there is no public self-service registration or automatically generated API key. Approved customers receive a scoped endpoint, credential, model list, and onboarding guide.

PythonJavaScriptRESTStreaming
Request private API access →
from openai import OpenAI

client = OpenAI({
  base_url=SPARK_BASE_URL,
  api_key=SPARK_API_KEY
})

response = client.chat.completions.create({
  model=SPARK_MODEL_ID,
  messages=messages,
  response_format=decision_schema
})
BUILDING BLOCKS

Useful primitives,
not infrastructure chores.

SPARK adds the technical layer between model access and a production AI product: routing, output control, evaluation, observability, and deployment lifecycle.

01

OpenAI-compatible API

Move an existing chat, responses, embedding, or tool-use workload with a base URL and model change.

02

Policy routing

Address a stable route while SPARK selects an approved model and capacity lane for each request.

03

Structured outputs

Constrain production responses to schemas and trace validation failures before they reach an application.

04

Async and batch

Separate interactive traffic from evaluation, extraction, and offline generation jobs.

05

Evaluation hooks

Attach task metrics, human review, and release gates to model and prompt versions.

06

Request telemetry

Inspect latency, token use, route decisions, cache behavior, and deployment health.

FROM FIRST CALL TO PRIVATE RELEASE
01

Evaluate

Compare owned, adapted, and officially connected models against task-specific evidence.

02

Configure

Set budgets, data rules, regions, fallback behavior, and quality thresholds.

03

Load test

Prove throughput, tail latency, error recovery, and capacity assumptions.

04

Promote

Release to an elastic, dedicated, or private lane with a versioned rollback path.

TECHNICAL ONBOARDING

Bring one workload.
Leave with a serving plan.

We will map the API surface, model candidates, evaluation set, and deployment lane.

Request developer access ↗