comparison
inference.sh teams vs building in-house
Ramp built their AI workspace with a dedicated eng team. you don't have to.
| build in-house | inference.sh | |
|---|---|---|
| shared memory across team | ||
| custom integrations | ||
| ready in days (not months) | ||
| no dedicated eng team required | ||
| evolving skill registry | ||
| 250+ tools built in | ||
| scheduled automations | ||
| model-agnostic | ||
| self-hostable | ||
| ongoing maintenance included |
what Ramp built
Melody Koh described how Ramp built an internal AI workspace for 800 people. shared memory across Slack, Notion, Linear, and Calendar. automated daily digests. skills that compound across the team. it took a dedicated engineering team and months of work.
inference.sh teams is that same architecture as a product. shared memory, evolving skills, scheduled automations, 250+ tools. connect your team's tools, pick skills from the registry, and start working. no eng team required.
the compounding problem
building in-house means maintaining in-house. every new integration, every model update, every security patch is on your team. inference.sh teams handles the infrastructure. you focus on the work your team was hired to do, not building internal AI tooling.
and because skills live in a public registry, your team benefits from improvements made by thousands of other users. that's a compounding advantage no in-house build can match.
frequently asked questions
what does 'building in-house' actually take?
Ramp's AI workspace took a dedicated engineering team months to build. shared memory, tool integrations, approval workflows, scheduling. each piece is a project. inference.sh teams ships it as a product.
can I still customize if I use inference.sh teams?
yes. connect custom tools via MCP, publish private skills, build custom integrations with the API. you get the foundation without building from scratch.
what if I outgrow inference.sh teams?
self-host on your infrastructure with full control. same product, your compute. or extend with custom integrations while keeping the core platform.
ready to ship?
start with the hosted platform. deploy your own when you're ready.
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