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Skydive vs Gumloop
Skydive gives you a team of AI coworkers that do real work in the tools you already use, no canvas to wire up, no credits to ration.
Skydive gives you a team of AI coworkers that do real work in the tools you already use, no canvas to wire up, no credits to ration.
Gumloop starts with a no-code canvas you build workflows on. Skydive starts with the coworker: describe the outcome, and each agent runs the job on its own computer, in your channels, on a flat plan.
Gumloop is a no-code automation platform: you build workflows on a visual canvas and layer agents on top. Skydive is agent-first, you describe an outcome and each agent assembles the tools and steps itself, working as a coworker with its own computer, identity, memory, and channels.
Yes. Gumloop runs agents and orchestrates multi-agent workflows on its canvas, with skills, knowledge sources, and schedules. The difference is the unit of work: Gumloop centers a workflow you design, while Skydive centers a named agent that owns a role and its own isolated computer, memory, and permissions.
Yes, and it's strong here: Gumloop offers 100+ prebuilt nodes, code sandboxes, MCP hosting, and bring-your-own API keys. Skydive matches the core execution and adds a dedicated cloud computer per agent, its own Slack and GitHub identity, and reach into your own computer via Portal.
Gumloop is credit-metered: Pro starts at 37 dollars per month for a bundle of monthly credits that don't roll over, with concurrency capped on Pro and higher limits on Enterprise. Skydive Team is a flat 99 dollars per month for unlimited seats and agents, plus metered usage. Credit units are not directly comparable, so compare on a real workload.
Gumloop is SOC 2 Type II attested and HIPAA compliant, with zero-retention LLM agreements, but role-based access, SSO/SAML, audit logs, VPC, and model access control sit on its Enterprise tier. Skydive injects credentials on the wire so they never touch prompts, logs, or memory, isolates each agent in its own sandbox, and offers SSO/SAML, audit logs, roles, and model allow-listing. Skydive is currently in beta; compliance details are available on request.
There is no canvas to rebuild. You describe the outcome you want, connect the tools the agent needs once, and it starts working. You do recreate the skills, memory, and schedules you rely on, since no automated migration is published between the two.