Dots vs New API

Side-by-side comparison · Updated September 2026

 DotsDotsNew APINew API
DescriptionOpenAI's persistent agents for ongoing work across connected apps, with read-only proactive research and separate approval rules for actions.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.
CategoryAI AgentsDeveloper Tools
RatingNo reviewsNo reviews
PricingPricing unavailableOpen Source
Starting PriceN/AN/A
Use Cases—
  • Development Teams
  • SaaS Companies
  • DevOps Engineers
  • Finance Teams
Tags
AI gatewayLLM routingmodel managementrate limitingusage accounting
Features
Persistent assignments on a separate cloud computer
Connected-app context with Activity View to inspect and redirect work
Read-only proactive research; actions follow connected-app rules and approval controls
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
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