11 Tools for Synthetic Data Generation in 2025

How to set up synthetic data workflows

Synthetic data mirrors real data's structure, stats, and patterns. Start with samples or models to create new datasets. Why use it? Train ML without exposing sensitive info. Test rare scenarios real data misses. Boost small datasets for better results. Formats: Tables, images, text, time series. Pick tools by data type and needs. Helpful: Fast.io Workspaces, Fast.io Collaboration, Fast.io AI.

Pro tip: Start with a small pilot. Define your baseline metrics, throughput, error rate, review time, before rolling out a tool to the whole team.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

What to check before scaling best tools for synthetic data generation

Gretel.ai focuses on tabular data with privacy built in. Upload a CSV. It learns patterns and generates new rows that match.

Features:

• Easy upload synthesis
• Privacy reports flag risks
• API hooks for automation

Pricing: Free small sets. Paid plans based on usage.

Example: Healthcare team generates patient records for model training. Outcome: High utility score vs real data, zero privacy flags.

Limit: Tabular only, no multimodals.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

Run Tools Synthetic Data Generation workflows on Fast.io

Fast.io gives AI agents 50GB free storage, MCP tools for file ops, and built-in RAG. Perfect for storing training data securely with humans. Built for tools synthetic data generation workflows.

Free Agent Storage

Tonic.ai

Tonic.ai swaps PII in databases for fakes while keeping realism. Works inside Snowflake or Postgres.

Highlights:

• Database native
• Realistic fakes (names, SSNs)
• Version control

Starts at listed enterprise rates.

Example: E-commerce tests checkout flows with synth orders. Outcome: Bug catch rate matches production, safe for devs.

Limit: Focus on de-ID over pure generation.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

YData Fabric

YData runs full pipelines: profile real data, synth, check quality.

Key:

• Auto GAN/VAE choice
• SDK for Python
• Bias checks

Open core, paid tiers available.

Example: Fintech expands loan data. Outcome: Model F1 score improved .

Limit: Heavier for quick jobs.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

Mostly AI

Mostly AI scales to huge tables, scores high on real-world utility.

Does:

• 100M+ rows
• Relationship fidelity
• SQL dumps

$10K/year entry.

Example: Retail simulates inventory chains. Outcome: Supply chain sim accuracy remains high.

Limit: Pricey for startups.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Syntho

Syntho preserves DB relationships. Schema in, full synth DB out.

Pros:

• Cloud warehouse support
• Rules-based generation
• On-prem option

€99/month start.

Example: SaaS tests user graphs. Outcome: Referral model trains without prod data.

Limit: Relational focus.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Hazy

Hazy pipelines for big enterprise with compliance.

Covers:

• Streaming synth
• Custom rules
• Full audits

Custom quote.

Example: Bank stress-tests with synth transactions. Outcome: Compliance passed, compute costs reduced.

Limit: Enterprise sales cycle.

Practical execution note for best tools for synthetic data generation: define a baseline process, assign ownership, and document fallback behavior when dependencies fail. Run a pilot with a small team, collect concrete metrics, and compare throughput, error rate, and review time before broad rollout. After rollout, keep a living checklist so future contributors can repeat the workflow without re-learning critical constraints.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Synthesis AI

Synthesis AI does images/videos from text. Synth humans/scenes.

Features:

• Pose/age tweaks
• Full bodies
• Easy API

Request quote.

Example: AV firm trains detection on rare poses. Outcome: Recall improved.

Limit: Visuals only.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Synthetic Data Vault (SDV)

Open-source SDV models relational tables end-to-end.

Strengths:

• Python pip install
• Model variety
• Built-in evals

Free.

Example: Startup prototypes DB synth. Outcome: Baseline models in days.

Limit: Needs tuning for quality.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

NVIDIA Nemotron

Nemotron generates text/images fast on GPUs.

Wins:

• High fidelity
• Hardware accel
• Open models

Free.

Example: Game dev fills asset datasets. Outcome: Training time halved.

Limit: GPU required.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Microsoft Presidio

Presidio detects PII and fakes it in text/tables.

Core:

• Scanner + generators
• CLI/Python
• MS integrations

Free OSS.

Example: Docs team anonymizes reports. Outcome: Redaction time cut .

Limit: Anonymization heavy.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Databricks Synthetic Data Generator

Databricks synth in Unity Catalog, Spark-scale.

Good:

• Big data friendly
• Governance
• SQL first

Platform bundled.

Example: Analytics firm tests pipelines. Outcome: Prod-like tests locally.

Limit: Databricks lock-in.

Add one practical example, one implementation constraint, and one measurable outcome so the section is concrete and useful for execution.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.

Document decisions, ownership, and rollback steps so implementation remains repeatable as the workflow scales.

Teams should validate this approach in a small test path first, then standardize it across environments once metrics and outcomes are stable.