# Zibra Labs

**Canonical:** https://apis.io/providers/zibra-labs/  
**Website:** https://zibralabs.ai/  
**APIs profiled:** 0

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.

## Kin Score — 5.0 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 5.0).

| Facet | Score |
|---|---|
| Discoverability | 50.0 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 0.0 |
| Developer Ergonomics | 0.0 |
| Commercial Clarity | 0.0 |
| Access Clarity | 0.0 |

## Agent readiness — 0.0 (human-only)

| Dimension | Value |
|---|---|
| Spec Presence | no |
| Agentic Access | no |
| Reversibility Documented | no |
| MCP Server | no |
| Auth Clarity | no |
| Idempotency | no |
| Error Semantics | no |
| OpenAPI Examples | no |
| Rate Limit Signal | no |
| Event Surface Described | no |
| Agent Skills | no |
| Well Known Catalog | no |
| Consent Identity | no |
| Agent Card | no |
| Dry Run Mode | no |

## Access

Unknown — onboarding: unknown, pricing: unknown, trial: no (confidence: low).

## Security (1)

- **Zibra Labs Domain Security** — HSTS · DMARC

## Tags

Company, Distributed Compute, AI Infrastructure, GPU, Cloud, Machine-Learning, Y Combinator

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Profiled by [API Evangelist](https://apievangelist.com) and published on [APIs.io](https://apis.io/providers/zibra-labs/). Scores are computed from the provider's own public artifacts under a published rubric.