Dataloop vs New API
Side-by-side comparison · Updated October 2026
| Description | Dataloop's video annotation platform enhances video data management by integrating advanced tools and workflows designed for AI and data operations. The platform provides comprehensive features like data and model management, pipeline creation, human feedback mechanisms, a marketplace for AI tools, and robust security measures. It also offers solutions by role, including AI & Data Leaders, Data Engineers, Data Scientists, Software Engineers, and Human Reviewers, facilitating customized user experiences. Furthermore, Dataloop supports various use cases, such as active learning workflows, GenAI stack building, AI production running, multi-cloud AI compute, and more. | 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 | AI Assistant | Developer Tools |
| Rating | No reviews | No reviews |
| Pricing | Freemium | Open Source |
| Starting Price | $49/mo | N/A |
| Plans |
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| Tags | video annotationdata managementAI toolsworkflowsdata operations | AI gatewayLLM routingmodel managementrate limitingusage accounting |
| Features | ||
| Data and model management | ||
| Pipeline creation | ||
| Human feedback mechanisms | ||
| Marketplace for AI tools | ||
| Robust security measures | ||
| Role-based solutions | ||
| Support for various AI use cases | ||
| Multi-cloud AI compute | ||
| GenAI stack building | ||
| AI production running | ||
| 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 Dataloop | View New API | |
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