Best Cloud Services for Synthetic Data Generation (2026)

Contents

Figure 1: Cloud platforms offer scalable synthetic data generation with built-in privacy tooling, eliminating the need to build compliance infrastructure from scratch

So you've decided to generate synthetic data. Excellent choice—welcome to the future. Now comes the slightly less fun part: picking a cloud service to actually do it. And oh boy, do you have options.

I've spent an unhealthy amount of time testing these tools for fraud detection work, so consider this your shortcut. I'll break down the best cloud services for synthetic data generation, tell you where each one shines, and share where each one made me want to throw my laptop out a window. If you're new to synthetic data, our synthetic data for healthcare guide covers foundational concepts that apply across platforms.

Why Go Cloud for Synthetic Data Anyway?

Fair question. You could absolutely run an open-source library on your laptop. But cloud services bring three big things to the table:

Scale: Generating 10 million realistic transactions on a MacBook? Good luck. Cloud GPUs laugh at that workload. A RTX 5080 helps locally, but cloud scale is unmatched for massive generation tasks.

Privacy tooling built in: Most platforms bake in differential privacy and leakage checks, so you don't have to roll your own compliance nightmares.

Pipelines, not scripts: Cloud platforms connect generation to validation to model training in one flow. That integration saves entire workdays, IMO.

Still, not all services fit every project. So let's compare the heavyweights.

Gretel.ai: The Privacy-First Specialist

If synthetic data had a fan club president, Gretel would run for that office. This platform lives and breathes privacy-preserving generation, and it's my go-to recommendation for teams handling sensitive financial or healthcare data.

What I like:

• Gretel Synthetics handles tabular, text, and time-series data with solid differential privacy controls
• Automatic privacy reports tell you exactly how risky your synthetic data is. No guessing, no vibes-based compliance
• The REST API makes integration into an existing pipeline almost embarrassingly easy

The catch? Pricing scales with usage, so a startup experiment can quietly grow into a real budget line item. Ask me how I know. :)

My honest take: for fraud detection pipelines with strict compliance requirements, Gretel earns its spot at the top.

MOSTLY AI: The Enterprise Powerhouse

MOSTLY AI built the OG synthetic data engine—they've been at this since before it was trendy. And it shows in the polish.

Here's where they shine:

• Correlated data generation that preserves relationships across related tables. Your transactions stay connected to your customers, which matters enormously for fraud modeling
• Fairness and rebalancing features that let you correct bias in your source data. You can generate a world where fraud is more common, so your models actually learn
• Reproducibility controls that keep auditors happy

The downside: it's enterprise-priced, and the UI occasionally assumes you have a data science degree. I once spent twenty minutes hunting for a setting that lived in a completely different menu. Was it my fault? Partially. Did I enjoy it? No.

If you're a large organization with complex relational data, MOSTLY AI justifies its price tag.

Tonic.ai: The Developer's Pick

Tonic started in the dev-tooling world (de-identification for test databases), and that heritage shows. Everything feels engineered for people who write code for a living.

Highlights:

• Database-native workflows — Tonic connects directly to Postgres, MySQL, Snowflake, and friends, then writes synthetic copies back out
• Subsetting and synthesis in one step, so you can generate a realistic 1% slice of your data for testing
• Solid handling of schemas, including the gnarly ones with foreign keys and weird constraints

For teams that want synthetic data primarily for development and testing environments, Tonic might be the best fit. For pure ML training data, I'd lean toward Gretel or MOSTLY. But hey, if your team lives in databases all day, give Tonic a look.

AWS: Build-It-Yourself at Cloud Scale

Ever wondered why nobody mentions AWS Synthetic Data Service? Because there isn't one single product—you assemble the pieces yourself.

Your toolbox includes:

• Amazon SageMaker for training and deploying generative models (GANs, VAEs, diffusion models)
• Amazon Bedrock for LLM-based synthetic data generation—great for synthesizing realistic text like fraud narratives or support tickets
• Amazon DataZone and Glue for governing and orchestrating your data flows

The upside: unmatched scale, tight integration with everything else you already run on AWS, and no per-record fees. The downside: you're building, not buying. You'll write more code, wire up more services, and own the validation logic yourself.

My verdict? If your team has strong ML engineers and existing AWS infrastructure, this route gives you maximum flexibility at minimum vendor lock-in. If your team consists of three overworked people, buy a managed platform instead. Trust me on this one. :)

Google Cloud: Vertex AI and the Synthetic Data Vault

Google brings two distinct flavors to the party.

The Managed Path: Vertex AI

Vertex AI gives you managed training for generative models, plus Synthetic Data Generation via Generative AI for text-heavy tasks. It also connects nicely to BigQuery, which matters if your data already lives there. The integration story is genuinely strong—your pipeline can read from BigQuery, train on Vertex, and write back without leaving the platform.

The Open-Source Path: Synthetic Data Vault (SDV)

Here's a fun fact: some of the best synthetic data tooling is open source, and Synthetic Data Vault leads that pack. SDV offers models like Gaussian Copulas, CTGAN, and TVAE, all free and battle-tested. You can run it anywhere, including on GCP infrastructure.

I keep SDV in my back pocket for prototyping. It generates a solid baseline in minutes, and I only escalate to a paid platform when I need enterprise features or compliance guarantees. Start free, pay when it earns it—that's just good engineering economics.

# Quick SDV baseline generation
from sdv.single_table import GaussianCopulaSynthesizer
from sdv.metadata import SingleTableMetadata

metadata = SingleTableMetadata()
metadata.detect_from_dataframe(real_transactions)

synthesizer = GaussianCopulaSynthesizer(metadata)
synthesizer.fit(real_transactions)
synthetic_transactions = synthesizer.sample(num_rows=100000)

Azure: Solid, Steady, Enterprise-Ready

Azure Machine Learning gives you a capable workspace for building generative pipelines, plus strong governance through Microsoft Purview. And if your company already drinks the Microsoft Kool-Aid, the integration wins you real convenience—Active Directory, compliance dashboards, the works.

Where Azure lags slightly: it lacks a dedicated, plug-and-play synthetic data product the way Gretel or MOSTLY AI offer. You assemble the capability from Azure ML, open-source libraries, and elbow grease. Enterprises already committed to Microsoft will love it. Everyone else might find greener pastures elsewhere.

Quick Comparison Cheat Sheet

Here's my honest scorecard after way too many hours with these tools:

How to Actually Choose

Forget feature checklists for a second. Ask yourself three questions:

What data shape do I have? Simple tabular data? Almost anything works. Relational, multi-table, time-series? Narrow your list to MOSTLY AI, Gretel, or SDV.

What does compliance demand? Regulated industries (finance, healthcare) need privacy guarantees and audit trails—Gretel and MOSTLY AI lead here.

Build or buy? Strong ML team plus existing cloud? Build on AWS/GCP/Azure. Small team, big problem? Buy a managed platform and thank me later.

And please, whatever you do: run a proof of concept before signing anything. Generate a sample, train a model on it, test against real data. A two-week pilot tells you more than any sales deck ever will. FYI, most of these platforms offer trials or free tiers, so the experiment costs you nothing but time.

Recommended Books

• Synthetic Data for Machine Learning by Various Authors — covers generation methods, platform comparisons, and deployment strategies for synthetic data across cloud and on-premise environments.
• Cloud Computing: Concepts, Technology & Architecture by Thomas Erl — essential reading for understanding cloud architecture patterns, useful when evaluating which platform fits your infrastructure.
• Designing Machine Learning Systems by Chip Huyen — covers the full ML lifecycle including data generation, pipeline design, and the build-vs-buy decisions this article explores.

Want to Go Deeper?

If you want structured learning on cloud ML platforms and data pipelines, Educative's cloud and ML courses offer hands-on labs for AWS, GCP, and Azure ML services. Their unlimited plan is useful when you're comparing platforms across multiple providers.

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Wrapping It Up

Quick recap: Gretel for privacy-first tabular generation, MOSTLY AI for enterprise relational data, Tonic for dev environments, AWS/GCP/Azure for build-your-own pipelines, and SDV for a free open-source baseline. Match the tool to your data shape, your compliance needs, and your team's bandwidth.

Start small. Pilot one or two options, validate the output rigorously, and scale what works. The best cloud service, IMO, is the one that fits your pipeline—not the one with the flashiest website.

And if you pilot three platforms and your favorite loses? Join the club. My team's winning tool was the cheapest one, and I still haven't fully recovered from the shock. :)