Seedfast, a Gretel alternative for self-serve users left after the acquisition

You went looking for a Gretel alternative, typed gretel.ai into the browser, and landed on nvidia.com. That redirect is the short version of what happened. NVIDIA acquired Gretel in March 2025 — reported at somewhere north of $320 million — and over the following year the self-serve product developers used to sign up for on their own got folded into NVIDIA's enterprise stack. Nobody sent the free-tier and self-serve accounts a migration plan. This is that plan, and it splits by what you were actually using Gretel for, because two different jobs hid under one login.

Key Takeaways

• NVIDIA acquired Gretel in March 2025 (reported above $320M). As of July 2026, gretel.ai 301-redirects to nvidia.com, the gretelai org's repositories are archived (read-only), and the legacy /pricing page returns a 404.
• The old self-serve free tier — a modest monthly credit allowance — has been discontinued. No public sign-up flow remains for the standalone product.
• Gretel's technology was absorbed into NVIDIA NeMo as the Data Designer and Safe Synthesizer microservices, available through NVIDIA AI Enterprise. Pricing is sales-gated, so there is no self-serve tier replacing the one you lost.
• Which Gretel alternative is right depends entirely on the job you ran it for. Model-training and privacy-synthesis users should evaluate YData, MOSTLY AI, SDV, or NeMo. Seedfast takes the other job, filling an application database so the software reading it actually boots.
• Seedfast covers that second job on PostgreSQL. Give it a connection string and a scope in plain English, and it reads the schema as it stands at that moment, then writes rows into every table the scope touches, each reference pointing at a row that exists. Pricing is a flat monthly plan that includes a pool of credits, and the free plan takes no card.

What happened to Gretel

Gretel launched in 2019 around one promise, privacy-preserving synthetic data. Train a model on a sensitive dataset, generate a synthetic version that keeps the statistical shape without carrying real records forward, and hand that safe copy to whoever needs it. Developers took to it because you could try it without a sales call — there was an API, a Python SDK, and a free tier that let you kick the tires before committing a budget.

NVIDIA announced the acquisition in March 2025. Reporting at the time put the deal above $320 million, and NVIDIA was direct about the intent. Gretel's synthetic-data technology would power its own AI platform rather than continue as an independent self-serve product, and that is how the next year played out.

Here is where things stand as of July 2026. The signals are easy to miss one at a time, so it helps to see them together:

• gretel.ai redirects to nvidia.com. The standalone marketing site is gone; the domain now points at NVIDIA.
• The gretelai org's repositories are archived (read-only). You can still clone the SDK and example repos, but nothing new gets merged into them.
• The old pricing page 404s. With no self-serve plan page, there is no free tier and no way to start a fresh paid subscription on your own.
• The technology moved into NVIDIA NeMo. Gretel's capabilities re-emerged as two NeMo microservices — Data Designer for synthetic dataset generation, Safe Synthesizer for privacy-preserving synthesis — distributed under NVIDIA AI Enterprise.

This is not the Snaplet Seed alternative situation, where a company wound down and open-sourced the code on its way out the door. Gretel was absorbed instead, so the technology arguably sits in a bigger, better-funded home now. What changed for a self-serve user is narrower and more annoying than a shutdown. That bigger home just does not sell to you the way Gretel did.

What this means if you were using Gretel

The honest framing is not "Gretel is dead" — the underlying technology is very much alive inside NVIDIA. The question that matters to you is whether the thing you depended on still exists in a form you can actually reach, and for self-serve accounts the answer is mostly no. A few risks are concrete enough to plan around.

New self-serve sign-ups are closed. If your team was going to add seats or spin up a new project on the old free tier, that path no longer exists. Onboarding a new hire onto Gretel the way you did in 2024 is not an option in 2026.

The free tier is discontinued. The credit-based free plan that made Gretel easy to adopt is not coming back. NVIDIA AI Enterprise licensing is the replacement, and it starts with a conversation rather than a credit card.

The SDK is frozen. Because the gretelai GitHub org is archived, the open-source client you may still have pinned in requirements.txt will not get patches. It runs until a dependency bump or a Python release breaks it, and then it stays broken.

Hosted endpoints carry sunset risk. If any of your pipelines still call a legacy Gretel Cloud endpoint, treat that as borrowed time. Post-acquisition APIs get deprecated on the acquirer's schedule, not yours, and rarely with generous notice. Anything running in CI against a hosted Gretel call is a good candidate to migrate before it fails on its own.

Support moved upmarket. The help a self-serve user got — docs, a community, a responsive issue tracker — is now enterprise support attached to a license, which suits a large team standardizing on NVIDIA and does little for a solo developer who wanted an API key and a quickstart.

None of these is an emergency on its own, but together they mean the product you evaluated is not the product you can buy today, so it is worth choosing where you go next deliberately rather than waiting for a broken build to force the decision.

The fit split — which job did Gretel do for you?

Gretel wore two hats, and the correct replacement is different for each one. Before you shortlist anything, work out which hat you were wearing, because picking a tool for the wrong job is how teams end up paying for capabilities they never touch.

The distinction is the same one that separates model-training data from application test data. Model-training data answers to statistical fidelity — the output has to reproduce the distributions and rare cases of a real dataset well enough to train on. Correctness is what application data answers to instead — every foreign key pointing at a row that exists, unique constraints holding, required columns filled, so the app boots instead of throwing on the first insert. A tool tuned for one is rarely much good at the other, and Gretel's heartland was firmly the first.

If your data was for training, fine-tuning, or evaluating a model, or for handing a de-identified copy of a sensitive dataset to another team, keep reading the ML-training section below; if it was for filling a development, test, staging, or demo database your software runs against, skip to the application-database section — the job Gretel was always overkill for.

Choosing a Gretel alternative for model-training data

Say your Gretel usage was genuinely about models and privacy, whether that meant synthesizing a training set, standing in for data you could not share, or evaluating on realistic-but-fake records. These options land closest to what you had, and every claim here is dated to July 2026 because this market moves fast.

NVIDIA NeMo (Data Designer + Safe Synthesizer). This is the literal continuation of Gretel's technology, so an organization already on NVIDIA AI Enterprise, or willing to get there, will find it the least-surprising destination — same lineage, more compute behind it. What you pay for is the shape of the relationship, now a licensed enterprise product with sales-gated pricing rather than an API key you provision yourself.

MOSTLY AI. Squarely in the fidelity-first camp, MOSTLY AI sells a hosted platform for tabular synthesis and, as of July 2026, maintains an open-source Synthetic Data SDK alongside it, so you can start in code the way Gretel users did and move to the managed platform when the workload justifies it. Statistical fidelity and privacy mechanisms are the product's whole pitch, which makes it a close spiritual match for the synthesize-then-share workflow Gretel popularized.

YData. Being Python-native, YData sits closest in spirit to Gretel's SDK-first workflow. The open ydata-synthetic library plus the commercial Fabric platform give you a self-hostable path and a managed one, so teams that liked living inside a notebook with Gretel tend to feel at home here.

SDV (Synthetic Data Vault). SDV is the open-source route on this list, an MIT-licensed Python library for tabular, relational, and time-series synthesis. You self-host and pay nothing in license fees, though you own the operational burden yourself. If your Gretel bill was the thing that hurt and you have the engineering time, this is where we would start.

For a broader survey of the AI-synthesis field and where each tool's strengths actually lie, the best AI test data generator guide lays out the model-fidelity camp against the schema-correctness camp in detail. Tonic competes in this lane too, with a multi-product suite that deserves its own map — the Tonic.ai alternatives rundown sorts it by job. The short version is that if you are training a model, one of the four above is your Gretel alternative and Seedfast is not, because it does not try to reproduce a distribution and has no business on that shortlist.

Choosing a Gretel alternative for application databases

The other group never used Gretel to train anything; plenty of teams reached for it to get realistic-looking data into a dev, test, or staging database — a synthetic-data tool pressed into service as a database seeder. That works on a toy schema and stops the moment the schema has forty tables and real foreign keys, because a fidelity-first synthesizer produces rows that look plausible on their own and then fail on insert when order.customer_id points at a customer nobody generated. The bar here was never distributional realism but whether the data holds together well enough for the app to run.

Seedfast is built for exactly that. Connect it to your application's database and Seedfast reads the live schema, then generates a connected dataset that satisfies the constraints it finds, leaving you no relationships to hand-wire. No production access is required at any point, because the rows are generated rather than copied out of something real. Gretel's question was how closely a generated dataset tracks a real distribution. For a model about to be trained on the output, that is the right question to ask. Seedfast answers a narrower one, whether the schema in front of you comes out populated at all, which is what a dev or CI database needs settled before its tests run.

# Point Seedfast at your database and describe what you want
seedfast connect
seedfast seed --scope "50 users, each with 3 projects and a handful of tasks, plus one admin account"

There is no model to fit and no export step. The scope is a sentence and the schema supplies the structure, so the rows come out connected. When a migration changes the schema, the next seedfast seed reads the new shape — there is no generation script pinned to yesterday's tables. If you are new to the category itself, the database seeding guide walks through why hand-maintained seed files break and what reading the schema fixes.

A few things matter to the specific person who is here because Gretel left:

• Seedfast fits AI coding agents. It ships an MCP server, so if you work in Claude Code, Cursor, or another MCP-aware assistant, the agent can seed a database inside the conversation instead of you shelling out. Gretel never offered that, and given its timeline it never would have.
• Seedfast runs in CI. A single seedfast seed step after your migrations leaves every pipeline run with a freshly seeded database, and an API key covers the non-interactive auth. There is no hosted-endpoint sunset to worry about here, because Seedfast writes rows into your own database rather than standing up a synthesis service you then have to keep alive.
• The pricing stays flat and self-serve, which is the part you just lost. Seedfast charges a flat monthly plan that includes a pool of credits, $16 a month for $32 of credits or $69 for $180, and a run draws from that pool by how much data it generates. Under both sits a free plan that takes no card and refills $5 of credits a month, about eight runs on a 30-table schema. After Gretel's move to sales-gated licensing, signing up and seeding a database the same afternoon is most of the point. The pricing page has the current plan breakdown, and if you are weighing flat-rate against metered or quote-gated tools, the test data tool pricing comparison sets them side by side.

Seedfast is PostgreSQL-first today and reads whatever ORM defined your schema — Prisma, Drizzle, TypeORM, Django, Rails, or raw SQL all work the same way, because it talks to the database rather than the ORM. For a category-level look at where schema-aware seeding sits next to masking, DIY scripts, and web generators, the data seeding tools comparison covers the field.

Gretel vs the alternatives: a quick map

The table below sorts the options by the job they actually do. Read it as a routing aid, not a scoreboard, because a tool being wrong for your job is not a tool being bad. Details are current as of July 2026.

If your row is one of the top four, you are in the model-training lane and one of them is your like-for-like Gretel replacement. If the job was filling an application database, Seedfast is the bottom row for a reason, since it is the only one on the list whose whole design goal is a schema that boots.

Frequently asked questions

Where to go from here

The awkward part of an acquisition like this is that nothing technically broke. Your last Gretel run still sits in a folder somewhere and the SDK still imports; what stopped is your ability to keep buying it on your own terms, and you notice that the day you try to add a project or a teammate. Sorting into the right lane now keeps the migration small. Teams training or evaluating models can land on NeMo, YData, MOSTLY AI, or SDV, and the sections above map which of those fits which workflow. For the other lane, Seedfast reads whatever schema your application database currently has and writes the dev or CI dataset that gets the app running, which the five-minute getting-started flow walks through end to end. On the free plan, $5 of credits arrive each month and no card is involved, which is enough to run the decision on your own data before anything is charged.