# Bagel Labs

**Canonical:** https://apis.io/providers/bagel-labs/  
**Website:** https://bagel.com  
**APIs profiled:** 0

Bagel Labs is an AI research lab building distributed diffusion training infrastructure for frontier diffusion workloads on commodity and heterogeneous GPU fleets. Its core method, Distributed Diffusion Models (DDM), replaces a single large diffusion model with an ensemble of smaller expert models trained independently on dataset partitions with no gradient synchronization, then ensembled at inference by a lightweight router (the Paris Inference Engine, PIE). Public releases include Paris-1 (image diffusion) and Paris 2.0 (video generation), with open weights on Hugging Face and papers on arXiv. Founded in 2023 by Bidhan Roy and backed by Polychain Capital, the lab targets image, video, world-model, and robotics / physical AI workloads.

## Kin Score — 9.8 / 100 (minimal)

Scored 2026-08-20 under rubric 0.12.0. Trend: flat (+0.0 from 9.8).

| Facet | Score |
|---|---|
| Discoverability | 57.4 |
| Contract Quality | 0.0 |
| Governance | 0.0 |
| Contract Governance | 0.0 |
| Operational Transparency | 2.6 |
| Developer Ergonomics | 11.9 |
| Commercial Clarity | 6.6 |
| Access Clarity | 6.6 |

## 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)

- **Bagel Labs Domain Security** — TLSv1.3 · HSTS · DMARC

## Tags

Company, Ai Data, Artificial Intelligence, Machine-Learning, Diffusion Models, Generative AI, Distributed Training, Research Lab

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