Most data teams have a context problem: alerts are firing, pipelines are instrumented, but when something breaks, engineers still scramble to understand whether it matters, who owns it, and what to fix first. Sifflet adds lineage, ownership & downstream impact context to every data quality signal, so teams can move from reacting to deciding. Connect your real stack, run monitors with full lineage context & experience what business-aware data observability actually means for your pipelines.
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Maker
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Hey PH 👋
I’m Gaby from Sifflet. We’ve been building business-aware data observability for a few years now, and today we’re launching something we’ve been asked for constantly: a free trial.
Here’s the honest context:
Data engineers kept telling us the same thing — “we need to test it before we can recommend it internally.” That’s fair. So we built a 14-day self-serve trial. No demo request, no procurement cycle to start.
What makes Sifflet different from other observability tools isn’t detection. It’s context. When an alert fires, you immediately see the lineage, the ownership, and what’s affected downstream. That’s what lets you decide what to fix first — instead of scrambling.
A few things worth knowing:
- The trial has real limits: 100 monitors, 10 integrations, 14 days. We’d rather be upfront.
- AI features are metadata-first. Your actual data values never touch our systems.
- One tenant per company — if a colleague signs up, they’ll be directed to your instance.
Happy to answer any questions here. We read every comment.
Gaby