Velocity dies in the queue
Twenty teams. Three staging slots. Every PR waits while someone else finishes, or ships on contaminated shared state.
Why Guepard DataOps
The data layer built for how you ship today
You parallelized builds, preview apps, and agent runs, then serialized them behind one persistent staging database. Guepard brings approachable, developer-first DataOps back: prod-identical clones in seconds, gone when the work is done.
Why ephemeralEverything else went ephemeral.
Everything else went ephemeral.
Your database didn't.
You parallelized builds, preview apps, and agent runs, then serialized them behind one persistent staging database. That mismatch is why shipping slowed down even as your toolchain got faster.
The modern stack
- ComputeEphemeral
- ContainersEphemeral
- Preview environmentsEphemeral
- Your databaseStill persistent
Guepard closes the gap: git-like branches for data, at the speed your pipelines already expect.
The real cost
3+ days 45 min CI lies to you
Pipelines run in parallel but hit the same stale DB. Migrations pass in CI, break in prod. Agents can't experiment without blocking humans.
$$$ Cost compounds quietly
Orphaned clones, oversized persistent staging, and ops tickets to provision/teardown, all because data envs weren't designed to be disposable.
Persistent staging made sense when releases were monthly. With daily deploys, agent swarms, and clone-per-PR CI, data environments have to be as disposable as the code that uses them.
Change the rhythm,
not the roadmap.
Ephemeral data isn't a feature checklist. It's what happens when you stop treating databases like scarce furniture the whole org has to share — and start treating them like compute: spin up, use, throw away.
Negotiate access. Serialize work. Hope nothing drifted.
- Staging is negotiated in Slack and on a shared calendar
- Agents wait until a human finishes with the only copy
- CI greenlights against data that drifted weeks ago
- Releases depend on restore windows and platform heroics
- Cost grows with always-on copies nobody remembers to kill
Self-serve data. Parallel by default. No one asks permission.
- Every PR, agent, and pipeline job claims its own database
- Parallel work stops colliding on the same stale schema
- Migrations get exercised on production-shaped data before merge
- Environments disappear when the work ends — no cleanup theater
- Platform shifts from gatekeeper to guardrails, not ticket queue
The shift isn't learning another platform. It's ending the daily negotiations around shared state — so shipping speed matches the rest of your stack.
Why invest
Self-serve speed, expert support, control you can prove.
Built for builders, not gatekeepers
You don't want a six-month data platform project or a shared staging queue owned by ops. You want a precision tool: snapshot once, branch per PR or agent, tear down when done from CLI, API, or CI.
Support from people who ship data envs
When restore scripts fail at 2am, forum threads won't save your release. Guepard is built by teams who've run production DataOps, with direct access to engineers who understand branching, masking, and your engines.
Resilience you can audit
GFS is open source and battle-tested. Self-host in your VPC, keep data in-region, and prove isolation to security, not a black-box restore from last Tuesday's backup.
Compare
Traditional data environments vs. Guepard DataOps
Proof
Built to earn trust at every scale
Open-source GFS core
Copy-on-write branching engine on GitHub: inspect the primitives, extend the stack, or run fully self-hosted.
Measured in seconds
Terabyte-scale forks in under 6s. Hundreds of parallel branches without duplicating storage upfront.
Your perimeter, your rules
VPC deploy, RBAC, masking policies that travel with every branch, built for regulated teams from day one.
CI-native from day one
GitHub Actions, webhooks, REST: clone on PR open, inject DATABASE_URL, tear down on merge. No bespoke provisioning scripts.
Simple to start. Intuitive to extend.
Connect production read-only, snapshot once, branch everywhere your stack runs, then let TTL and CI tear environments down for you.
