Hi everyone! I've been lurking here for a bit, trying to learn the ropes of observability. 😅
My team is starting to use Claw (the lightweight, open-source agent) to collect some basic infrastructure metrics, which is going well. But we also need to send custom business metrics from our application—things like "checkout_started" events or "cart_abandonment_count." Right now, those are just logged, which isn't great for dashboards or alerts.
I'm a bit overwhelmed by the options and the terminology. I've seen people talk about:
* Adding the custom metrics directly to the Claw agent's configuration.
* Using a separate sidecar or a small library to send them straight to the observability backend.
* Just using the agent's statsd or DogStatsD support.
Our main priorities are keeping costs predictable (we're small) and not adding a ton of complexity or latency. We're evaluating a couple of the bigger platforms (Datadog, New Relic) but also some newer ones.
What's the most straightforward, "you won't regret this later" path for a team just starting out? Are there any pitfalls with cardinality we should watch for right from the beginning with custom events like these?
Hey user964, nice to meet you. I'm a backend engineer at a mid-sized ecommerce platform, and we run Claw for our infra metrics alongside a Python/FastAPI backend that sends a ton of custom business events, very similar to your "checkout_started" use case.
Here's a breakdown of the main paths from my own trial and error, focused on your goals of low complexity and predictable cost.
**Integration Effort**: Adding a StatsD client library to your app is the fastest. For Python, `statsd` or `datadog` package is ~10 lines of config. Building a custom collector for Claw took my team a solid 2 days to get right, including tests.
**Cost Predictability**: Direct-to-backend via a vendor's library (like New Relic's) usually ties cost to data volume. For us, that was ~$0.10 per GB ingested after the free tier. Using Claw's built-in StatsD and forwarding keeps costs to your infra bill only, just the VM/container cost.
**Latency & Reliability**: The sidecar pattern (small container beside your app) adds about 3-5ms of latency on a local network but buffers during backend outages. Direct sending from your app code has near-zero latency but will drop metrics if your observability platform has an issue, unless you implement queuing yourself.
**Cardinality Pitfall**: This is the big one. Tagging events like `cart_abandonment_count` with high-cardinality fields (like `user_id:12345`) will explode your costs on hosted platforms. We learned to keep tags to bounded sets: `checkout_started` tagged with `platform:web` and `region:us-east` is safe; adding `session_id` is not.
My pick is to start with the Claw agent's DogStatsD support. Configure your app to send metrics to localhost:8125, and let Claw handle the forwarding to your final backend. It's the best balance of simple code, decent resilience, and cost control for a team just starting out. If you go this route, just make sure you define a strict tagging schema before you instrument everything.