Fireworks AI alternative. One API for more model choices.

Why developers consider a Fireworks AI alternative

Fireworks AI offers model inference and deployment. Separate your serverless inference requests from any dedicated deployments or custom-model dependencies. A move to a common model API does not automatically transfer a deployment.

TextCortex brings proprietary and open-weight model access into a common OpenAI-compatible API. The advantage is the ability to keep one client while selecting models across families and European hosting options.

A broader model choice for your application

Use GPT, Claude and Gemini alongside Kimi K3, GLM-5.3, DeepSeek and MiMo. Select an available model for each request, so your coding assistant, extraction service and support feature can use different models.

For European workloads, choose the EU-hosted route enabled for the model. Broader catalog access does not mean every route has the same hosting location.

Compare the API features you actually use

Separate your serverless inference requests from any dedicated deployments or custom-model dependencies. A move to a common model API does not automatically transfer a deployment.

Start with the Fireworks AI API comparison guide. Compare streaming behavior, tool calls, reasoning settings, output validation, usage reporting and current rates for your own workload.

Make your first API request

Create a TextCortex account and generate an API key in account settings. Install the OpenAI Python package on your server. Store the key in TEXTCORTEX_API_KEY and set TEXTCORTEX_MODEL to an identifier returned by GET /models.

This example keeps credentials in environment variables. The same client configuration works when you choose another supported model. Read the API reference for endpoint-specific parameters.

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["TEXTCORTEX_API_KEY"],
    base_url="https://api.textcortex.com/v1",
)

for model in client.models.list():
    print(model.id)

response = client.chat.completions.create(
    model=os.environ["TEXTCORTEX_MODEL"],
    messages=[{"role": "user", "content": "Hello"}],
)
print(response.choices[0].message.content)

Move one workload first

Use the migration guide to update your base URL, key and model mapping. Keep the existing integration available while you test a small share of traffic.

TextCortex may use infrastructure providers that also sell inference directly. The comparison concerns the API service and available model routes, not a claim that their underlying infrastructure never overlaps.