Databricks Model Serving

Deploy MLflow models and AI agents to scalable REST API endpoints.

Quick Decision: What Are You Deploying?

Model Type	Pattern	Reference
Traditional ML (sklearn, xgboost)	mlflow.sklearn.autolog()	1-classical-ml.md
Custom Python model	mlflow.pyfunc.PythonModel	2-custom-pyfunc.md
GenAI Agent (LangGraph, tool-calling)	ResponsesAgent	3-genai-agents.md

Prerequisites

• DBR 16.1+ recommended (pre-installed GenAI packages)
• Unity Catalog enabled workspace
• Model Serving enabled

Foundation Model API Endpoints

ALWAYS use exact endpoint names from this table. NEVER guess or abbreviate.

Chat / Instruct Models

Endpoint Name	Provider	Notes
databricks-gpt-5-2	OpenAI	Latest GPT, 400K context
databricks-gpt-5-1	OpenAI	Instant + Thinking modes
databricks-gpt-5-1-codex-max	OpenAI	Code-specialized (high perf)
databricks-gpt-5-1-codex-mini	OpenAI	Code-specialized (cost-opt)
databricks-gpt-5	OpenAI	400K context, reasoning
databricks-gpt-5-mini	OpenAI	Cost-optimized reasoning
databricks-gpt-5-nano	OpenAI	High-throughput, lightweight
databricks-gpt-oss-120b	OpenAI	Open-weight, 128K context
databricks-gpt-oss-20b	OpenAI	Lightweight open-weight
databricks-claude-opus-4-6	Anthropic	Most capable, 1M context
databricks-claude-sonnet-4-6	Anthropic	Hybrid reasoning
databricks-claude-sonnet-4-5	Anthropic	Hybrid reasoning
databricks-claude-opus-4-5	Anthropic	Deep analysis, 200K context
databricks-claude-sonnet-4	Anthropic	Hybrid reasoning
databricks-claude-opus-4-1	Anthropic	200K context, 32K output
databricks-claude-haiku-4-5	Anthropic	Fastest, cost-effective
databricks-claude-3-7-sonnet	Anthropic	Retiring April 2026
databricks-meta-llama-3-3-70b-instruct	Meta	128K context, multilingual
databricks-meta-llama-3-1-405b-instruct	Meta	Retiring May 2026 (PT)
databricks-meta-llama-3-1-8b-instruct	Meta	Lightweight, 128K context
databricks-llama-4-maverick	Meta	MoE architecture
databricks-gemini-3-1-pro	Google	1M context, hybrid reasoning
databricks-gemini-3-pro	Google	1M context, hybrid reasoning
databricks-gemini-3-flash	Google	Fast, cost-efficient
databricks-gemini-2-5-pro	Google	1M context, Deep Think
databricks-gemini-2-5-flash	Google	1M context, hybrid reasoning
databricks-gemma-3-12b	Google	128K context, multilingual
databricks-qwen3-next-80b-a3b-instruct	Alibaba	Efficient MoE

Embedding Models

Endpoint Name	Dimensions	Max Tokens	Notes
databricks-gte-large-en	1024	8192	English, not normalized
databricks-bge-large-en	1024	512	English, normalized
databricks-qwen3-embedding-0-6b	up to 1024	~32K	100+ languages, instruction-aware

Common Defaults

• Agent LLM: databricks-meta-llama-3-3-70b-instruct (good balance of quality/cost)
• Embedding: databricks-gte-large-en
• Code tasks: databricks-gpt-5-1-codex-mini or databricks-gpt-5-1-codex-max

These are pay-per-token endpoints available in every workspace. For production, consider provisioned throughput mode. See supported models.

Reference Files

Topic	File	When to Read
Classical ML	1-classical-ml.md	sklearn, xgboost, autolog
Custom PyFunc	2-custom-pyfunc.md	Custom preprocessing, signatures
GenAI Agents	3-genai-agents.md	ResponsesAgent, LangGraph
Tools Integration	4-tools-integration.md	UC Functions, Vector Search
Development & Testing	5-development-testing.md	MCP workflow, iteration
Logging & Registration	6-logging-registration.md	mlflow.pyfunc.log_model
Deployment	7-deployment.md	Job-based async deployment
Querying Endpoints	8-querying-endpoints.md	SDK, REST, MCP tools
Package Requirements	9-package-requirements.md	DBR versions, pip

---

Quick Start: Deploy a GenAI Agent

Step 1: Install Packages (in notebook or via MCP)

%pip install -U mlflow==3.6.0 databricks-langchain langgraph==0.3.4 databricks-agents pydantic
dbutils.library.restartPython()

Or via MCP:

execute_code(code="%pip install -U mlflow==3.6.0 databricks-langchain langgraph==0.3.4 databricks-agents pydantic")

Step 2: Create Agent File

Create agent.py locally with ResponsesAgent pattern (see 3-genai-agents.md).

Step 3: Upload to Workspace

manage_workspace_files(
    action="upload",
    local_path="./my_agent",
    workspace_path="/Workspace/Users/you@company.com/my_agent"
)

Step 4: Test Agent

execute_code(
    file_path="./my_agent/test_agent.py",
    cluster_id="<cluster_id>"
)

Step 5: Log Model

execute_code(
    file_path="./my_agent/log_model.py",
    cluster_id="<cluster_id>"
)

Step 6: Deploy (Async via Job)

See 7-deployment.md for job-based deployment that doesn't timeout.

Step 7: Query Endpoint

manage_serving_endpoint(
    action="query",
    name="my-agent-endpoint",
    messages=[{"role": "user", "content": "Hello!"}]
)

---

Quick Start: Deploy a Classical ML Model

import mlflow
import mlflow.sklearn
from sklearn.linear_model import LogisticRegression

# Enable autolog with auto-registration
mlflow.sklearn.autolog(
    log_input_examples=True,
    registered_model_name="main.models.my_classifier"
)

# Train - model is logged and registered automatically
model = LogisticRegression()
model.fit(X_train, y_train)

Then deploy via UI or SDK. See 1-classical-ml.md.

---

MCP Tools

If MCP tools are not available, use the SDK/CLI examples in the reference files below.

Development & Testing

Tool	Purpose
manage_workspace_files (action="upload")	Upload agent files to workspace
execute_code	Install packages, test agent, log model

Deployment

Tool	Purpose
manage_jobs (action="create")	Create deployment job (one-time)
manage_job_runs (action="run_now")	Kick off deployment (async)
manage_job_runs (action="get")	Check deployment job status

manage_serving_endpoint - Querying

Action	Description	Required Params
get	Check endpoint status (READY/NOT_READY/NOT_FOUND)	name
list	List all endpoints	(none, optional limit)
query	Send requests to endpoint	name + one of: messages, inputs, dataframe_records

Example usage:

# Check endpoint status
manage_serving_endpoint(action="get", name="my-agent-endpoint")

# List all endpoints
manage_serving_endpoint(action="list")

# Query a chat/agent endpoint
manage_serving_endpoint(
    action="query",
    name="my-agent-endpoint",
    messages=[{"role": "user", "content": "Hello!"}],
    max_tokens=500
)

# Query a traditional ML endpoint
manage_serving_endpoint(
    action="query",
    name="sklearn-classifier",
    dataframe_records=[{"age": 25, "income": 50000, "credit_score": 720}]
)

---

Common Workflows

Check Endpoint Status After Deployment

manage_serving_endpoint(action="get", name="my-agent-endpoint")

Returns:

{
    "name": "my-agent-endpoint",
    "state": "READY",
    "served_entities": [...]
}

Query a Chat/Agent Endpoint

manage_serving_endpoint(
    action="query",
    name="my-agent-endpoint",
    messages=[
        {"role": "user", "content": "What is Databricks?"}
    ],
    max_tokens=500
)

Query a Traditional ML Endpoint

manage_serving_endpoint(
    action="query",
    name="sklearn-classifier",
    dataframe_records=[
        {"age": 25, "income": 50000, "credit_score": 720}
    ]
)

---

Common Issues

Issue	Solution
Invalid output format	Use self.create_text_output_item(text, id) - NOT raw dicts!
Endpoint NOT_READY	Deployment takes ~15 min. Use manage_serving_endpoint(action="get") to poll.
Package not found	Specify exact versions in pip_requirements when logging model
Tool timeout	Use job-based deployment, not synchronous calls
Auth error on endpoint	Ensure resources specified in log_model for auto passthrough
Model not found	Check Unity Catalog path: catalog.schema.model_name

Critical: ResponsesAgent Output Format

WRONG - raw dicts don't work:

return ResponsesAgentResponse(output=[{"role": "assistant", "content": "..."}])

CORRECT - use helper methods:

return ResponsesAgentResponse(
    output=[self.create_text_output_item(text="...", id="msg_1")]
)

Available helper methods:

• self.create_text_output_item(text, id) - text responses
• self.create_function_call_item(id, call_id, name, arguments) - tool calls
• self.create_function_call_output_item(call_id, output) - tool results

---

Related Skills

• databricks-agent-bricks - Pre-built agent tiles that deploy to model-serving endpoints
• databricks-vector-search - Create vector indexes used as retriever tools in agents
• databricks-genie - Genie Spaces can serve as agents in multi-agent setups
• databricks-mlflow-evaluation - Evaluate model and agent quality before deployment
• databricks-jobs - Job-based async deployment used for agent endpoints

Resources

• Model Serving Documentation
• MLflow 3 ResponsesAgent
• Agent Framework