Gretel charges a $295/month platform fee for teams, plus usage rates around $2.20 per credit, though individuals can start with a generous free tier of 15 credits (approx. 100k records) per month. For an ML team generating 1 million synthetic training rows to balance a fraud dataset, you’re looking at roughly $300–$500 in compute credits on top of the base fee. It’s not a budget tool, but since Nvidia’s $320M acquisition in 2025, it has solidified itself as the premium infrastructure for privacy-preserving AI development.
The platform’s crown jewel is Gretel Navigator, a compound AI system that lets you prompt-engineer entire datasets. Instead of writing complex probability distributions in Python, you simply tell it, “Generate a medical claims dataset with a 5% fraud rate and correlated diagnosis codes,” and it outputs statistically valid tabular data. The Python SDK is polished, handling everything from PII redaction to differential privacy with minimal friction. The integration with the Nvidia ecosystem has only improved inference speeds, making it viable for generating massive-scale datasets for RAG evaluation or fine-tuning.
However, the “developer-first” branding hides a steep cost curve. While the 15 free credits are great for prototyping, moving to production volume hits the wallet hard compared to open-source alternatives like SDV (Synthetic Data Vault). Additionally, Gretel is strictly for generation and transformation. It is not a data labeling platform; if you need humans to draw bounding boxes or classify sentiment, this is the wrong tool. It also feels like overkill if you just need a simple anonymized dump of your Postgres production database for a staging environment—Tonic.ai handles that specific “database subsetting” workflow with less friction.
Use Gretel if you are an AI engineer needing net-new data to fix class imbalances or a privacy engineer stripping PII from RAG contexts. Skip it if you just need a mocked-up database for your frontend team; a simple script or Tonic is more cost-effective for that.
Pricing
The free tier is surprisingly usable, offering 15 credits/month which equates to roughly 100k synthetic records or 1.5M Navigator characters—enough for a serious POC. The cliff comes at the Team plan: a mandatory $295/month platform fee kicks in before you even pay for extra usage (approx. $2.20/credit). Unlike usage-only APIs, this high floor makes it hard for mid-sized projects to justify unless they require the 99.5% SLA or SSO. For strictly massive-scale simple data, running open-source models on your own GPUs is significantly cheaper.
Technical Verdict
Gretel's SDK is a joy to use—cleanly typed, well-documented, and reliable. You can spin up a synthetic generation job in under 10 lines of code. The API handles the heavy lifting of training and generation asynchronously, so you aren't keeping connections open. Latency on the new Navigator inference API is impressive, often returning complex synthetic rows in milliseconds. It integrates natively with LangChain and LlamaIndex, making it a drop-in component for modern RAG pipelines.
Quick Start
# pip install gretel-client
from gretel_client import configure_session, Navigator
configure_session(api_key="GRT-...")
navigator = Navigator(model="gretelai/auto")
# Generate synthetic data from a prompt
data = navigator.generate(
prompt="Create 5 rows of realistic IoT sensor data including temp and vibration.",
num_records=5
)
print(data.df)Watch Out
- The $295/mo Team plan fee is a platform fee, not a credit retainer; usage is charged on top.
- Navigator credits burn faster than standard tabular generation—watch your usage on complex prompts.
- It is NOT a labeling tool; it cannot help you annotate existing images or text manually.