alt Hacker NewsHow much of my observability data is waste?
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Reminded me a note I heard about backups. You don't want backups, it is a waste of time, bandwidth and disk space, by far most if not all of it will end being discarded without being ever used. What you really want is something to restore from if anything breaks. That is the cost that should matter to you. What if you don't have anything meaningful to make a restore from?
With observability is not the volume of data, time and bandwidth used on it, is being able to understand your system and properly diagnose and solve problems when they happen. Can you do that with less? For the next problem that you don't know yet? If you can't because of lack of information or information you didn't collect, then spending so much may be was not enough.
Of course that there are more efficient (towards the end result) ways to do it than others. But having the needed information available, even if it is never used, is the real goal here.
Hard agree on the data waste, noise to signal ratio is typically very high and processing, shipping and storing all of that data costs a ton.
Previous start-up I worked on (jClarity, exited to Microsoft) mitigated much of this by having a model of only collecting the tiny amount of data that really mattered for a performance bottleneck investigation in a ring buffer and only processing / shipping and storing that data if a bottleneck trigger occurred (+ occasional baselines).
It allowed our product at the time (Illuminate to run at massive scale without costing our customers an arm and a leg or impacting their existing infrastructure. We charged on the value of the product reducing MTTR and not on how much data was being chucked around.
There was the constant argument against approach of always on observably or “collect all data JIC”, but with a good model (in our case something called the Java Performance Diagnostic Method) we never missed having the noise
Kudos to Ben for speaking to one of the elephants in the room in observability: data waste and the impact it has on your bill.
All major vendors have a nice dashboard and sometimes alerts to understand usage (broken down by signal type or tags) ... but there's clearly a need for more advanced analysis which Tero seems to be going after.
Speaking of the elephant in room in observability: why does storing data on a vendor cost so much in the first place? With most new observability startups choosing to store store data in columar formats on cheap object storage, think this is also getting challenged in 2026. The combination of cheap storage with meaningful data could breathe some new life into the space.
Excited to see what Tero builds.
As Ops (DevOps/Sysadmin/SREish) person here, excellent article.
However, as always, the problem is more political than technical and those are hardest problems to solve and another service with more cost IMO won't solve it. However, there is plenty of money to be made in attempting to solve it so go get that bag. :)
At end of day, it's back to DevOps mentality and it's never caught on at most companies. Devs don't care, Project Manager wants us to stop block feature velocity and we are not properly staffed since we are "massive wasteful cost center".
Ben, you probably don't remember me but you hired me ages ago to help out with the Python client for Timber. Just want to say thanks for that opportunity — it's been amazing to watch you guys succeed.
Also, I've ended up being responsible for infra and observability at a few startups now, and you are completely correct about the amount of waste and unnecessary cost. Looking forward to trying out Tero.
One of the problems described here seems to be that the people building the dashboards aren’t the ones adding the instrumentation. Admittedly I’ve only worked on one project that was all in on telemetry instead of using log analysis. And even that one had one foot in Splunk and one in Grafana, but I worked there long enough to see that we mostly only had telemetry for charts at least someone on call used regularly. I got most of them out of Splunk but that wasn’t that hard. We hadn’t bought enough horsepower from them that it didn’t jam up if too many people got involved in diagnosing production issues.
Occasionally I convinced them that certain charts were wrong and moved them to other stats to answer the same question, and some of those could go away.
I also wrote a little tool to extract all the stats from our group’s dashboard so we could compare used to generated and I cut I’d say about a third? Which is in line with his anecdote. I then gave it to OPs and announced it at my skip level’s staff meeting so other people could do the same.
You should not even need a regex; no serious logging system should be emitting formatted strings, JSON, etc. as a storage format. You are immediately incurring on the order of a 5-100x log size and 5-100x log performance overhead with any serialization format that poor. A properly performant logging system should be able to generate on the order of 100 million logs per second per core (assuming relatively small payloads).
At a minimum you should be using message template [1] serialization which is trivial to implement transparently on any logging system/statement with zero code changes to the emitter itself.
Any filtering done on top of that would then just be parsing structured data which is way easier than a regex, though of course that is somewhat beside the point of the article.
It's so funny, I've never done a cost-benefit analysis of having "good monitoring" and then still not being able to figure out what broke and needing to pull in someone who doesn't need the monitoring at all because they built the thing.
We store the data because we might need to know it. We only discover we didn’t need to know it once we’ve finished knowing it.
Observability vendors massively overcharge. I got tired of paying an ever-increasing amount of money per month, so my solution now is a self-hosted SigNoz instance on a cheap Hetzner box. It costs me $30/month and I can throw large quantities of data at it and it doesn’t break a sweat.
I spent a decade in observability. Built Vector, spent three years at Datadog. This is what I think is broken with observability and why.
This pitch seems ok to people using simple log aggregation tools or metric tools that have to be wary of tag cardinality
But how does it compare to an actual modern observability stack built on a columnar datastore like Honeycomb?
Lurked on HN for years, and finally a post that made me excited enough to create an account.
First of all, thanks for you (and the team’s) work on Vector. It is one of my favorite pieces of software, and I rave about it pretty much daily.
New endeavor sounds very exciting, and I definitely can relate to the problem. Are there plans to allow Teri to be used in on-premises environments and self-hosted?
Thank you and good luck!
> You run observability at your company. But really, you're the cost police. You wake up to a log line in a hot path, a metric tag that exploded cardinality. You chase down the engineer. They didn't do anything wrong, they're just disconnected from what any of this costs.
Somebody didn’t math right when calculating if moving off hostedgraphite and StatsD was going to save us money or boil us alive. We moved from an inordinate number of individual stats with interpolated names to much simpler names but with cardinality and then the cardinality police showed up and kept harping on me to fix it. We were the user and customer facing portion of a SaaS company and I told them to fuck off when we were 1/7 of the overall stats traffic. I’d already reduced the cardinality by 400x and we were months past the transition date and I just wanted to work on anything that wasn’t stats for a while. Like features for the other devs or for our customers.
Very frustrating process. I suspect there’s a Missing Paper out there on how to compress stat cardinality out there somewhere. I’ve done a bit of work in that area but my efforts are in the 20% range and we need an order of magnitude. My changes were more about reducing the storage for the tags and reduced string arithmetic a bit in the process.
Just want to say thanks for creating Vector. We use it heavily at Zerodha and wrote about our setup here: https://zerodha.tech/blog/logging-at-zerodha/
It replaced both Filebeat and Logstash for us with a single binary that actually has sane resource usage (no more JVM nightmares). VRL turned out to be way more powerful than we could imagine - we do all our log parsing, metadata enrichment, and routing to different ClickHouse tables in one place. The agent/aggregator topology with disk buffering is pretty dope.
Genuinely one of my favorite pieces of infra software. Good luck with Tero.
>Turns out you can compile tens of thousands of patterns and still match at line rate.
Well, yea, sort of the magic of the regular expression <-> NFA equality theorem. Any regex can be converted to a state machine. And since you can combine regexes (and NFAs!) procedurally, this is not a surprising result.
> I ran it against the first service: ~40% waste. Another: ~60%. Another: ~30%. On average, ~40% waste.
I'm surprised it's only 40%. Observability seems to be treated like fire suppression systems: all important in a crisis, but looks like waste during normal operations.
> The AI can't find the signal because there's too much garbage in the way.
There's surprisingly simple techniques to filter out much of the garbage: compare logs from known good to known bad, and look for the stuff thats' strongly associated with bad. The precise techniques seem bayesian in nature, as the more evidence (logs) you get the more strongly associated it will appear.
More sophisticated techniques will do dimensional analysis -- are these failed requests associated with a specific pod, availability zone, locale, software version, query string, or customer? etc. But you'd have to do so much pre-analysis, prompting and tool calls that the LLM that comprise today's AI won't provide any actual value.