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.

Zibra Labs is profiled on the APIs.io network. Tagged areas include Company, Distributed Compute, AI Infrastructure, GPU, and Cloud.

🌐 Visit website 📡 Source on GitHub
6.8/100 minimal ▬ flat Agent 4/100 human only Full breakdown ↓
scored 2026-07-27 · rubric v0.5
0 APIs
CompanyDistributed ComputeAI InfrastructureGPUCloudMachine LearningY Combinator

Kin Score

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scored 2026-07-27 · rubric v0.5
Composite quality — 6.8/100 · minimal
Contract Quality 0.0 / 25
Developer Ergonomics 0.0 / 20
Commercial Clarity 0.0 / 20
Operational Transparency 0.0 / 13
Governance 0.0 / 12
Discoverability 6.8 / 10
Agent readiness — 4/100 · human only
Machine-Readable Contract 0 / 18
Agentic Access Contract 0 / 15
MCP Server 0 / 12
Machine-Readable Auth 0 / 10
Idempotency 0 / 9
Stable Error Semantics 0 / 8
Request/Response Examples 0 / 7
Rate-Limit Signaling 0 / 7
Typed Event Surface 0 / 6
Agent Skills 0 / 5
Well-Known Catalog 4 / 4
Consent & Bot Identity 0 / 3
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Security Posture 1

Authentication, domain security, vulnerability disclosure, and trust-center signals.

Zibra Labs Domain Security

HSTS · DMARC

SECURITY

Resources

Agent Surfaces 1

MCP servers, agent skills, and machine-readable catalogs

WellKnown
WellKnown

Access & Security 1

Authentication, authorization, and security posture

DomainSecurity
DomainSecurity

Company 1

The organization behind the API

Website
Website

Source (apis.yml)

apis.yml Raw ↑ 
aid: zibra-labs
accessModel:
  pricing: unknown
  onboarding: unknown
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  try_now: false
  public: false
  label: Unknown
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  source: []
  generated: '2026-07-22'
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image: https://kinlane-images.s3.amazonaws.com/shared/apis-json/icons/zibra-labs.png
name: Zibra Labs
description: 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.
url: https://raw.githubusercontent.com/api-evangelist/zibra-labs/refs/heads/main/apis.yml
x-type: company
x-source: vc-portfolio
x-backed-by:
- y-combinator
x-tier: stub
x-tier-reason: portfolio-lead
specificationVersion: '0.20'
created: '2026-07-17'
modified: '2026-07-21'
tags:
- Company
- Distributed Compute
- AI Infrastructure
- GPU
- Cloud
- Machine Learning
- Y Combinator
apis: []
common:
- type: Website
  url: https://zibralabs.ai/
- type: DomainSecurity
  url: security/zibra-labs-domain-security.yml
- type: WellKnown
  url: well-known/zibra-labs-well-known.yml
maintainers:
- FN: Kin Lane
  email: kin@apievangelist.com
- FN: APIs.json
  email: info@apis.io
x-enrichment:
  date: '2026-07-21'
  status: minimal
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