Auto Wiki vs New API
Side-by-side comparison · Updated October 2026
| Description | TensorFlow is a comprehensive open source platform for machine learning, featuring tools, libraries, and community support for developing ML-powered applications. It offers C++ and Python APIs, and additional features like distributed learning, mobile deployment, and conversion tools. | 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 | Machine Learning | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Free | Open Source |
| Starting Price | Free | N/A |
| Plans |
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| Tags | TensorFlowmachine learningopen sourcelibrariescommunity support | AI gatewayLLM routingmodel managementrate limitingusage accounting |
| Features | ||
| Comprehensive ecosystem for machine learning development | ||
| C++ and Python APIs for ML model construction and execution | ||
| Support for training neural networks and making predictions | ||
| Tools for distributed learning and mobile deployment | ||
| Debugging, profiling, and conversion utilities | ||
| Flexible framework for custom plugin development | ||
| Extensive community resources and documentation | ||
| Open-source with active development and support | ||
| Integration of state-of-the-art machine learning models | ||
| Support for a wide range of machine learning environments | ||
| 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 Auto Wiki | View New API | |
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