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Databricks: DBRX 132B Instruct
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Databricks: DBRX 132B Instruct

Databricks’ Open-Weight Enterprise LLM for Data, Coding, and Business AI via API

Context window: 
N/A 
M tokens
Output: 
32000
 tokens
Modality:
Text
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Databricks’ Open-Weight LLM for Enterprise-Grade AI Workflows

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DBRX 132B Instruct is Databricks’ flagship open-weight large language model, designed for enterprise-scale applications in data engineering, business intelligence, and AI-powered analytics. With 132 billion parameters, DBRX Instruct is optimized for reasoning, coding, and domain-specific instruction following, making it one of the largest open-source models available.

Now accessible via AnyAPI.ai, DBRX 132B Instruct provides developers and enterprise teams with both the transparency of open weights and the scalability of managed API infrastructure.

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Key Features of DBRX 132B Instruct

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Massive Scale (132B Parameters)

Delivers top-tier reasoning, code generation, and data analysis comparable to proprietary frontier models.

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Instruction-Tuned for Enterprise Tasks

Fine-tuned for structured outputs, SQL queries, BI dashboards, and enterprise workflows.

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Extended Context Window (32k Tokens)

Processes large documents, codebases, or multi-turn dialogues without losing coherence.

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Open-Weight Availability

Released with transparent weights, enabling private hosting, fine-tuning, and compliance-friendly deployments.

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Strong Coding and Data Capabilities

Specialized in Python, SQL, and other languages for data science and analytics-driven applications.

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Use Cases for DBRX 132B Instruct

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Business Intelligence and Analytics

Generate SQL queries, interpret dashboards, and assist with BI platform integrations.

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Enterprise Knowledge Assistants

Deploy DBRX for document-heavy corporate workflows such as HR, compliance, and operations.

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Code Generation and Data Engineering

Write, refactor, and debug Python, SQL, and Spark pipelines in enterprise data environments.

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Research and Report Summarization

Summarize whitepapers, financial filings, and technical documents with high accuracy.

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RAG and Knowledge Base Integration

Combine with Databricks’ Lakehouse or external vector stores for retrieval-augmented generation.

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Why Use DBRX 132B Instruct via AnyAPI.ai

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No GPU Setup Required

Access DBRX instantly without managing infrastructure or cluster provisioning.

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Unified API for Proprietary and Open Models

Query DBRX alongside GPT, Claude, Gemini, and Mistral models with a single SDK.

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Usage-Based Billing

Scale affordably with pay-as-you-go pricing for enterprise workloads.

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Production Monitoring and Observability

Track latency, token usage, and cost across workloads with real-time analytics.

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Better Reliability Than HF Inference or OpenRouter

Optimized endpoints for consistent, high-volume enterprise usage.

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Enterprise-Scale AI with DBRX 132B Instruct

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DBRX 132B Instruct combines massive scale, open-weight transparency, and enterprise alignment—ideal for AI-driven data workflows and business applications.

Start building with DBRX via AnyAPI.ai - sign up, get your API key, and launch enterprise AI in minutes.

Sample code for 

Databricks: DBRX 132B Instruct

Python
JavaScript
Curl
import requests

url = "https://api.anyapi.ai/v1/chat/completions"

payload = {
    "stream": False,
    "tool_choice": "auto",
    "logprobs": False,
    "model": "Model_Name",
    "messages": [
        {
            "role": "user",
            "content": "Hello"
        }
    ]
}
headers = {
    "Authorization": "Bearer AnyAPI_API_KEY",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.json())
import requests url = "https://api.anyapi.ai/v1/chat/completions" payload = { "stream": False, "tool_choice": "auto", "logprobs": False, "model": "Model_Name", "messages": [ { "role": "user", "content": "Hello" } ] } headers = { "Authorization": "Bearer AnyAPI_API_KEY", "Content-Type": "application/json" } response = requests.post(url, json=payload, headers=headers) print(response.json())
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const url = 'https://api.anyapi.ai/v1/chat/completions';
const options = {
  method: 'POST',
  headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'},
  body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"Model_Name","messages":[{"role":"user","content":"Hello"}]}'
};

try {
  const response = await fetch(url, options);
  const data = await response.json();
  console.log(data);
} catch (error) {
  console.error(error);
}
const url = 'https://api.anyapi.ai/v1/chat/completions'; const options = { method: 'POST', headers: {Authorization: 'Bearer AnyAPI_API_KEY', 'Content-Type': 'application/json'}, body: '{"stream":false,"tool_choice":"auto","logprobs":false,"model":"Model_Name","messages":[{"role":"user","content":"Hello"}]}' }; try { const response = await fetch(url, options); const data = await response.json(); console.log(data); } catch (error) { console.error(error); }
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curl --request POST \
  --url https://api.anyapi.ai/v1/chat/completions \
  --header 'Authorization: Bearer AnyAPI_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
  "stream": false,
  "tool_choice": "auto",
  "logprobs": false,
  "model": "Model_Name",
  "messages": [
    {
      "role": "user",
      "content": "Hello"
    }
  ]
}'
curl --request POST \ --url https://api.anyapi.ai/v1/chat/completions \ --header 'Authorization: Bearer AnyAPI_API_KEY' \ --header 'Content-Type: application/json' \ --data '{ "stream": false, "tool_choice": "auto", "logprobs": false, "model": "Model_Name", "messages": [ { "role": "user", "content": "Hello" } ] }'
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Frequently
Asked
Questions

Answers to common questions about integrating and using this AI model via AnyAPI.ai

Yes, with transparent weights for private or API deployment.

Data engineering, BI tasks, enterprise assistants, and coding-heavy workflows.

It offers transparency and open deployment but lacks multimodal support.

Yes, it can be deployed on GPUs or integrated directly with Databricks’ Lakehouse.

Yes, though optimized primarily for English and enterprise use cases.