Chat With Data vs New API
Side-by-side comparison · Updated October 2026
| Description | Chat With Data, also known as DataChat, is a no-code generative AI analytics platform designed for business users and domain experts to extract insights from data seamlessly. By using plain English queries instead of complex programming languages, it eliminates the need for technical skills, allowing easy access to data analysis. Key features include its intuitive no-code interface, transparency in analytical steps, and iterative analytics process. It integrates with platforms like Google BigQuery and HubSpot for varied data handling and supports industries like finance, sales, marketing, and customer service in data-driven decision-making. | New API is a self-hosted AI gateway and model-management project maintained in the QuantumNous repository. It brings provider channels, access tokens, model restrictions, usage statistics and cost accounting into one interface. Teams supply their own authorized model-provider access and operate the gateway in their chosen environment. The gateway supports several API formats, including OpenAI-compatible requests, Claude Messages and Google Gemini. Compatibility has boundaries: the README marks Gemini-to-OpenAI conversion as text-only without function calling, and OpenAI-compatible to Responses conversion as in development. Test the exact endpoints, streaming behavior and tool calls your application uses before switching traffic. Routing features include weighted channel selection, automatic retry after failures and user-level model rate limits. The dashboard supports request-based, usage-based and cache-hit cost accounting, with token grouping and model-access controls. These controls can help organize usage, but retries and a common API format do not guarantee uninterrupted service or identical behavior across providers. The current repository license is AGPLv3. Docker Compose is the recommended quick-start path in the README, which also documents Docker commands with SQLite or MySQL. Running the software still involves infrastructure, operations and upstream model charges. Review the current license, secure your deployment and validate accounting against provider bills. Compare LiteLLM if you also want a Python SDK or a documented gateway for MCP servers and agents. |
| Category | Data Analytics | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Pricing unavailable | Open Source |
| Starting Price | N/A | N/A |
| Use Cases |
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| Tags | no-codeAI analyticsdata insightsbusiness usersdomain experts | AI gatewayLLM routingmodel managementrate limitingusage accounting |
| Features | ||
| Natural language interaction | ||
| User-friendly interface | ||
| Real-time data processing | ||
| Integration with multiple data sources | ||
| Intelligent data analysis | ||
| Comprehensive data visualization | ||
| Customizable queries | ||
| Large dataset support | ||
| Secure data handling | ||
| Self-hosted AI gateway and model management | ||
| Provider channels, token groups and model restrictions | ||
| OpenAI-compatible, Claude Messages and Gemini format support | ||
| Documented limits on protocol conversion | ||
| Weighted channel selection and failure retries | ||
| User-level model rate limiting | ||
| Request, usage and cache-hit cost accounting | ||
| Docker Compose deployment documentation | ||
| View Chat With Data | View New API | |
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