How do I control consumption?
Create keys per project, define a budget per key, and follow use in the console before increasing volume.
Generative AI
OpenAI-compatible AI in the same platform console
Use models through a familiar API, create keys in the console, set a budget, and follow Brazilian-real consumption alongside your applications and databases.
From API key to observed consumption
The AI experience follows the same platform logic: create a key, integrate with a base URL, measure consumption, and evolve usage predictably.
Base configuration
base_url: https://ai.zenifra.com/v1
model: alibaba/qwen3.6-35b-a3b
api_key: created in the console
Use cases
- Chatbots and internal support
- Agents for operational automation
- Document analysis and data extraction
- Integrations with a per-key budget
Current limitations
Review available models, context window, concurrency, and consumption limits before taking a workload to production. The technical documentation keeps parameters and errors current for more sensitive integrations.
When to use AI at Zenifra
Cases where keeping the AI API, budget, and consumption in the same console as applications and databases makes sense.
When to use AI at Zenifra
Use it when you want to integrate AI into the product without separating infrastructure, keys, budgets, and consumption into another console.
Chatbots and internal agents
Connect internal systems, support, and automations through a familiar API.
Document analysis
Extract, classify, and transform text while following Brazilian-real consumption.
API key flow
A short sequence to create a key, change the base URL, and observe consumption before scaling usage.
01
Create the key
Create an API key per project, with a budget to limit use and reduce surprise consumption.
02
Change the base URL
Use OpenAI compatibility with Zenifra’s base_url and the model available in the console.
03
Monitor consumption
Follow calls, costs, and limits before expanding to critical flows.
Consumption control
Decisions that help separate environments, limit budgets, and understand current API limits.
Model
Choose the model available for the use case and confirm current limitations.
Per-key budget
Consumption control per key helps separate environments and integrations.
Context and concurrency
Check the context window and limits before more sensitive workloads.
Environment
Use separate keys for development, staging, and production.
Quick examples
Test with the same interface you already use in OpenAI-compatible projects: change the base URL, use your Zenifra key, and send the first request.
curl
curl https://ai.zenifra.com/v1/chat/completions \
-H "Authorization: Bearer $ZENIFRA_AI_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model":"alibaba/qwen3.6-35b-a3b","messages":[{"role":"user","content":"Hello, who are you?"}]}' \
| jq -r '.choices[0].message.content'JavaScript
import OpenAI from 'openai'
const client = new OpenAI({
apiKey: process.env.ZENIFRA_AI_API_KEY,
baseURL: 'https://ai.zenifra.com/v1',
})
const completion = await client.chat.completions.create({
model: 'alibaba/qwen3.6-35b-a3b',
messages: [
{ role: 'user', content: 'Hello, who are you?' },
],
})
console.log(completion.choices[0].message.content)Python
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["ZENIFRA_AI_API_KEY"],
base_url="https://ai.zenifra.com/v1",
)
completion = client.chat.completions.create(
model="alibaba/qwen3.6-35b-a3b",
messages=[
{"role": "user", "content": "Hello, who are you?"},
],
)
print(completion.choices[0].message.content)AI key in the console
Create per-project keys, define a budget per key, and follow consumption to keep predictability before scaling usage.
Frequently asked questions
Short answers to decide whether the AI API already fits your workflow.
Do I need to change my SDK?
In most cases, use an OpenAI-compatible client and change the base URL, model, and API key.