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YABANIN

Self-hosted AI infrastructure with measurable returns

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YABANIN is a self-hosted gateway that brings AI into your business while keeping control of your data, models, and costs through routing, budgets, and usage

About YABANIN

Screenshot of the YABANIN homepage
The YABANIN homepage

YABANIN is a self-hosted gateway platform designed for companies to deploy AI with full control and accountability. It provides routing of multiple model providers through a single gateway, budgets and usage analytics, and attribution of costs to specific teams, tasks, and agents. The platform supports open-source foundations with Apache-2.0 licensing for the core, and pairs infrastructure with enterprise deployment and integration support. YABANIN works alongside RAIDER, an agent workflow platform that turns engineering tasks into repeatable workflows. Together they help companies measure AI adoption through a structured implementation approach: setting baselines, deploying and connecting systems, validating outcomes, and making AI a standard part of work.

  • agent workflows
  • budget control
  • model routing
  • ai-infrastructure
  • open-source
  • self-hosted
  • usage analytics

Features worth knowing

  • Model provider support through a single common gateway
  • Usage attribution to teams, tasks, and agents
  • Agent workflows in Scala for repositories and code
  • Apache-2.0 open-source core with enterprise deployment option

Pricing

Pricing model: Contact for pricing

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YABANIN metrics

Majestic link data for yabanin.ai. Arrows show the change since the previous check.
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Majestic scanned 11 Nov 2026, updated 11 Nov 2026. Link stats via Majestic. Trust Flow, Citation Flow and Topical Trust Flow are trademarks of Majestic-12 Ltd.

YABANIN domain facts

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From the registry's public RDAP record, checked 11 Nov 2026, 18:50 UTC.
Domain age2 months
Registered
Expires
RegistrarCloudflare, Inc
DNSSECNot signed

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FAQ

How does the self-hosted deployment protect data?
YABANIN runs the platform in your own environment, allowing you to choose local inference for restricted work and approved external models only where your policy permits. This keeps the data boundary under your control.

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