Originally published on basecut.dev.
If you used Snaplet, you know exactly what it felt like when it worked: one command to pull a slice of production, anonymized, ready to restore. When they shut down in 2024, that workflow went with them.
Snaplet had a real following because it solved a real problem. It made "get anonymized production data into your dev environment" feel like a first-class workflow instead of a weekend project. Teams built their local dev setup and CI pipelines around it. When the shutdown happened, those workflows broke overnight.
Losing it meant teams had to either find a maintained alternative, fork the open-source code themselves, or fall back to scripts they had avoided writing for good reason. Two years on, there are a few legitimate paths. They are not all equivalent.
This post is an honest 2026 survey of where things stand: what you can actually use, who each option fits, and what the trade-offs are.
TL;DR: The best actively maintained Snaplet alternative in 2026 is Basecut — same subset → anonymize → restore workflow, YAML config instead of TypeScript, free tier for small teams. The open-source Snaplet fork is viable if you have bandwidth to self-host and maintain it indefinitely. Tonic.ai is a heavier enterprise option. pg_dump plus anonymization scripts works for tiny schemas and breaks quietly as they grow.
What is the best Snaplet alternative in 2026?
Basecut is the most actively maintained alternative to Snaplet for teams that need anonymized PostgreSQL snapshots. It covers the same core workflow — subset, anonymize, restore — with a YAML config instead of TypeScript and a broader set of anonymization strategies. The open-source Snaplet fork is also viable if your team has the bandwidth to self-host and maintain it long-term. Tonic.ai is available if you need an enterprise-grade commercial alternative with heavier procurement. And for the simplest possible cases, pg_dump plus some post-restore scripts will do the job, though that approach has a well-documented ceiling.
Snaplet alternatives compared
Before going deep on each option, here is how the four mainstream paths line up against the workflow Snaplet established.
| Basecut | OSS Snaplet fork | Tonic.ai | pg_dump + scripts | |
|---|---|---|---|---|
| Actively maintained | Yes | No active upstream | Yes | N/A |
| FK-aware subsetting | Yes | Yes | Yes | DIY with WHERE clauses |
| Anonymize before data leaves prod | Yes | Yes | Yes | No (masking runs post-restore) |
| Auto-detects common PII | Yes | Yes | Yes | No |
| Deterministic masking across tables | Yes | Partial | Yes | No |
| Config format | YAML | TypeScript | GUI + config | SQL / shell |
| Hosted option | Yes (free tier) | Self-host only | Self-host + hosted | N/A |
| Team / org-level policies | Yes (paid) | No | Yes | No |
| Free for small teams | Yes | Yes (self-host) | No | Yes |
| Best for | Teams wanting a maintained Snaplet replacement | Teams with bandwidth to own the fork | Enterprise with procurement cycles | Tiny schemas, temporary stopgaps |
The rest of this post walks through each option in detail, who it fits, and where it falls down.
What teams actually miss about Snaplet
Before evaluating options, it helps to be specific about what Snaplet actually gave people. Not everything about it was special — but a few things were.
The one-command workflow. snaplet snapshot capture followed by snaplet snapshot restore. That was the whole loop. New developer joins the team — two commands and they have a working database. CI needs realistic test data — restore the snapshot. Debugging a production incident locally — restore the snapshot. The value was not the technology; it was the fact that the workflow required no thinking.
A config that understood your schema. Snaplet's TypeScript config let you write transforms close to the data model. When it worked, it felt like the tool was aware of your schema rather than applying dumb column-name heuristics. You could write logic that said "for this table, transform this column this way" and have it behave exactly as intended.
Automatic PII masking. Snaplet would detect and mask common PII fields without requiring you to enumerate every sensitive column manually. For teams that had not yet thought carefully about their anonymization strategy, that auto-detection was the difference between using the tool and not using it.
Subsetting — not full dumps. This is the one that gets underappreciated. Snaplet did not copy your entire database. It followed foreign keys and extracted a connected slice: recent users, their related orders, their related records. The result was small enough to restore locally in seconds and referentially intact because the relationships were traversed rather than copied blindly. Full pg_dump gives you everything; Snaplet gave you the right slice. That difference matters for restore speed, local disk usage, and CI job time. It is also the reason a pg_dump-based fallback feels so much worse by comparison — once you have had subsetting, going back to full dumps is painful.
The main options after Snaplet
The open-source Snaplet fork
When Snaplet shut down, they open-sourced their code. It is on GitHub and available for anyone to run. For teams that depended heavily on Snaplet's TypeScript config and want to keep as much of their existing setup intact as possible, the fork is worth a look.
When it fits: you have a small team with the engineering bandwidth to set it up, self-host the necessary infrastructure, and handle any issues that come up. If you are comfortable owning a dependency that has no active upstream, this can be a workable path — especially if your existing snaplet.config.ts is already dialed in and you do not want to migrate anything.
The honest catch is that there are no active maintainers, no bug fixes, and no support channel. When PostgreSQL releases a new version and something breaks, that is your problem to debug. When your schema evolves and an edge case triggers unexpected behavior, you are reading source code to figure out why. That is not a knock on Snaplet — open-sourcing was a generous move by the team. It just means you are taking on maintenance ownership in full.
Verdict: viable if you are prepared to own it indefinitely. Not a good fit if "set it and forget it" is a requirement.
pg_dump plus anonymization scripts
The path many teams take immediately after losing a tool like Snaplet: wire together pg_dump, restore the dump to a scratch database, run a series of UPDATE statements to mask sensitive fields, and call it done.
When it fits: your schema is simple, your compliance requirements are minimal, and you just need a basic way to move data around. If you have five tables, no complicated FK relationships, and PII is confined to a handful of columns you can enumerate manually, this works fine.
The catch is that this is exactly the pattern that breaks as the schema grows — and it breaks quietly. UPDATE-based anonymization runs after the data is already in the target database, which means real PII is sitting on a dev machine or CI runner for however long the masking script takes. Subsetting is not built in, so you are either restoring the full production dump or writing custom WHERE clauses for every table. And when a new sensitive column gets added, nothing reminds you to update the masking script.
Verdict: fine for simple cases. Expect to outgrow it.
Tonic.ai
Tonic is the heaviest commercial option in this space. It predates Snaplet, targets enterprise buyers, and covers the core workflow — subsetting, anonymization, restore — with a GUI-first experience and a set of higher-end features like synthetic data generation.
When it fits: you are at an org where procurement, SOC 2 paperwork, and a dedicated platform team are normal parts of buying a tool. Tonic is built for that world. If your team can spend a quarter evaluating it and your compliance team wants sign-off from a vendor with a full trust-center, it is a reasonable pick.
The honest catch for most Snaplet refugees is that Tonic is not really a like-for-like replacement. The pricing, onboarding, and operational model are different — it is not a CLI you install in an afternoon. Smaller teams that liked Snaplet for its "two commands and a YAML" ergonomics usually find Tonic heavier than what they were looking for.
Verdict: reasonable if you are already an enterprise buyer. Heavier than most Snaplet users want.
Basecut
Basecut is built around the same workflow Snaplet established: define what to extract, run a CLI command, restore anywhere.
The config is YAML instead of TypeScript, which trades flexibility for simplicity. For most teams — especially those whose Snaplet configs were mostly just masking rules and row limits — the YAML is easier to read, easier to review, and easier for non-JS teams to maintain.
version: '1'
name: 'dev-snapshot'
from:
- table: users
where: 'created_at > :since'
params:
since: '2026-01-01'
limits:
rows:
per_table: 1000
total: 50000
anonymize:
mode: auto
Basecut has 30+ anonymization strategies and auto-detects common PII fields — names, emails, phone numbers, addresses — without requiring you to enumerate them. It also supports deterministic masking, which matters when the same source value needs to map to the same fake value across related tables. If jane@company.com turns into two different fake emails across users and audit_logs, your data stops behaving like the real system.
For teams with compliance requirements, Basecut adds org-level anonymization policies — rules that apply across all snapshots in a workspace without relying on individual contributors to remember to set them. You can enforce that certain columns are always masked, regardless of who runs the snapshot or what config file they used.
Snapshots can be stored locally or in cloud storage, and the CLI supports both interactive use and async agent execution for teams that want to run snapshot creation on a schedule without leaving a terminal open. The free tier covers small teams. Paid plans add team features and higher snapshot volumes.
Verdict: the closest like-for-like replacement for Snaplet, actively maintained, with a free tier that covers most small teams.
Post-Snaplet migration checklist
If your workflow just broke and you are staring at a Snaplet-shaped hole in your dev setup, here is the minimum viable path to get back to a working state without picking the wrong tool under time pressure.
- List the workflows that actually depended on Snaplet. Usually there are two or three: local dev onboarding, CI test data, and staging refreshes. Write them down. You do not need to replace every Snaplet feature — you need to restore these workflows.
- Identify the data each workflow needs. How many rows of which root tables? What recency window? What is genuinely PII? This is the information that will become your replacement tool's config, regardless of which tool you pick.
-
Inventory the anonymization rules you actually rely on. Pull up your old
snaplet.config.tsand list the columns that had explicit transforms. Most teams find that 80% of the rules are "mask emails, names, phone numbers" — which any serious replacement handles automatically. - Pick one workflow to migrate first. Local dev onboarding is usually the right starting point: it is the lowest stakes, has the fastest feedback loop, and exercises the full subset → anonymize → restore loop.
- Run the new tool alongside the old script for one sprint. Do not delete anything yet. Let one workflow prove itself before you rip out the fallback scripts that kept you running after the Snaplet shutdown.
- Expand to CI once local dev is stable. CI/CD test data is where the time savings compound fastest — a snapshot restore that takes seconds saves real engineer time on every PR.
- Automate snapshot refresh on a schedule. Weekly is a reasonable default. Without a refresh schedule, any replacement tool is just a different kind of stale dataset six months from now.
-
Delete the fallback scripts. Once the replacement has been running unattended for a few weeks, delete the
pg_dump-and-UPDATEscripts you wired up in the panic. Leaving them around means they get used again eventually, and now you have two parallel systems drifting apart.
The whole migration typically takes an afternoon for the CLI swap and one or two sprints to expand across local, CI, and staging. The hard part is usually the decision, not the execution.
Migrating from Snaplet to Basecut
The CLI migration is straightforward. If your team ran:
snaplet snapshot capture
snaplet snapshot restore
The Basecut equivalent is:
basecut snapshot create --config basecut.yml
basecut snapshot restore my-snapshot:latest --target $DEV_DB_URL
The conceptual mapping is close enough that most teams can get a working Basecut config in an afternoon. The main translation is from Snaplet's TypeScript transform functions to anonymize rules in YAML — and in most cases, anonymize: mode: auto handles the common fields automatically, so the config ends up shorter than what you had before.
One thing worth knowing: Basecut uses snapshot names with a version tag (my-snapshot:latest) rather than Snaplet's path-based restore syntax. The restore command takes a --target flag pointing at the destination database URL, which keeps source and destination separate and makes it explicit which database is being written to.
FAQ
Is Snaplet still available in 2026?
No. Snaplet shut down in 2024 and open-sourced their codebase. The hosted product is gone, the team no longer maintains it, and there is no support channel. The code is still on GitHub for anyone who wants to self-host, but there are no active maintainers or bug fixes.
What is the best alternative to Snaplet in 2026?
Basecut is the most actively maintained like-for-like alternative: same subset → anonymize → restore workflow, YAML config instead of TypeScript, and a free tier that covers small teams. The open-source Snaplet fork is viable if you can self-host and own the maintenance. Tonic.ai is a heavier enterprise option. pg_dump plus anonymization scripts is a stopgap that breaks quietly as schemas grow.
Can I migrate my Snaplet config directly to Basecut?
There is no automatic translator, but the conceptual mapping is close. Snaplet transform functions become Basecut anonymize rules in YAML. For most teams, anonymize: mode: auto handles the common PII columns (emails, names, phones, addresses) without explicit rules, which makes Basecut configs shorter than the Snaplet equivalents they replace. Most teams get a working Basecut config in an afternoon.
Is the open-source Snaplet fork still maintained?
The code is available, but there is no active upstream. New PostgreSQL versions, edge cases, and schema quirks are your problem to debug. It is a reasonable option if you have engineering bandwidth and want to keep your existing snaplet.config.ts intact. It is a poor fit if "set it and forget it" is a requirement.
How long does a Snaplet → Basecut migration usually take?
The CLI swap itself takes an afternoon: write a basecut.yml, run basecut snapshot create, run basecut snapshot restore, and verify the result against your app. Rolling it out across local dev, CI, and staging typically takes one or two sprints, mostly because each workflow needs to be validated independently before the old fallback scripts can be deleted.
Do I need to self-host Basecut the way I would self-host the Snaplet fork?
No. Basecut is a hosted product with a free tier for small teams, and snapshots can be stored either locally on your own machine or in cloud storage managed by Basecut. You install the CLI, point it at a read replica, and create your first snapshot without provisioning any infrastructure.
If you want to see whether Basecut fits your workflow before committing to a migration, the free tier is a reasonable place to start. No infrastructure to set up — install the CLI, point it at a read replica, and you can have a first snapshot in a few minutes.
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