11 Tools for Synthetic Data Generation in 2025

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.

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.

Give Your AI Agents Persistent Storage

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 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.

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.

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.

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.

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.

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.

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.

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.

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.

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.