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Picture this: You open Databricks Marketplace, ready to pick a model for your new AI project. You're greeted with Meta Llama 3.3, OpenAI GPT OSS, Google Gemini, Qwen3-Next, and about ten other options staring back at you.
Which one do you choose?
If you're like most ML engineers I've talked to, you probably do one of three things:
Pick the one with the biggest parameter count (because bigger = better, right?)
Choose whatever's trending on tech Twitter this week
Spend three days testing everything and still feel uncertain
Here's what nobody tells you: the "best" model doesn't exist. What exists is the right model for your specific use case.
Last month, I watched a team deploy a 70B parameter model for a simple customer FAQ bot. Overkill? Absolutely. They switched to a lightweight 20B model and cut costs by 60% with zero drop in quality. On the flip side, I've seen teams try to squeeze complex reasoning tasks out of models that were never designed for it, then wonder why their accuracy tanked.
The Databricks Marketplace has evolved into a full AI ecosystem with foundation models, AI agents, MCP servers, and solution accelerators. It's genuinely powerful—but only if you know how to navigate it.
So let's skip the guesswork. Whether you're building a conversational agent, deploying a text generation system, or just trying to figure out which model won't blow your budget, this guide breaks down the exact criteria that actually matter.
Databricks Marketplace isn't just about foundation models anymore. It now offers:
Foundation Models: Pre-trained LLMs like Meta Llama, OpenAI GPT, Google Gemini, and specialized models from Alibaba Cloud
AI Agents and MCP Servers: Tools that connect AI systems to external data sources and APIs
Solution Accelerators: Pre-built notebooks demonstrating specific use cases
Datasets: Both public and commercial data products
The marketplace supports models through different deployment options: pay-per-token endpoints for quick experimentation, provisioned throughput for production workloads, and external model integrations for third-party services.
Before we dive deep into selection criteria, here's a practical table to give you a starting point. Think of this as your "cheat sheet" for matching common use cases to appropriate models:
Use Case | Recommended Models | Why This Model? | Key Considerations |
Conversational AI Agents |
| Strong instruction-following, function calling support, maintains context across multi-turn conversations | Need function calling? Choose models with tool integration support |
Text Generation & Content Creation |
| Chain-of-thought reasoning, creative outputs, long-form coherence | Consider context window size (128K tokens) for longer documents |
Real-Time Copilots & Assistants |
| Lightweight, fast response times, cost-efficient for high throughput | Latency-sensitive? Prioritize Flash models |
Complex Reasoning & Analysis |
| Adjustable reasoning effort, step-by-step problem solving, mathematical accuracy | Best for tasks requiring transparent logic |
Multimodal Applications |
| Processes both text and images, unified understanding across modalities | Need vision? Only select models with image support |
Document Analysis & RAG |
| Ultra-long context windows (up to 128K), retrieval-augmented generation optimized | Match context length to your document size |
Batch Processing & Inference |
| Optimized for AI Functions, handles high-volume batch jobs efficiently | Volume matters—use AI Functions for scale |
Enterprise Compliance Workflows | Models with HIPAA support (Provisioned throughput mode) | Security certifications, data residency controls, fine-grained governance | Healthcare/Finance? Compliance is non-negotiable |
Prototyping & Experimentation | Any pay-per-token model | Low upfront cost, no infrastructure setup, easy to swap models | Start here, then optimize for production |
How to Use This Table:
Find your primary use case in the left column
Start with the recommended models
Read the "Why" to understand the match
Check "Key Considerations" for deal-breakers
Then dive into the detailed criteria below to refine your choice
Now let's break down exactly how to evaluate these models for your specific requirements.
Not all models are created equal. Start by identifying your primary use case:
For Conversational AI Agents: Look for models with strong instruction-following capabilities and function-calling support. Models like Meta Llama 3.3 70B Instruct and Qwen3-Next 80B excel at multi-step workflows and maintaining context across conversations. These models support tool integration, allowing your agent to query databases, execute code, or call external APIs.
For Text Generation and Content Creation: Models optimized for creativity and coherent long-form content work best here. OpenAI GPT OSS 120B brings chain-of-thought reasoning with adjustable effort levels, while Google's Gemini 2.5 Pro handles multimodal inputs and offers both quality and speed.
For Lightweight, Real-Time Applications: If you're building copilots or need rapid batch inference, consider lightweight models like OpenAI GPT OSS 20B or Gemini 2.5 Flash. These models balance performance with cost-efficiency, making them ideal for high-throughput scenarios.
The most powerful model isn't always the right choice. Consider these factors:
Token Pricing: Pay-per-token endpoints charge based on usage, making them perfect for experimentation. For instance, you might start with a larger model during development, then optimize down to a smaller model for production after establishing performance baselines.
Provisioned Throughput: Production workloads requiring consistent performance need dedicated capacity. This option provides performance guarantees and supports fine-tuned models with HIPAA compliance for sensitive applications.
Context Window Requirements: Models like Qwen3-Next 80B offer ultra-long context windows (128K tokens), essential for analyzing lengthy documents or maintaining extensive conversation history. If your use case doesn't require this, a model with a shorter context window might save costs.
Databricks AI Playground offers a no-code environment to compare models directly. Here's how to use it effectively:
Run Identical Prompts: Test 2-3 models with the same prompts to compare response quality, tone, and accuracy
Enable Tools: For agent use cases, add Unity Catalog functions or vector search indices to see how models handle tool integration
Iterate on Prompts: Different models respond differently to prompt engineering—what works for one might fail for another
The synchronized comparison feature lets you evaluate responses side-by-side, making quality differences immediately apparent.
Some use cases demand specialized capabilities:
For Multimodal Applications: Models like Gemini 3 12B support both text and image inputs, essential for applications that need to process screenshots, diagrams, or product photos alongside text.
For Reasoning Tasks: Models with chain-of-thought capabilities (like GPT OSS 120B) excel at complex problem-solving, mathematical calculations, and logical inference. These models break down problems step-by-step, making their reasoning transparent.
For Enterprise Integration: If you're working within strict data governance frameworks, prioritize models that support Delta Sharing, Unity Catalog integration, and compliance certifications. Databricks-hosted models with provisioned throughput offer HIPAA compliance for healthcare applications.
Here's a game-changer: instead of manually testing dozens of models, use Agent Bricks to automate the selection process. This feature:
Auto-generates Evaluation Benchmarks: Creates domain-specific tests based on your data and task description
Optimizes Cost vs. Quality: Automatically tests different models, prompts, and configurations to find the sweet spot
Provides Continuous Improvement: Incorporates feedback to refine model selection over time
Agent Bricks is particularly valuable when you're not sure which model to start with—it eliminates the guesswork by testing multiple options programmatically.
Let's walk through a real-world example. Suppose you're building a customer support agent that needs to:
Answer questions using internal documentation
Escalate complex issues to human agents
Track conversation context across sessions
Step 1: Define Requirements
Need: Strong instruction-following, function calling, RAG support
Volume: 10,000 queries/day
Budget: Moderate
Latency: < 2 seconds
Step 2: Shortlist Models Based on requirements:
Meta Llama 3.3 70B Instruct (balanced performance)
Qwen3-Next 80B (excellent at multi-step workflows)
Gemini 2.5 Flash (cost-effective, fast)
Step 3: Test in AI Playground Run 20-30 representative queries through each model, focusing on:
Accuracy of document retrieval
Quality of generated responses
Ability to recognize escalation triggers
Step 4: Optimize with Agent Bricks Let Agent Bricks fine-tune the winning model with your actual data, automatically handling prompt optimization and evaluation.
Step 5: Deploy with Monitoring Start with provisioned throughput for consistency, implement logging to track performance, and gather user feedback for continuous refinement.
Choosing Based on Size Alone: A 70B parameter model isn't automatically better than a 20B model for your specific task. Smaller, specialized models often outperform larger general-purpose ones.
Ignoring Model Updates: Databricks frequently adds new models and retires older ones. For example, Meta Llama 3.3 70B recently replaced Llama 3.1 70B. Stay informed about deprecations and recommended migrations.
Skipping the Playground: It's tempting to deploy based on benchmarks alone, but 5-10 minutes in AI Playground can save hours of production debugging.
Overlooking Licensing: Each model has its own license and acceptable use policy. Review these carefully, especially for commercial applications.
When selecting models from Databricks Marketplace, remember:
Match the task first: Agent work, text generation, and batch inference each have ideal model profiles
Balance cost and performance: The most expensive model isn't always the most effective
Test before deploying: AI Playground makes side-by-side comparison effortless
Leverage automation: Agent Bricks handles optimization better than manual tuning
Stay informed: Model availability changes—subscribe to Databricks updates
The right model depends on your specific combination of requirements, budget, and technical constraints. Start broad, test thoroughly, and let your actual data guide the final decision. With Databricks Marketplace offering everything from lightweight copilot models to heavyweight reasoning engines, there's a perfect fit for virtually every AI use case—you just need to know where to look.
Ready to explore models in Databricks Marketplace? Start with AI Playground to compare models risk-free, or dive into Agent Bricks for automated optimization tailored to your specific needs.
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