Open WebUI vs SuperAnnotate

Connect local runtimes like Ollama alongside OpenAI-compatible and Anthropic APIs, and switch between them inside one interface.

RAG over your own documents, web search and citation-aware answers come standard, so responses are grounded in real sources.

Extend the platform with Python code to add custom tooling, preprocessing, and multi-step pipelines, then share them with others.

SSO, RBAC, audit logs, data residency, and on-premises, hybrid or air-gapped deployment for organisations with strict requirements.

One configurable annotation surface for image, video, text, and audio projects, so multimodal datasets can be labeled without switching tools.

Search, filter, and inspect datasets to find the examples that will most improve a model, then route them into targeted annotation or review queues.

Dashboards for annotation volume, reviewer agreement, and project progress, plus advanced analytics on upper tiers for program-level reporting.

Metered orchestration capacity for running annotation, evaluation, and data-processing workloads, scaled by plan from roughly 1K to 10K compute hours.