Visual Prompt Management
RAG (Retrieval-Augmented Generation) Pipeline
Enterprise LLMOps
BaaS (Backend as a Service) Solution
LLM Agents
AI Workflow Orchestration
Multi-LLM Support
PlasticDB, AI Query, Seek AI, Vanna.AI, SupaSQL, EverSQL, SQLBuilder.ai, SQL CREATOR, SQLtroughAI, Ask Your App are the best paid / free ai sql query builder tools.






AI SQL query builders are tools that use artificial intelligence to generate SQL queries based on user input or requirements. These tools aim to simplify the process of writing complex SQL queries by automating the generation of SQL code, making it easier for users to interact with databases without extensive SQL knowledge.
Core Features
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Price
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How to use
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|---|---|---|---|
Dify.AI | Visual Prompt Management |
Sandbox Free Free Trial of Core Capabilities, 200 messages, Support OpenAI/Anthropic/Llama2/Azure OpenAI/Hugging Face/Replicate, 1 Team Workspace, 1 Team Member, 5 Apps, 50 Knowledge Documents, 50MB Knowledge Data Storage, 10/min Knowledge Request Rate Limit, 5,000/day API Rate Limit, Standard Document Processing, 10 Annotation Quota Limits, 30 Days Log History
| Use Dify.AI to visually design AI apps in an all-in-one workspace, fortify apps with reliable data pipelines using the RAG pipeline, empower prompt design and testing with the Prompt IDE, monitor and refine model reasoning with Enterprise LLMOps, integrate AI into products with the BaaS solution, create custom LLM Agents, and orchestrate AI workflows. |
Hex | Notebooks for analysis and modeling |
Community Free Free to explore data and showcase your work.
| Connect Hex to your data warehouse, analyze data using SQL, Python, or R, and build interactive apps with a drag-and-drop UI builder. Share your work with teams and stakeholders. |
Formula Bot | AI-powered formula generation |
Free forever $0/month Includes 10 chat messages / month, 15 enrichments / month, 5MB file upload limit, Access all data sources, external data & utilities, 2 uploaded files / chat
| Users can connect their data, type questions in plain English, and Formula Bot's AI will generate insights, charts, tables, and formulas. It supports uploading data, chatting with the data to convert text into various formats, and automating tasks with enrichments. |
GPTExcel | Generate Spreadsheet Formulas |
Free $0/month Formula Generator for Excel and Sheets. Formula Explainer for Excel and Sheets. Regex Generator. Script Generator not included. SQL Generator not included. 4 requests per day. Limited access to customer support.
| Users can input their requirements for a formula, script, or query, and GPTExcel's AI will generate the corresponding code. The tool also explains existing formulas to enhance understanding. |
OSS Insight | Data Explorer: GPT-powered querying tool for GitHub data | Use the Data Explorer to ask questions in natural language to generate SQL, query the data, and visualize the results. Explore trending repositories and hot collections for insights into technical fields. You can also explore events happening on GitHub in real-time. | |
TiDB Cloud | Fully managed TiDB service | Sign up for a TiDB Cloud account, link your AWS account, and deploy a cluster on Amazon Web Services or Google Cloud through the intuitive console. Create and connect to a database in minutes without managing software. | |
AI2sql | Natural Language to SQL Conversion |
Starter $9 /mo 100 SQL queries/mo, 10 Tables Limit, Free for 7 days, cancel anytime
| Users can input natural language questions or descriptions, and AI2sql automatically generates the corresponding SQL query. The platform also offers tools to explain existing SQL queries, optimize them for performance, format them for readability, and validate their syntax. |
Vanna.AI | AI-powered SQL generation from natural language | You can use Vanna.AI by asking questions about your database in natural language. Vanna will then generate the SQL query for you. It can be integrated into Jupyter Notebooks, Slackbots, web apps, Streamlit apps, and other frontends. | |
Latitude | Prompt manager | Design prompts using the prompt manager, evaluate and refine them using real data, and deploy new changes easily. Use the SDK or gateway for integration. | |
Basedash | AI-powered data querying and visualization |
Small $80/mo 3 org members, 30+ data connections, 10 dashboards, 100 charts, Unlimited AI chats, 1m monthly active rows
| Connect Basedash to your database or warehouse. Then, use natural language to generate charts, dashboards, and query your data. The AI will clarify and help you get the data you need. |

AI Search Engine
Large Language Models (LLMs)
AI Chatbot
AI Developer Tools
AI For Data Analytics
Business intelligence and data analytics
Ad-hoc reporting and data exploration
Data-driven decision making
Integration with data visualization tools and dashboards
Users generally appreciate the time-saving and accessibility benefits of AI SQL query builders. They highlight the ease of use and the ability to generate queries without extensive SQL knowledge. However, some users note that the generated queries may not always be as optimized as manually written queries, and complex queries may require some manual intervention. Overall, AI SQL query builders are seen as valuable tools for streamlining data retrieval and analysis processes.
A marketing analyst uses an AI SQL query builder to quickly generate reports on customer demographics and purchasing behavior, without having to write complex SQL queries.
A business owner uses an AI SQL query builder to retrieve sales data and generate visualizations, enabling them to make data-driven decisions without relying on technical staff.
To use an AI SQL query builder, users typically input their requirements or questions in natural language. The AI system then processes this input, identifies the relevant tables and columns in the database, and generates an optimized SQL query to retrieve the desired information. Users can then review, modify, and execute the generated SQL query.
Reduced time and effort in writing complex SQL queries
Improved accessibility for users with limited SQL knowledge
Increased productivity and efficiency in data retrieval and analysis
Reduced risk of errors and inconsistencies in SQL queries







































