# Snorkel AI

> A data development platform for frontier AI that creates specialized, high-quality training datasets and evaluation systems designed for complex, domain-specifi

- **Canonical URI:** https://www.vioscale.ai/software/snorkel-ai
- **Category:** Fine Tuning Platforms
- **Homepage:** https://snorkel.ai
- **Also known as:** snorkel-ai
- **Profile claimed by vendor:** no
- **Last updated:** 2026-08-21T11:23:15.383Z

## Vioscale score

**41.5 / 100**, confidence 21% (low). Computed 2026-09-01.

Composite of weighted, independently-sourced signals (no user reviews, no vendor payment).

| Signal | Score | Weight | Contribution | Evidence present |
|---|--:|--:|--:|:--:|
| price_level | 0 | 0.052 | 0 | - |
| reliability | 0 | 0.073 | 0 | - |
| capabilities | 45.8 | 0.04877777777777778 | 2.2 | ✓ |
| integrations | 29.9 | 0.04066666666666666 | 1.2 | ✓ |
| security_posture | 45 | 0.073 | 3.3 | ✓ |
| pricing_transparency | 0 | 0.084 | 0 | - |

## About

Snorkel provides tools for building research-grade training data, evaluation systems, and benchmarks tailored to frontier AI models and agents. It uses programmatic data labeling with audit trails, multi-reviewer pipelines, and curriculum-structured datasets to move beyond generic coverage and address domain-specific gaps that generic models struggle with.

_Independently observed._

## Platform & deployment

- **Platforms:** Web
- **Deployment:** Cloud / SaaS, Self-hosted

## Integrations (10)

_Independently observed._

- Databricks
- Snowflake
- Google BigQuery
- Amazon S3
- Google Cloud Storage
- Azure Blob Storage
- Azure Active Directory
- AWS Secrets Manager
- SAML
- OIDC

## Capabilities

_The capabilities that matter for Fine Tuning Platforms. "-" = undocumented, not absent._

| Capability | Supported |
|---|:--:|
| **Capabilities** | |
| Architecture model | Managed cloud saas platform |
| Peft lora and qlora parameter efficient tuning | ✗ |
| Rlhf and dpo preference alignment optimization | ✗ |
| Serverless hosting of fine tuned adapters lorax | ✗ |
| Four bit and eight bit memory quantization | ✗ |
| Flash attention and xformers compilation | ✗ |
| Distributed multi GPU orchestration fsdp deepspeed | ✗ |
| Synthetic data generation and evaluation pipeline | ✓ |
| Native huggingface hub push pull integration | ✗ |
| Weights and biases wandb experiment tracking | ✗ |
| SOC2 type ii | ✓ |
| Mit or apache permissive oss license | ✗ |
| Pricing model | Enterprise annual license |

## Facts

Every value below carries its source and our confidence. Facts are re-crawled on a freshness schedule.

### pricing

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| pricing.model | commercial | [link](https://snorkel.ai) | 2026-08-21 | 60% (medium) |

### integrations

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| integrations.count | 10 | [link](https://snorkel.ai) | 2026-08-21 | 60% (medium) |

### market

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| market.availability | `{"hqCountry":"US","primaryMarkets":["US"],"availabilityScope":"global","availableCountries":[],"notAvailableCountries":[]}` | [link](https://snorkel.ai) | 2026-08-21 | 75% (high) |

### security

| Attribute | Value | Source | Retrieved | Confidence |
|---|---|---|---|---|
| security.gdpr | yes | [link](https://snorkel.ai/blog/trustworthy-ai-introduction/) | 2026-08-21 | 75% (high) |
| security.soc2 | yes | [link](https://snorkel.ai/blog/trustworthy-ai-introduction/) | 2026-08-21 | 75% (high) |

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*Source: Vioscale (https://www.vioscale.ai/software/snorkel-ai). Independent, evidence-based software intelligence. Cite the canonical URI.*