February 15th, 2026
The 11 Best TextQL Competitors and Alternatives in 2026
By Zach Perkel · 36 min read
The 11 best TextQL competitors: At a glance
PromptQL translates natural language into query plans for accurate data analysis. However, teams often look for alternatives with faster setup, simpler workflows, or different approaches to connecting data.
Here's a quick comparison of the top 11 alternatives to help you find the best option for your business:
Tool | Best For | Starting price | Key advantage over PromptQL |
|---|---|---|---|
Business teams asking data questions in plain English | Natural language interface with no setup required | ||
Users who want fast SQL from plain English prompts | Focuses strictly on writing SQL rather than running or visualizing analysis | ||
Individuals who need AI-assisted SQL writing | Focuses on drafting and improving SQL instead of full analysis workflows | ||
Teams querying warehouse data conversationally | $2,750/month, billed quarterly | Acts as a conversational data assistant rather than a standalone SQL generator | |
Enterprises querying data directly inside the Databricks platform | DBU-based pricing in SQL Pro & Serverless plans | Embedded within the Databricks Lakehouse rather than operating as a standalone SQL tool | |
Enterprises with strong data governance needs | Embedded within a data catalog rather than operating as a standalone query tool | ||
Teams mixing business users with analysts | $36/editor/month, billed monthly | Combines natural language prompts with manual SQL control | |
Engineering teams with defined data models | Generates SQL and GraphQL on top of existing schemas | ||
Quick file uploads and ad-hoc questions | Focuses on speed over structured workflows | ||
Analysts who work in collaborative notebooks | Assists with SQL inside notebook environments | ||
Testing queries before buying dedicated tools | Works for small datasets but lacks production features |
Why I looked for TextQL alternatives
TextQL delivers on its core promise of turning natural language into SQL queries, but after testing it in everyday business workflows, I noticed where it starts to feel incomplete.
For business users, getting the SQL query is only half the job. For technical teams, the generated queries sometimes need refinement, but TextQL doesn't offer much room to iterate or save versions for reuse.
Here are the three main reasons teams look for TextQL competitors:
Incomplete workflows: Getting SQL is useful, but most business users need the query run, visualized, and turned into a report without extra steps.
Limited iteration: Technical users often want to refine generated queries, save variations, or build on previous work. TextQL doesn't support that kind of iteration well.
Usage-based pricing: TextQL’s costs scale with how often teams explore data, which adds up quickly when multiple users run queries throughout the week.
Tip: Teams that need heavier cleaning or transformation may want to look at the best data preparation tools instead of query-focused platforms.
1. Julius: Best for business users who want insights without SQL
We designed Julius to help you get answers from your data without writing SQL. You can upload a CSV or Excel file, or connect directly to databases like PostgreSQL, Snowflake, and BigQuery.
Ask a question in natural language, and Julius returns charts and explanations rather than just raw query output. This works well when you want to check revenue trends or understand customer patterns without moving between multiple tools.
Each result opens in a Notebook that shows how Julius found your answer. You can review what it did, change filters, and set up automatic updates that send fresh charts to your inbox or Slack. This lets you reuse or adjust earlier work when new data comes in.
Julius learns your database structure as you use it, mapping how tables connect and what each column represents. When you ask a new question, it applies that context to generate better queries. As a result, answers get more accurate the more you use it, and you spend less time explaining your database setup.
You can view results as charts or open the code to see exactly how Julius built the answer. Both options work the same way, so you don’t need to set up a full notebook environment just to explore your data.
Why it beats TextQL
No setup or coding required: Business users get charts from simple questions without learning SQL or setting up notebooks.
Remembers your data structure: Julius learns your table connections and what each column means, so follow-up questions work better over time.
Automated recurring reports: Set up weekly or monthly reports that run on their own and send results to Slack or email.
Built for natural language: Designed specifically for asking questions in plain English, not adapted from existing notebook tools.
Direct data connections: Links to Postgres, Snowflake, BigQuery, Google Ads, Google Drive, Stripe, and Intercom without extra plugins.
Pros
Creates charts quickly from natural language questions
Easy to share results with clean visuals
Notebook format keeps your analysis steps organized
Cons
Not built for advanced statistical work or custom algorithms
Handles complex database joins less well than pure SQL tools
Pricing
Bottom line
2. AI2SQL: Best for users who want fast SQL from plain English prompts
AI2SQL is a lightweight text-to-SQL tool built for quickly turning natural language questions into working SQL queries. It focuses strictly on drafting SQL for use in your existing database or BI tool, without adding dashboards or reporting layers.
I tested AI2SQL by entering common business questions like revenue by month and customer counts by segment. It returned structured SQL statements that were ready to run in a database editor. The experience was fast and direct, which works well when you already know where the query will be executed.
Because AI2SQL stops at query generation, it doesn’t attempt to run analysis, create charts, or manage recurring workflows. You’re responsible for executing the SQL and handling visualization elsewhere.
Why it beats TextQL
Focused query generation: Designed specifically for converting plain English into SQL without adding extra analytics layers.
Minimal interface: Simple input and output flow for quickly drafting queries.
Low-cost entry point: Affordable option for individuals who primarily need SQL assistance.
Pros
Fast SQL generation from natural language
Simple interface with no workflow setup
Useful for drafting and refining queries
Cons
Doesn’t run queries or build visualizations
No built-in reporting or workflow management
Limited collaboration features
Pricing
Bottom line
3. SQLAI.ai: Best for individuals who need AI-assisted SQL writing
SQLAI.ai is a text-to-SQL tool designed to help users draft, explain, and optimize SQL queries using natural language prompts. It focuses on assisting with query writing rather than operating as a full analytics platform.
I asked SQLAI.ai to explain and optimize existing SQL queries, including nested joins and multi-condition filters. It generated revised versions with clearer structure and suggested changes to improve readability and efficiency. The experience felt more like a writing assistant for SQL than a full analytics environment.
SQLAI.ai centers on drafting and improving queries, so it doesn’t run analysis, create charts, or manage recurring workflows. You still execute queries and handle visualization inside your own database or BI environment.
Why it beats TextQL
SQL drafting assistance: Helps generate SQL from plain English prompts.
Query improvement tools: Can explain, rewrite, and optimize existing SQL statements.
Lightweight interface: Built specifically for writing and refining queries without additional analytics layers.
Pros
Generates SQL quickly from natural language
Can improve and explain existing SQL queries
Simple interface with minimal setup
Cons
Doesn’t execute queries or build visualizations
No built-in dashboards or recurring workflows
Limited collaboration features
Pricing
Bottom line
4. DataGPT: Best for teams querying warehouse data conversationally
DataGPT is an AI data assistant built to connect to business data and answer questions in natural language. It positions itself as a conversational layer over your existing data stack rather than a standalone SQL drafting tool.
I explored order and customer data in a demo workspace and asked questions about revenue trends and customer segments. The assistant generated queries behind the scenes and returned results directly in the interface, along with charts. I could continue asking related questions without rewriting each prompt, making the experience feel like a guided conversation rather than a series of separate queries.
DataGPT uses a chat-first interface with built-in visuals, so you move from one question to the next inside a single flow. It works well for interactive exploration but isn’t built for structured reporting workflows or data pipelines.
Why it beats TextQL
Conversational interface: Supports multi-step questioning without manually refining SQL each time.
Warehouse connectivity: Designed to sit directly on top of existing warehouse data.
Context retention: Maintains conversational context across related follow-up questions.
Pros
Natural language querying over connected warehouse data
Supports conversational follow-up questions
No manual SQL editing required for basic exploration
Cons
Less structured than notebook-based workflows
Limited control over manual SQL refinement
Not built for complex reporting pipelines
Pricing
Bottom line
5. Databricks Genie: Best for enterprises querying data inside the Databricks platform
Databricks Genie is an AI-powered assistant built directly into the Databricks Lakehouse. It allows teams to ask questions in natural language and generates queries against data stored in Databricks, returning results within the platform.
I asked Genie about revenue trends and operational metrics across structured warehouse tables. Then, I followed up with more specific questions to drill into the results. It translated my questions into SQL and returned results directly inside the Databricks SQL interface.
Because Genie runs within the Lakehouse, it operates under the existing Databricks permissions and governance controls. That makes it a strong fit for teams already committed to the Databricks ecosystem, but less relevant if your data lives elsewhere.
Why it beats TextQL
Native Lakehouse integration: Built directly into the Databricks environment rather than connecting externally.
Enterprise governance alignment: Respects existing permissions and access controls inside Databricks.
Natural language interface: Converts plain English questions into queries without requiring manual SQL editing.
Pros
Works directly within the Databricks platform
Aligned with existing governance and permissions
Supports natural language querying over Lakehouse data
Cons
Limited to the Databricks ecosystem
Requires Databricks SQL access
Not designed as a standalone analytics platform
Pricing
Bottom line
6. Atlan AI: Best for enterprises with strong data governance needs
Atlan AI is built into Atlan’s data catalog and governance platform. Instead of acting as a standalone text-to-SQL tool, it layers natural language querying on top of documented datasets, lineage, and access controls.
I used Atlan’s AI assistant inside a governed data catalog to search for specific datasets. Before querying metrics, I reviewed definitions, ownership, and lineage. It showed related tables and documentation next to the results, which made it clear that this is built for data discovery, not just writing SQL.
Because Atlan AI sits inside a catalog and governance framework, it emphasizes context and lineage as much as query output. That makes it well-suited for organizations where data ownership, permissions, and documentation are already formalized.
Why it beats TextQL
Catalog-native AI: Embedded inside a data discovery and governance platform rather than operating independently.
Metadata awareness: Surfaces dataset definitions, ownership, and lineage alongside query results.
Permission alignment: Operates within existing access controls and governance policies.
Pros
Built on top of a structured data catalog
Strong alignment with governance and metadata workflows
Provides contextual information beyond raw query results
Cons
Requires an existing Atlan deployment
Less lightweight than standalone SQL tools
Not designed as a simple query drafting assistant
Pricing
Bottom line
7. Hex: Best for teams mixing business users with analysts
Hex is a data workspace that combines natural language prompts, SQL editing, and Python notebooks in one interface. Business users can ask questions in plain English to generate charts, while analysts can open the underlying queries to refine logic or add custom code.
I tested Hex by building dashboards that required both quick exploration and repeatable analysis. Business users on my team could type questions and get visualizations immediately, while I could click into the SQL to adjust joins or add filters. The dual interface worked well when non-technical stakeholders needed fast answers, but analysts wanted control over accuracy.
Hex’s built-in version control is one of its most practical features. Every change to a notebook gets saved automatically, so you can roll back to earlier versions or see who modified what. This helps when multiple people work on the same analysis and need to see how edits changed the results.
Why it beats TextQL
Hybrid interface: Business users ask questions in plain English, while analysts can edit the generated SQL directly for precision.
Built-in version control: Tracks every change to notebooks automatically so teams can review edits or restore earlier versions without external tools.
Collaborative workspace: Multiple users can work on the same analysis simultaneously, with real-time updates visible to everyone.
Pros
Supports both natural language prompts and manual SQL editing
Version control built into every notebook
Real-time collaboration across technical skill levels
Cons
Steeper learning curve for business users compared to chat-only tools
Higher cost per editor compared to simpler alternatives
Pricing
Bottom line
8. PromptQL: Best for engineering teams with defined data models
PromptQL is a text-to-SQL engine that runs on top of existing database schemas and generates both SQL and GraphQL queries from natural language. Engineering teams implement it as infrastructure that other applications can call, rather than as a standalone interface that business users open directly.
I connected PromptQL to a PostgreSQL database that already had tables, relationships, and user permissions configured. The queries it generated matched my database structure without needing extra setup. It followed the access rules I had already defined, so users only saw data they were supposed to see based on their permissions.
PromptQL performs best in environments where data models are well-documented and governance rules already exist. Business users don't typically interact with PromptQL directly. Instead, developers integrate it into internal tools or dashboards so non-technical teams can ask questions without seeing the SQL layer.
Why it beats TextQL
Infrastructure-first design: Developers implement PromptQL as a query generation layer that internal apps can call, not as a standalone tool that users log into.
GraphQL support: Generates both SQL and GraphQL queries, giving engineering teams flexibility across different API architectures.
Respects existing governance: Works within pre-defined permissions and data access rules without requiring separate security configuration.
Pros
Works on top of existing database schemas and permissions
Supports both SQL and GraphQL query generation
Strong fit for engineering-led data infrastructure
Cons
Requires technical implementation and ongoing developer support
Business users can't access it directly without custom integration work
Pricing
Bottom line
9. Powerdrill Chat: Best for quick file uploads and ad-hoc questions
Powerdrill Chat is a lightweight data analysis tool that lets you upload files or connect data sources and start asking questions immediately. You can drag in a CSV, Excel file, or PDF and ask questions in plain English without any setup steps.
I tested Powerdrill by uploading sales data and asking basic questions about revenue trends and customer counts. The tool returned tables and charts fast, making it useful for one-off analysis when you need a quick answer. The interface stays minimal, so there's little learning curve or configuration required before you start exploring.
Powerdrill works well for fast exploration but doesn't support scheduled reports or repeatable workflows. Each session starts fresh, so if you need to run the same analysis weekly or save query templates for later, you'll need to rebuild your questions from scratch.
Why it beats TextQL
Instant file analysis: Upload a CSV or Excel file and start asking questions immediately without connecting to databases or configuring schemas.
Minimal setup required: No account configuration, data modeling, or permission setup needed to get started.
Fast response times: Returns tables and charts within seconds for straightforward questions on smaller datasets.
Pros
Very fast setup with drag-and-drop file uploads
Clean, simple interface with no learning curve
Low cost for occasional use
Cons
No support for scheduled reports or recurring workflows
Limited ability to save and reuse analysis templates
Pricing
Bottom line
10. Deepnote: Best for analysts who work in collaborative notebooks
Deepnote is a cloud-based notebook platform that uses AI to help you write SQL and Python code inside collaborative workspaces. Multiple team members can work in the same notebook simultaneously, seeing each other's changes in real time.
I used Deepnote to compare how its AI assistance worked against writing queries from scratch. As I typed queries, it suggested the next part of the code, which made writing database joins and filters faster. The notebook setup let me edit the code directly and add custom math or logic that simpler chat tools can't do.
Deepnote works best when your team already uses notebooks for analysis. The AI helps reduce manual query writing, but you still need to understand SQL basics to review and adjust what it generates. Business users without coding experience will find the notebook interface more complex than chat-based tools.Why it beats TextQL
Full notebook environment: Combines SQL, Python, and visualizations in one workspace instead of just generating queries.
Real-time collaboration: Multiple analysts can edit the same notebook simultaneously, with changes visible to everyone instantly.
AI-assisted coding: Suggests SQL completions and Python code as you type to speed up analysis without requiring perfect syntax knowledge.
Pros
Supports both SQL and Python in the same workspace
Real-time collaborative editing across team members
AI suggestions help write queries faster
Cons
Requires basic SQL and Python knowledge to use effectively
More complex interface compared to chat-only tools
Pricing
Bottom line
11. ChatGPT: Best for testing queries before buying dedicated tools
ChatGPT is a general-purpose AI assistant that can generate SQL queries, explain database concepts, and help you understand data analysis logic. Most business users try ChatGPT first before looking at specialized tools because it's familiar and accessible.
I tested ChatGPT by pasting sample data and describing my database structure. It wrote working SQL for simple tasks like calculating totals or filtering records, and explained what each query was doing in plain English. This helped me understand the syntax without needing a SQL tutorial.
ChatGPT isn't as effective once you move beyond small examples, and it doesn’t connect to live databases by default. To query live data, you need an integration or a custom setup.
It also struggles with real-world business datasets unless you use its data analysis tools, and even then, you’ll hit file and context limits. New chats often require restating context, which makes repeated analysis harder. This makes it useful for learning or testing ideas, but impractical for ongoing analysis work.
Why it beats TextQL
No learning curve: Most business users already know how to use ChatGPT and can start asking data questions immediately
Explains the logic: Breaks down what each query does in plain English so you can learn SQL concepts while getting answers
Free tier available: Lets you test query generation and data concepts without paying for a subscription
Pros
Familiar interface that most users already know
Explains SQL logic in simple terms
Free tier works for basic query generation
Cons
Doesn’t connect to live databases by default
New chats require restating context
Not reliable for large or ongoing business datasets
Pricing
Bottom line
How I tested these TextQL competitors
I ran the same set of business questions through each tool to see how they handled real analysis work. My goal was to understand which tools actually deliver answers business users can act on, not just generate SQL that still needs work.
Here's what I focused on during testing:
Query accuracy: I checked whether the generated SQL matched the question I asked and returned correct results without needing manual fixes.
Result format: I tracked whether tools stopped at SQL generation or continued through to charts, tables, and reports that business users could share.
Learning curve: I measured how long it took to go from signup to getting a useful answer, including any setup, configuration, or training required.
Repeatability: I tested whether I could save workflows, schedule recurring analysis, or reuse queries without rebuilding context each time.
Data handling: I pushed each tool with different file sizes and database types to see where performance dropped or features stopped working.
How to choose your TextQL competitor
Choosing a TextQL competitor depends on whether you need fast answers for business users or structured workflows for technical teams. Some tools hide the SQL completely to speed up exploration, while others expose queries so analysts can refine logic and build repeatable processes.
Choose:
Julius if business users need charts and insights from plain English questions without learning SQL or setting up notebooks.
AI2SQL if you primarily need fast SQL generation that you’ll run inside your own database or BI tool.
SQLAI.ai if you want help explaining, rewriting, or optimizing existing SQL queries.
DataGPT if your team prefers conversational querying over warehouse data without manually editing SQL.
Databricks Genie if your organization already operates inside Databricks and wants AI querying built directly into the Lakehouse.
Atlan AI if data governance, lineage, and catalog context matter as much as the query results.
Hex if your team mixes business users who ask questions with analysts who need to review and edit the underlying queries.
PromptQL if developers are embedding text-to-SQL functionality into internal applications rather than using a standalone interface.
Powerdrill Chat if you need quick answers from uploaded files without setup or long-term workflow requirements.
Deepnote if your team already works in notebooks and wants AI assistance with SQL and Python code.
ChatGPT if you only need occasional SQL help (drafting, debugging, or rewriting queries) and you’re okay copying results into your database or BI tool to actually run them.
My final verdict
TextQL delivers on text-to-SQL generation, but many teams need more than just queries. I found that Hex and Deepnote work well when analysts need to refine code manually. DataGPT and Databricks Genie focus on conversational querying inside warehouse environments, while Powerdrill and ChatGPT handle quick exploration but stop short of repeatable workflows.
Julius takes a different approach by hiding the SQL layer entirely. You ask questions in plain English and get charts without needing to review or run queries yourself. I've found this works better for business users who want answers without learning syntax or waiting on analysts to translate their questions into code.
Try Julius if you want a simpler alternative to TextQL
The biggest difference between TextQL competitors often comes down to whether you get just SQL or complete analyses. Julius skips the query step entirely by letting you ask questions in plain English and returning charts you can use immediately.
Here’s how Julius helps:
Direct connections: Link databases like PostgreSQL, Snowflake, and BigQuery, or integrate with Google Ads and other business tools. You can also upload CSV or Excel files. Your analysis can reflect live data, so you’re less likely to rely on outdated spreadsheets.
Smarter over time with the Learning Sub Agent: Julius's Learning Sub Agent learns your database structure, table relationships, and column meanings as you use it. With each query on connected data, it gets better at finding the right information and delivering more accurate answers without manual configuration.
Quick single-metric checks: Ask for an average, spread, or distribution, and Julius shows you the numbers with an easy-to-read chart.
Built-in visualization: Get histograms, box plots, and bar charts on the spot instead of jumping into another tool to build them.
Catch outliers early: Julius highlights suspicious values and metrics that throw off your results, so you can make confident business decisions based on clean and trustworthy data.
Recurring summaries: Schedule analyses like weekly revenue or delivery time at the 95th percentile and receive them automatically by email or Slack.
One-click sharing: Turn a thread of analysis into a PDF report you can pass along without extra formatting.
Ready to get answers from your data without writing SQL? Try Julius for free today.
