Zibra Labs
Zibra Labs is a Y Combinator (Spring 2026) startup building distributed compute infrastructure for AI workloads at scale. The platform gives teams access to the cheapest CPUs and GPUs across hyperscalers and neoclouds, orchestrating clusters of 100 to 50,000 mixed hardware nodes with sub-50ms task-dispatch overhead and millions of parallel tasks on spot instances across regions. Target workloads include quantitative-trading backtesting and parameter sweeps, AI post-training and reinforcement-learning pipelines, multi-modal data processing, batch and high-volume inference, and long-horizon agentic workflows. The founding team previously built three LinkedIn databases (Venice, Liquid, Espresso) and were tech leads on the open-source Ray compute framework. As of this profile Zibra Labs publishes only a corporate marketing site; no public API, developer documentation, or SDKs are available yet.
Index entry only — little beyond a description and a link, and nothing machine-readable enough for an agent to act on without a human reading the site first.
API Evangelist profiles Zibra Labs the way a machine reads it — 1 machine-readable artifact, pulled from the provider's own public surface and indexed so a developer, an analyst, or an AI agent can evaluate it against every other provider on the network.
Every provider in the network is reduced to the same set of machine-readable artifacts — OpenAPI contracts, event specifications, GraphQL schemas, runnable collections, pricing and rate-limit signals, security posture, OAuth scopes, and the agent surfaces (MCP servers and skills) that let software drive the API on its own. We profile them because the interface is the part of a company you can actually inspect: it is a truer signal of what a provider does than any marketing page. From those artifacts we compute the Kin Score — Zibra Labs scores 6.8/100 (minimal), with a separate agent-readiness read of 4/100 (human only). The full breakdown is below, followed by every artifact we hold — each card links through to its machine-readable definition on apis.io.
Kin Score
This is the API Evangelist rating — a single, repeatable read computed from the artifacts on this page. Green fill is points earned; the red track is points possible, so every bar shows earned-versus-possible at a glance.
Kin Score
How this is scored →scored 2026-07-27 · rubric v0.5
How we profile Zibra Labs
Each block below is one kind of artifact we hold for Zibra Labs. For each we say what it is and why it earns a place in the profile, then list every one we've indexed — capped at two rows, scroll within the panel for the rest.
Security Posture 1
Authentication, domain security, vulnerability disclosure, and trust-center signals — the evidence that a provider takes security seriously enough to document it. We profile it because you can't govern what you can't see.
Authentication, domain security, vulnerability disclosure, and trust-center signals.
Resources
Every other property we hold for Zibra Labs — documentation, portals, status pages, policies, and corporate surface — grouped by the job it does, following the integrator's arc from getting started to running in production.
Agent Surfaces 1
MCP servers, agent skills, and machine-readable catalogs
Access & Security 1
Authentication, authorization, and security posture
Company 1
The organization behind the API
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