Just made the switch from Logstash to Cribl for our log pipeline. Wanted to share my first impressions, since I was deep in Logstash configs for years.
The good stuff is real. The UI is a game-changer for seeing the data flow live. Building pipelines feels more like connecting blocks than writing Ruby conditionals. I got a simple parser routing to two destinations running in maybe 20 minutes. The built-in functions for parsing common formats saved me so much time.
But the painful part hit when I needed custom logic. In Logstash, I'd just drop into the filter{} and write Ruby. In Cribl, I had to write a JavaScript function inside a "Eval" operator. It's powerful, but debugging feels harder. The documentation is vast, but finding the exact syntax for something niche can be a hunt. I also miss the sheer volume of community plugins Logstash had.
Overall, it's a trade-off. Huge win on simplicity and speed for standard pipelines, but you give up some of that raw, code-level flexibility. Curious if others have hit similar walls or found good workarounds.
Hey OP, I'm a senior data engineer at a mid-sized e-commerce company, and I've run both Logstash and Cribl in production pipelines, plus some Fivetran for specific app connectors.
Here's a breakdown based on my experience:
1. **Cost Structure and Scale**: Cribl is priced per volume processed (GB/day) and per worker node. At my last shop, it was a clear step up in budget from our open-source Logstash setup. For a 1TB/day pipeline, you're looking at a mid-five-figure annual commitment. Logstash is "free," but you pay with engineering hours. Cribl starts making financial sense when the number of pipelines or destinations you manage would require multiple dedicated Logstash maintainers.
2. **Operator vs. Developer Experience**: Cribl's UI wins for operations and initial setup. You can onboard a new engineer and have them building a pipeline in an afternoon. But for deep, custom transformations, you're now a JavaScript developer in a constrained editor. In Logstash, I could write a complex Ruby filter to handle a malformed CSV with a rescue block. In Cribl, that same logic required a 50-line Eval function that was harder to unit test locally before deploying.
3. **Performance and Resource Overhead**: In my environment, a well-tuned Logstash instance (with JVM heap set right) could push about 15-20k events/sec per node on our log stream. Cribl's worker nodes, which are also JVM-based, handled a similar volume but used more memory overall for the same throughput, likely due to the UI and management layer. The benefit was Cribl's built-in routing and sampling cut our egress costs by about 30% by not sending debug logs to our expensive long-term store.
4. **Vendor Support and Community**: When we had a Cribl routing issue, their support was responsive and solved it in a day. You're paying for that. The Logstash community is massive for plugins - there's probably a filter for that weird legacy syslog format. With Cribl, you either find a pre-built Pack or write it yourself in JavaScript. The plugin ecosystem is growing but isn't as deep, so check if your niche sources/destinations are already covered.
My pick is Cribl, but only if you have standard log formats and multiple high-cost destinations (like Splunk, Snowflake, S3) where its routing and reduction features will directly cut your bill. If your need is a single, complex parsing job to one destination where you need to write custom code, a maintained Logstash pipeline is still simpler. To make the call clean, tell us your daily volume and how many distinct source formats you have that aren't JSON, syslog, or CSV.
ship it
Great point about the operator vs. developer experience. That Eval operator is a blessing and a curse. I found it helpful to prototype the custom JS logic in a proper editor first, then paste it in. Saves so much frustration with the constrained UI.
Your cost breakdown hits home. For us, the tipping point wasn't just pipelines but managing pipeline *versions* across teams. Cribl's UI made it way easier for folks to self-serve, which freed up our core team's time and justified the cost. Did you see any change in how many pipeline-related tickets your team got after the switch?
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Totally feel you on the missing community plugins. That was a big adjustment for me too. The built-in functions are great for standard stuff, but when you need something really specific, you're kind of on your own.
One workaround I found for the Eval operator pain was using the GitHub integration. You can write and version control your custom JavaScript functions in a repo, then pull them in as lookup files. Makes debugging and editing way less painful than in the UI box.
Have you tried the Cribl community Slack? It's been a lifesaver for finding syntax for those niche use cases. Way faster than digging through docs sometimes.
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