Pavel Ivanov, Solutions Architect at DataArt, recently examined three "talk-to-data" services within Microsoft Fabric to understand their respective roles within the data stack. His findings reveal a straightforward picture: these tools aren't interchangeable, and choosing between them depends on your specific requirements. All three start working immediately – there's no weeks-long implementation process. But their capabilities, scope, and use cases differ. Understanding those differences is essential for informed design decisions.
Three Services, Three Approaches
Microsoft Fabric offers three distinct ways for users to query data using natural language. Q&A functions as a visual component embedded directly in reports, using natural language parsing rather than AI. Data Agent is an LLM-based service that connects multiple data sources and allows custom instructions. Copilot operates as an integrated chat experience that combines the capabilities of both, adding conversational memory and workspace-wide access. Each handles different complexity levels and serves different organizational needs.
Q&A: Direct Questions Within Your Report
Q&A operates as a visual component you embed directly into reports or dashboards. Despite its name, it isn't an LLM. Instead, it functions as a linguistic schema based natural language-to-DAX parser, converting text questions into queries against your existing semantic model.
What it does:
- Converts natural language questions to DAX queries against your semantic model
- Handles straightforward queries with aggregation, grouping, and filtering
- Responds with text or visuals that integrate seamlessly into your reports
- Processes each query independently with no conversational memory
What it doesn't do:
- Support nested queries or on-the-fly measure generation
- Work outside the scope of a single semantic model
- Remember the context between requests
Setup and deployment:
- Add the visual to your report, and it works immediately
- Optional configurations involve setting up synonyms and language models to give users multiple ways to phrase questions
- Available on Fabric Trial capacity, making it accessible for initial exploration
The core value: Q&A allows business users to ask simple questions that the report itself doesn't directly answer. For anything beyond straightforward queries, it reaches its ceiling quickly.
Data Agent: Complex Queries Across Multiple Sources
Data Agent is an LLM-based service published as a workspace item. Unlike Q&A, which is bound to a single semantic model, it supports conversational memory, connects to multiple data sources and allows you to guide its behavior through custom instructions.
What it does:
- Handles complex, multi-step, nested queries that Q&A cannot process
- Draws from up to five data sources simultaneously (Lakehouse, Warehouse, KQL Database, semantic models)
- Applies your domain-specific instructions to improve accuracy
- Responds in text, JSON, or grid format
What it doesn't do:
- Generate visual outputs or charts
- Work on Fabric Trial capacity
Setup and deployment:
- Create the agent, configure sources, select data assets—initial setup is quick
- Provide specific instructions on which data sources to use in different scenarios
- Supply examples of expected requests and responses to guide the LLM's behavior
- Requires at least F2 Fabric Capacity licensing
The core value: Data Agent handles sophisticated analytical queries across multiple systems while giving you control over how those systems are queried. When your questions require reasoning across multiple data sources, Data Agent is built for that.
Copilot: Integrated Experience with Conversational Context
Copilot is an LLM-based chat service already integrated into Fabric. From a "talk-to-data" perspective, it operates as an aggregator that can leverage Q&A and Data Agents behind the scenes while adding conversational memory and unique capabilities.
What it does:
- Maintains conversational context and remembers previous questions in the same session
- Works across datasets, reports, semantic models, data agents, and across workspaces
- Creates measures on the fly and explains query logic
- Responds with text and charts (though occasionally returns URLs instead of rendered images)
What it doesn't do:
- Allow fine-grained customization through instructions and examples, like Data Agent does
- Give you control over behavior configuration
- Work on Fabric Trial capacity
Setup and deployment:
- Zero setup required—already integrated into Fabric
- Widest scope of the three services with cross-workspace access
- Requires at least F2 Fabric Capacity licensing
The core value: Copilot removes friction. When you want users to explore data, ask follow-up questions, and view visualizations without worrying about which tool or data source to use, Copilot provides an integrated experience.
Comparison at a Glance
| Feature | Q&A | Data Agent | Copilot |
|---|---|---|---|
| Technology | NL-to-DAX parser | LLM-based | LLM-based |
| Setup complexity | Minimal | Quick | None |
| Data sources | Single semantic model | Up to 5 sources | Multiple sources, cross-workspace |
| Query complexity | Simple queries only | Complex, multi-step queries | Complex queries with context memory |
| Visualization support | Yes (visuals in report) | No | Yes (text + charts) |
| Customization | Synonyms, language models | Instructions, examples, domain logic | Limited configuration |
| Licensing | Fabric Trial or higher | F2 Capacity minimum | F2 Capacity minimum |
| Conversational memory | No | Yes | Yes |
Tool Selection: A Clear Framework
The choice between these three depends on your use case:
Q&A fits when business users need to ask simple, direct questions within the context of a specific report. It requires minimal setup and works within the Fabric Trial, making it ideal for teams just beginning to explore natural language query capabilities. Use it when the questions users need answered are straightforward and stay within the boundaries of a single semantic model.
The Data Agent applies when you need to support complex queries across multiple data sources, with specific instructions guiding which sources to use in each case. It's the right choice when your organization has sophisticated analytical needs that require reasoning across multiple systems and when you want to maintain control over how those systems are queried. The tradeoff is no visual output and the F2 Capacity requirement.
Copilot serves when you want a broad, integrated experience across your workspace. Users benefit from conversational context, cross-workspace access, and capabilities like measure creation and query explanation. Choose Copilot when you prioritize ease of use and discovery over fine-grained control, and when your team needs to work with data across multiple reports and workspaces in a single session.
The Reality Behind the Results
All three tools share a common trait: they begin working immediately. There's no weeks-long implementation process. You can start using each within minutes of deciding to deploy it. Quality, however, depends entirely on the underlying data models. A well-designed semantic model makes these services genuinely helpful. A poorly structured one renders all three unreliable, regardless of their features.
LLMs generate incorrect responses. Copilot includes a necessary disclaimer that AI-generated content may be inaccurate. This isn't a flaw unique to Fabric. It's the nature of technology. Organizations need to accept this constraint and design workflows that take it into account.
Conclusion
The path forward depends on your starting point. Teams exploring natural language query capabilities should begin with Q&A. Organizations ready to integrate data from multiple sources should evaluate Data Agent. Those seeking an integrated discovery experience should pilot Copilot. And most organizations will likely benefit from deploying all three, each filling its specific role within the data architecture.
Want to explore these capabilities in your organization?
Visit our Microsoft Fabric Services page to discuss how Q&A, Data Agent, and Copilot can work within your data architecture.
