Our free tier doesn't include the anomaly/error detection (noted on the site, we can make it more clear though). And your numbers do add up! That's why you can't just run all your logs through an LLM.
Aggregated (+ simplified) versions of your logs + flagged anomalies get passed through our LLMs
I assume this is most in regards to our anomaly/error detection! Deterministic rules for flagging anomalies + human feedback help in adapting our flagging system accordingly. So hallucinations won't directly impact flagged anomalies. The rules (patterns) we generate are on the stricter end, so they err on the side of flagging more.
However, the rules themselves aren't deterministically generated (and therefore prone to LLM hallucinations). To address this, we currently have a simpler system that lets you mark incorrectly flagged anomalies so they can be incorporated into our generated rules. There's room to improve that we're actively working on: exposing our generated patterns in a human-digestible manner (so they can be corrected), introducing metrics and more data sources for context, and connecting with a codebase.
We offer competing core functionality in terms of storage and search, but we’re also focused on intelligence: real-time anomaly detection, semantic log analysis, and natural language search.
Would recommend the demo video and playground environment we linked above! Feel free to reach out at [email protected] if you’d like to learn more
Yep we have an on-prem offering as well, got similar notes from folks before!
> What stops me from building my own logger that sends a request to write a record to a DB and later asks an LLM what it means ?
Great question! The main limitation over brute force is the sheer volume of noise, and therefore relevant context. We tried this and realized it wasn't working. From a numbers perspective, at even just 10s of GBs/day scale of data (not even close to enterprise scale), mainstream LLMs can't provide the context windows you need for more than a few minutes of operational data. And larger models suffer from other factors (like attention diffusion / dilution & drift).
> I see the landing page. The pricing should be clear though “ Contact Us” is scary.
Noted!
Yep, but it's sometimes a compromise people may be unwilling to make. Too often I hear (and have seen via DD customers) horror stories about initiatives to fix observability squashed by teams in hopes of shipping.
Moving fast has it's downsides and I can't say I blame people for deprioritizing good logging practices. But it does come back to bite...
Though as a caveat, you don't always have control over your logs -- especially with third party services, large but fragmented engineering organizations, etc. -- even with great internal practices, there's always something.
On another note, access to codebase + live logs gives room to develop better auto-instrumentation tooling. Though perhaps cursor could do a decent enough job at starting folks off
> I don't understand, what about that is a "silent failure"?
Silent failures can be "allowed" behavior in your applications that aren't actually labeled as errors but can be irregular. Think race conditions, deadlocks, silent timeouts, or even just mislabeled error logs.
> in order for your product to even know about it, wouldn't I need to write a log message for every single record update?
That's right, and this may not always feasible (or necessary!), but if your application can be impacted by errors like these, perhaps it may be worth logging anyway.
> the general question I have with any product that's marketing itself as being "AI-powered" - how do hallucinations get resolved?
> and if my architecture allows two microservices to update the same row in the same database...maybe it happening within 50ms is expected?
> if I ask your product "what caused such-and-such outage" and the answer that comes back is incorrect, how do I "teach" it the correct answer?
For these concerns, human-in-loop feedback is our preliminary approach! We have our own internally running to account for changes and false errors, but having explanations from human input (even as simple as "Not an error" or "Missed error" buttons) is very helpful.
> when that happens I can walk through their thought process and analysis chain with them and identify the gap that led them to the incorrect conclusion. often this is a useful signal that our system documentation needs to be updated, or log messages need to be clarified, or a dashboard should include a different metric, etc etc.
Got it, I imagine it'll be very helpful for us to display our chain of thought from our dashboards too. Great feedback, thank you!
Agreed! Metrics are a high priority, especially since working to increase the available context around each anomaly we flag.
Logs were a natural starting point because that’s where developers often spend a significant amount of time stuck reading & searching for the right information, manually tracking down issues + jumping between logs across services. In a way, just finding & summarizing relevant logs for the user gave people an easier time debugging.
But metrics will introduce more dimensions to establish baseline behavior, so we're pretty excited about it too.
Very relatable experience with log diving, feels very much like a needle-in-haystack problem that gets so much harder when you're not the only one who contributed to the source of errors (often the case).
As for the skepticism with LLMs stumbling around raw logs: it's super deserved. Even the developers who wrote the program often refer to larger app context when debugging, so it's not as easy as throwing a bunch of logs into an LLM. Plus, context window limits & the relative lack of "understanding" with increasingly larger contexts is troublesome.
We found it helped a lot to profile application logs over time. Think aggregation, but for individual flows rather than similar logs. By grouping and ordering flows together, it's bringing the context of thousands of (repetitive) logs down to the core flows. Much easier to find when things are out of the ordinary.
Still a lot of improvements in regards to false positives and variations in application flows.
Absolutely! Java bindings are on our radar. Any specific use cases / implementations you'd like to see? In the meantime, we do also support a couple off-the-shelf collectors that should already support Java applications!
Aggregated (+ simplified) versions of your logs + flagged anomalies get passed through our LLMs