What is Rockfish Data used for?

Rockfish Data is used to generate labeled synthetic data for testing machine learning models and AI agents and for sharing data safely.

Who should use Rockfish Data?

Data science, ML, and AI teams in telecom, cybersecurity, finance, healthcare, and automotive that need realistic test data without exposing production records.

Can Rockfish Data run inside our own environment?

Yes. Rockfish Data runs in a private VPC, on premises, or as a Snowflake Native App, so source data stays in your environment.

Is Rockfish Data SOC 2 compliant?

Yes. Rockfish Data states that it holds a SOC 2 Type II attestation, which helps teams meet security and compliance requirements for sensitive data.

What are the best alternatives to Rockfish Data?

Alternatives to Rockfish Data include MOSTLY AI, Tonic.ai, YData Fabric, and GenRocket for synthetic and test data generation across teams.

Rockfish Data Reviews, Features & Alternatives

Overview

What is Rockfish Data?

Rockfish Data is an enterprise synthetic data platform that generates labeled time-series and tabular data from a schema, sample dataset, or production export. Teams inject anomalies, drifts, incidents, and edge cases, then use the data for model testing, agent evaluation, regression testing, and privacy-safe sharing. It runs in a customer VPC, on premises, or as a Snowflake Native App. Data science, ML, and AI teams in telecom, cybersecurity, finance, healthcare, and automotive use it.

Best for

Data Science TeamsML EngineersAI Product TeamsTelecom CompaniesCybersecurity Teams

Product snapshot

Category: Data Analytics
Best for: AI model testing
Deployment: Cloud / On-premise
Free trial: Contact vendor
Integrations: Snowflake, Databricks, Hugging Face
AI features: Yes

Key features

Key features of Rockfish Data

Synthetic data generation

Create realistic time-series and tabular data from schemas or samples.

Scenario injection

Add labeled anomalies, drifts, incidents, and edge cases to datasets.

Agent evaluation sets

Build domain-realistic test scenarios paired with ground-truth answers.

Built-in quality scoring

Score generated data for fidelity, privacy, and agent answer correctness.

Plain-language generation

Describe the data needed in plain language to generate datasets.

Flexible deployment

Run in a private VPC, on premises, or inside Snowflake.

Reviews

Rockfish Data reviews

Overall

Nobody has reviewed

What users like

Keeps data inside customer environment
Labeled anomalies and edge cases
Preserves temporal and multivariate patterns
Runs as Snowflake Native App

What users dislike

Pricing not published on website
Trial terms not clearly stated
Focused on time-series and tabular
Listed as US availability only

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Alternatives

Top Rockfish Data alternatives

MOSTLY AI
Best for
Privacy-safe tabular data
Why choose it
Mature synthetic data generator for enterprise data
Tonic.ai
Best for
Test data for developers
Why choose it
De-identified and synthetic data for software testing
YData Fabric
Best for
Data quality for ML
Why choose it
Synthetic data plus profiling for ML pipelines
GenRocket
Best for
Rule-based test data
Why choose it
Generates controlled test data at large scale

Integrations

Rockfish Data integrations

Rockfish Data is designed to fit into existing data and AI workflows without moving sensitive records out of the customer's environment. It can be deployed as a Snowflake Native App, inside a private VPC, or on premises, and generated datasets can land in Snowflake, Databricks, or a team's own data pipeline. The platform works with existing evaluation frameworks and harnesses rather than replacing them, and the vendor maintains a Hugging Face presence and an MCP server for agent tooling. Teams can start from a schema, a sample dataset, or a production export, then feed the labeled output into model testing, agent evaluation, and regression testing workflows.

FAQ

Rockfish Data