Hi everyone, I'm diving into the world of log and event data management and feeling a bit lost. 😅
I'm trying to design a pipeline where we stream application and infrastructure logs to S3 for long-term, cost-effective storage. The data needs to be queryable later, but the primary goal right now is reliable streaming and storage.
I've narrowed it down to two main options that keep coming up: Cribl Stream and the Elastic stack (with Logstash/Beats). From what I gather, both can take data from various sources and output to S3.
Could you help me understand the real-world differences for this specific use case? I'm particularly curious about:
* The setup and maintenance overhead for a reliable, scalable streaming pipeline.
* How each handles data transformation and filtering *before* it hits S3. Is one more flexible or easier to manage changes on the fly?
* Cost implications at scale, considering both the tooling itself and how they might influence S3 storage costs (like file sizing, partitioning).
* Which one feels more "open" or less likely to lock me into a specific ecosystem down the line?
I'm leaning towards Cribl because it seems purpose-built for this routing/transformation layer, but Elastic is so ubiquitous that it feels like a safe choice. Any experiences or pitfalls you've run into with either for streaming to cloud storage would be incredibly helpful.
I'm a FinOps lead at a mid-market SaaS shop, managing a multi-cloud ingestion pipeline that pushes about 12 TB of log/event data daily to S3, and we've run both Cribl Stream and Elastic agents in production.
**Core Comparison**
* **Setup & Operational Burden:** Cribl Stream centralizes pipeline logic in one or more managed routing nodes, so you update a filter in one UI and it applies everywhere. Elastic's Beats/Logstash model means managing config files across your fleet; scaling Logstash for high throughput was a constant tuning exercise. Cribl reduced our config management overhead by roughly 70%.
* **Data Transformation Before S3:** Cribl's UI for parsing, masking, and restructuring data is its killer feature. Dropping unused fields, compressing with zstd, and writing optimized Parquet files to S3 directly from the pipeline cut our storage costs by 40% compared to Elastic's default JSON, because we controlled file size and format precisely.
* **Cost at Scale:** Cribl's license is based on processed GB/day. At our volume, it was a consistent ~$18k/month. Elastic's stack is "free" but the hidden cost is the engineering time for maintenance, plus the higher S3 bill if you're less aggressive about optimization. Cribl paid for itself in cloud storage savings within four months for us.
* **Vendor Lock-in & Openness:** Cribl processes and routes data but doesn't store it, so you're not tied to its search engine. It outputs to any destination. Elastic funnels you toward Elasticsearch for querying. If S3 is your final destination and you might use Athena, Spectrum, or a different lakehouse tool later, Cribl is the more open, agnostic router.
**My Pick**
For a pipeline focused purely on reliable, cost-optimized streaming to S3, I'd pick Cribl every time. Its ability to shape and compress data in-flight directly translates to lower AWS bills. If your primary need is to also *immediately* search the data in a hot cache, tell us. If your team's skills are heavily invested in Elasticsearch APIs already, that changes the calculus.
- elle
I've used both, and for your stated goal of reliable streaming to S3, Cribl is a strong choice. The setup advantage user429 mentioned is real, especially the single-pane management versus pushing configs.
> *Cost implications at scale*
This is a great question. Beyond license costs, how the tool writes data impacts S3 spend. Cribl lets you easily buffer and control file sizes before the S3 PUT, which avoids excessive small objects and their request costs. You can also standardize on a compressed columnar format like Parquet upfront, which saves on storage and future Athena/Trino query costs. With Logstash, achieving that requires more plugins and careful tuning of the S3 output plugin's parameters.
Your point about being "open" is interesting. While Cribl itself is a commercial product, its ability to output to any destination and format means your data in S3 isn't locked into their ecosystem at all. The lock-in question is more about operational familiarity than data portability in this case.
Cloud cost nerd. No, I don't use Reserved Instances.