The right alternative depends on what you are replacing: price, AI quality, integrations, enterprise controls, or workflow focus. Start with the closest competitors, then use the related guides to narrow the decision.
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls
Not rated
Dust is worth testing for teams that want employees to build and operate shared AI agents over company knowledge and tools while retaining model choice and administrative controls. Its flexibility also creates the buying risk: every connector, agent instruction, tool permission, model, and credit-consuming action needs an owner. The relevant metric is accepted work per credit after review—not agents deployed.
An open-source local and self-hosted AI workspace for documents, agents, and teams
4.0
AnythingLLM packages local models, document knowledge, agents, meeting notes, and multi-user workspaces into desktop, Docker, and hosted options, but privacy depends on deployment, model endpoints, plugins, and operational discipline.
A self-hosted multi-user AI interface for local, private, and third-party models
4.0
Open WebUI gives teams one customizable interface for local and hosted models, knowledge, tools, permissions, and enterprise deployment, but security, licensing, operator access, and model data paths remain the deployer's responsibility.
FreemiumProductivityCode
When to switch
Look for an alternative if Onyx is too expensive, lacks the integrations you need, or is not strong enough for your primary workflow.
What to compare
Compare output quality, setup time, pricing limits, supported models, API access, and whether the tool fits your team’s existing systems.
Best next step
Shortlist two options, run the same real workflow through both, and keep the one that reduces rework rather than the one with the longest feature list.