[HN Gopher] Launch HN: Sift Dev (YC W25) - AI-Powered Datadog Al...
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Launch HN: Sift Dev (YC W25) - AI-Powered Datadog Alternative
Hi HN! We're Kaushik and Ishir. We're building SiftDev
(https://app.trysift.dev/docs), an intelligent logging tool that
understands your observability data in real time, automatically
identifies anomalies, and lets you interact with your logs through
natural language queries. Here's a demo video:
https://www.youtube.com/watch?v=uQ-TTdiu3fc&t=20s, and there's a
demo playground you can try out here: https://app.trysift.dev/. We
used to work on product and engineering at Datadog and Splunk. We
saw how even teams using these industry-leading tools were
struggling to effectively interpret and use their logging data. The
sheer volume of logs overwhelmed experts and newcomers alike,
making it difficult to quickly identify meaningful issues or
patterns. Despite powerful indexing and search capabilities,
developers still had to manually piece together context from
different logs, dashboards, and sources--a tedious and error-prone
process. The "noisy logging" problem--that is, the gap between
overwhelming amounts of raw log data and insights people can act on
--ultimately is a gap between machines (which generate all this
data) and humans (who want and need the insights). SiftDev is built
to bridge that gap and to automate the tedious, manual aspects of
debugging and observability. In marketing-speak: "humans should
never have to look at a log again!" We think people should interact
with their data in terms that make sense on a human level. What
makes SiftDev different is its understanding of application context
over time. While traditional tooling typically lets developers
analyze logs in isolation, or with minimal surrounding context,
SiftDev builds comprehensive profiles of your application's normal
behavior patterns. This awareness allows us to understand what's
truly abnormal versus what might appear unusual in a single
snapshot but is actually expected behavior for your specific
application. SiftDev applies semantic analysis and profiling to
understand your application's logging behavior holistically.
Instead of relying solely on manual search, Sift identifies core
application processes, automatically detects patterns, and surfaces
anomalies, including clear explanations and context. Here are some
examples of what this can look like in practice: Identify core
processes: SiftDev instantly recognizes your payment workflows--
like authorization, capture, and refunds--without manual tagging.
Detect performance patterns: SiftDev learns your nightly batch job
typically handles 10,000 records in 45 minutes, establishing a
clear baseline. Surface hidden anomalies: SiftDev flags silent
failures, such as two microservices updating the same record within
50ms--issues normally hidden by routine logs. You can then
directly ask your logs questions like, "What's causing errors in
our checkout service?" or "Why did latency spike at 2 AM?" and
immediately receive insightful, actionable answers that you'd
otherwise manually be searching for. We'd love for you to test out
our product via our demo playground at https://app.trysift.dev/!
It's a slightly less functional version of our platform but shares
a lot of the core features. Note: we do need users to sign up to do
this but waitlist is optional (of course). We'd love your
feedback, thoughts, and experiences dealing with logging and
observability challenges!
Author : Akula112233
Score : 77 points
Date : 2025-03-11 17:00 UTC (1 days ago)
| dang wrote:
| [stub for offtopicness]
| kadomony wrote:
| The marketing design approach feels very off to me. You barrage
| me with an annoying scrolling marquee showing me the most
| abstract, unrecognizable logos telling me I should trust you
| because they do. 10+ companies on board feels rather small.
|
| You said AI-driven analysis to identify logs, but I'm already
| skeptical of AI doing tasks like this, and you obfuscate it
| further by not actually showing me how it works, just another
| generic abstract marketing design graphic.
|
| I dunno. It just seems like vaporware-as-a-service from the
| design vibes.
| dang wrote:
| Early-stage startups often have websites that are little more
| than landing pages. That's because a full commercial website
| isn't in their critical path yet--first they need to build
| their product and attract early users, who don't typically
| come in through general web traffic.
|
| That's one reason why Launch HNs usually include a demo
| video. That's the link you should be clicking on if you want
| to see these guys' product. If you do that, you'll see that
| it isn't vaporware.
|
| We also advise startups doing Launch HNs to provide a link
| for users to try the product (preferably without a signup
| gate, but that's not always doable). There's such a link in
| the text above as well.
|
| I suppose one way to avoid complaints about stub websites
| would be not to link to them at all--but then other comments
| would say "why would I trust you, you don't even have a
| website"!
|
| Edit: I've replaced https://runsift.com/ with
| https://app.trysift.dev/docs in the text above. Perhaps that
| will help.
| Velorivox wrote:
| I want to jump in here and post this Launch HN form [0].
| Obviously do not submit it if you are not a YC startup, but
| the questions on there are very helpful in terms of
| thinking about how to post about your startup on HN and
| elsewhere.
|
| [0] https://docs.google.com/forms/d/1pRMkNiD-FKjYL-
| La5JWMwwrcWsp...
| dang wrote:
| There's also https://news.ycombinator.com/yli.html, which
| is the guide for YC startups who want to launch on Hacker
| News. The formal mechanism is YC-only but the principles
| apply more broadly.
| jascination wrote:
| I'm not in YC, but I want to launch my startup here as
| it's relevant to the audience. Can I go through a process
| like this to coordinate with you for a launch, or should
| we just follow the guidelines, make a submission and hope
| for the best?
| Velorivox wrote:
| You would have to do a "Show HN", the YC launch (post to
| the front page) is only for YC startups. You can
| certainly try and go through the process to do a "Launch
| HN" - but it would start with applying to YC.
|
| Apart from show vs launch I think following the
| guidelines and hoping for the best is the norm. Launch HN
| is nice to get a one-time boost but it doesn't confer any
| long-term special treatment on your post afaict.
| paularmstrong wrote:
| What's not recognizable about Duck, Square, Triangle,
| Asterisk, C, two different cubes, and the letter 'n'?
|
| These, coupled with the random number generator to claim how
| many logs they're processing makes me wonder if the entire
| product is just AI generated slop.
| graphman wrote:
| Is it common practice to display fake realtime numbers on the
| homepage?
|
| let storedNumber = getCookie("countingNumber"); let startNumber
| = storedNumber !== null ? storedNumber :
| Math.floor(Math.random() * (10300000 - 10000000 + 1)) +
| 10000000; let currentNumber = startNumber; function
| updateNumber() { let randomIncrement = Math.floor(Math.random()
| * (275 - 101 + 1)) + 101; currentNumber += randomIncrement;
| element.textContent = formatNumber(currentNumber);
| setCookie("countingNumber", currentNumber, 7); // Save number
| in cookie for 7 days } element.textContent =
| formatNumber(currentNumber); setInterval(updateNumber, 1000);
| Akula112233 wrote:
| Ah! That was a leftover from the initial dev version of our
| website. I've taken it out now. Thank you!
| Jeslijar wrote:
| Hey, maybe you can have a better hiring practice than datadog
| with a 5 question test where if you get a single answer wrong
| in even the smallest of ways you get disqualified from getting
| a job with them for 6 months.
|
| I'm guessing they lost a wealth of great talent due to this
| test on how to support a platform that they give to fresh off
| the street applicants rather than having even a modicum of
| training about their product. They want you to study it for
| free, probably as a marketing tactic - but also so they don't
| have to pay to train employees. it's great like cancer.
|
| Disclaimer: I have never applied to a role with datadog, nor
| interviewed with them. Just had multiple friends complete the
| process with mixed results. Seems like you need to put in ~two
| full weeks of self directed study to pass their on site
| interview 'exam' where they don't tell you about the exam being
| 100% or fail (but it is!)
| waffletower wrote:
| Java bindings would be welcomed by many.
| Akula112233 wrote:
| 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!
| nextts wrote:
| Funny I was thinking this week logging needs some magic.
|
| Log diving takes a lot of time especially during some kind of
| outage/downtime/bug where the whole team might be watching a
| screen share of someone diving into logs.
|
| At the same time I am sceptical about "AI" especially if it is
| just an LLM stumbling around.
|
| Understanding logs is probably the most brain intensive part of
| the job for me, more so than system design, project planning or
| coding.
|
| This is because you need to know where the code is logging,
| imagine code paths in your head and you constantly see stuff that
| is a red herring or doesn't make sense.
|
| I hope you can improve this space but it won't be easy!
| Akula112233 wrote:
| 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.
| ohgr wrote:
| The best way to improve this is to just generate decent useful
| and actionable logs. Sifting through a trash heap is where the
| problem is. No magic will suddenly turn that trash into gold.
|
| You have to do this at the inception of the software you're
| building rather then strap it on the donkey when something
| breaks (the usual way).
| Akula112233 wrote:
| 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
| bmurphy1976 wrote:
| This is part of hardening a system for production. Making it
| easy to operate:
|
| * Make sure the logs are actionable
|
| * Make sure the logs are readable
|
| * Make sure you are collecting operational metrics
|
| * Make sure the metrics are useful
|
| * Make sure you have error handling
|
| * Make sure you have alerting
|
| * Make sure you document how to support the application
|
| * Make sure you have knows and levers you can pull in an
| emergency to change the systems behavior or fix things
|
| * Make sure you have vetted the system for security issues
|
| etc.
| cthuen wrote:
| Disclaimer: I'm a founder at Gravwell, a log analytics startup
|
| I agree, even when applicable LLMs are relegated to analyzing
| subselected data, so logs have to go somewhere else first. I
| think understanding logs is brain intensive because it can be a
| tricky problem. It gets easier with good tools, but often those
| tools are the kind that need to be used to build something else
| that solves the problem, rather than solve the problem
| themselves (e.g. building a good query + automation). I think
| LLMs can get better at creating the queries which would help a
| lot.
|
| We started Gravwell to try bring some magic. It's a schema-on-
| read time-series data lake that will eat text or binary and
| comes in SaaS or self-hosted (on-prem). We built our backend
| from scratch to offer maximum flexibility in query. The search
| syntax looks like a linux command line, and kinda behaves like
| one too. Chain modules together to extract, filter, aggregate,
| enrich, etc. Automation system included. If you like Splunk,
| you should check us out.
|
| There's a free community edition (personal or commercial use)
| for 2GB/day anon or 14GB/day w/ email. Tech docs are open at
| docs.gravwell.io.
| vardaro wrote:
| Neat idea. Why logs, and not metrics too? You can characterize an
| accurate "baseline" system behavior through a combination of
| system level and userspace metrics. This profile would offer more
| depth than what you'd otherwise piece together with userspace
| logs.
| Akula112233 wrote:
| 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.
| vardaro wrote:
| I tend to use logs the least when debugging production
| issues. I realize that's a personal anecdote, so I see your
| point.
| evil-olive wrote:
| > SiftDev flags silent failures, such as two microservices
| updating the same record within 50ms
|
| I don't understand, what about that is a "silent failure"?
|
| in order for your product to even know about it, wouldn't I need
| to write a log message for every single record update?
|
| and if my architecture allows two microservices to update the
| same row in the same database...maybe it happening within 50ms is
| expected?
|
| that could be an inefficient architecture for sure, but I'm
| confused as to whether your product is also trying to give me
| recommendations about "here's an architectural inefficiency we
| found based on feeding your logs to an LLM"
|
| > You can then directly ask your logs questions like, "What's
| causing errors in our checkout service?" or "Why did latency
| spike at 2 AM?" and immediately receive insightful, actionable
| answers that you'd otherwise manually be searching for.
|
| the general question I have with any product that's marketing
| itself as being "AI-powered" - how do hallucinations get
| resolved?
|
| I already have _human_ coworkers who will investigate some error
| or alert or performance problem, and come to an incorrect
| conclusion about the cause.
|
| 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.
|
| 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?
| Akula112233 wrote:
| > 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!
| evil-olive wrote:
| > Think race conditions, deadlocks, silent timeouts, or even
| just mislabeled error logs.
|
| I agree that those are bad things.
|
| but how does your product help me with them?
|
| I have some code that has a deadlock. are you suggesting that
| I can find the deadlock by shipping my logs to a 3rd-party
| service that will feed them into an LLM?
| theogravity wrote:
| Hi, I'm the author of LogLayer (https://loglayer.dev) for
| Typescript, which has integration with DataDog and competitors.
| Sift looks easy to integrate with since you have a TS library and
| the API is straightforward.
|
| Would you like me to create a transport for it (I'm not implying
| I'd be charging to do this; it'd be free)?
|
| The benefit of LogLayer is that they'd just use the loglayer
| library to make their log calls and it ships it to whatever
| transports they have defined for it. Better than having them
| manage two separate loggers (eg Sift and Pino for example) or
| write their own wrapper.
| Ishirv wrote:
| Hey, loglayer looks super cool! Would love to chat and set
| something up, send us an email at founders@runsift.com
| theogravity wrote:
| Sent an e-mail!
| 999900000999 wrote:
| Can it run completely on prem ?
|
| In most of the industries I work in we would never just send you
| our logs.
|
| 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 ?
|
| Where is the pricing information?
|
| Why do I need to login visit your homepage? How would I pitch
| this to my boss if they can't read what it does ?
|
| Edit: https://runsift.com/pricing.html
|
| I see the landing page. The pricing should be clear though "
| Contact Us" is scary.
| Akula112233 wrote:
| > Can it run completely on prem ?
|
| 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!
| 999900000999 wrote:
| Thanks!
|
| I hope my tone wasn't too brash.
|
| If you can update the pricing I might be able to pitch this
| to my org later this year. We'd definitely like an on prem
| solution though!
| TZubiri wrote:
| Consider not marketing yourself as an X alternative when
| launching? That might fly in slide decks and investor meetings.
| But I don't know what Datadog is, and I certainly don't care,
| won't look into what DataDog is just so I can be qualified to
| learn about your product.
|
| I guess it may be the case that you really know who your target
| is, but why miss the majority of the market and position yourself
| as pepsi on the same stroke?
| Sytten wrote:
| Datadog is industry standard at this point, if you dont know
| what splunk or datadog is you are likely not their ICP and
| their marketing is not targeting you.
| theogravity wrote:
| Agreed, if you don't know what Datadog is then you're
| probably not the target audience for this product.
| TZubiri wrote:
| Do you think if I don't know what datadog is, I am not the
| target audience for datadog?
| chzblck wrote:
| probably
| csomar wrote:
| Kinda? There aren't that many players in this niche and
| datadog is the "dog".
| Ishirv wrote:
| Hey - thanks for the feedback. We were trying to give people a
| good idea of where we fit in quickly, but I can see where
| you're coming from!
| n2d4 wrote:
| Even if it won't work for everyone -- some people (including
| me) are looking for Datadog alternatives, so this is the
| easiest way for them to speak to their ICP.
| mattfrommars wrote:
| What is your background to build 'AI powered datadog' alternate?
| Datadog is a massive company... how much experience do you guys
| have to have a product that competes with them?
| adelowo wrote:
| OP literally said they worked at Datadog and Splunk. That's
| enough tbh as those are leaders in this space
| mdaniel wrote:
| Your python sdk's <https://pypi.org/project/sift-dev-logger> GH
| link is 404: <https://github.com/sift-dev/python-sdk> Navigating
| upward shows the fork of SigNoz which I think is funny
|
| There was no GH link for your npm dep so maybe they're both
| private. Although npmjs shows your npm one as ISC licensed,
| likely because of the default in package.json
| Akula112233 wrote:
| Ah, any particular reason to want these SDKs public? Happy to,
| especially since you can see source on install anyway. Just
| curious!
|
| And Kudos to SigNoz as well - have to check out other folks in
| the space :)
| mdaniel wrote:
| My initial concern was what transitive deps it was pulling
| in, but the other answer to your question is the thing that
| most GH repos are good for: submitting bugs and submitting
| fixes
|
| It is also good for finding out what the buffering story is,
| because I would want to know if I'm dragging in an unbounded
| queue into my app (putting memory pressure on me) or knowing
| that your service returning 503s is going to eat logs. The
| kind of thing that only looking at the source would say for
| sure because the docs don't even hint at such operational
| concerns
|
| Anyway, the only reason I mentioned the dead link is because
| your PyPI page linked to GH in the first place. So if you
| don't intend people to think there's supposed to be a repo,
| then I'd suggest removing the repo link
| Akula112233 wrote:
| Noted, thank you! Will make some changes accordingly.
| ritvikpandey21 wrote:
| curious how LLM hallucinations will work on logging info - gonna
| be a hard problem to solve
| JyB wrote:
| Related https://www.datadoghq.com/product/platform/watchdog/
| r_singh wrote:
| How does this compare with Axiom? I'm looking to shift out of
| Datadog asap and Axiom was the choice. Would consider Sift
| Akula112233 wrote:
| 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 founders@runsift.com if
| you'd like to learn more
| reconnecting wrote:
| TINLA, but perhaps you need to ensure your product complies with
| potential trademark issues related to sift[.]com.
| csomar wrote:
| Can you explain what goes through an LLM and what does not. You
| offer 100K logs per _day_ for free but if all of these goes
| through an LLM, this will burn "thousands?" of dollars every
| month for a free customer that is milking the machine.
| super_ar wrote:
| It looks awesome! What are you guys using under the hood? I've
| lately seen a lot of companies building on top of ClickHouse.
| kbouck wrote:
| From the docs it looks like you ingest directly from apps
| instrumented with your libraries. Do you also plan to ingest
| OpenTelemetry events, such as those exported from an
| OpenTelemetry agent, or OpenTelemetry collector?
| Akula112233 wrote:
| Yes, we support OpenTelemetry ingestion! Also Datadog, Splunk,
| and various other vendors' agents/forwarders - even custom HTTP
| Daemons.
|
| If you've already set up logging, good chance you can just
| point your instrumentation towards us and we know how to ingest
| and handle it.
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