Most log products are expensive filing cabinets. You pour everything in, pay by the gigabyte, and the data mostly waits — until an incident sends you digging through it by hand. 24Observe treats your logs as a live substrate: searchable in a readable query language, followable in real time, and continuously watched by a detection engine that opens incidents the moment something matters. Storage is the floor, not the product.
Ask most teams what their logging tool does and the honest answer is: it holds the logs until something goes wrong, and then it charges us to look at them.
That is a strange product when you say it out loud. The whole reason you keep logs is to answer questions — what happened, when, to whom, and why — yet the dominant pricing model bills you most aggressively at exactly the moment you need to ask. So teams ration. They drop "noisy" sources, shorten retention until the window is too short to investigate the slow-burning problems, and sample away the very lines that would have explained the outage. The tool technically works; it has just trained you to send it less of the truth.
The second failure is subtler. Even when the data is there, getting an answer is manual. You open a search, guess at a filter, scroll, refine, copy a timestamp, jump to another tool to see what the host was doing, copy it back. The log is a passive record and you are the query engine. Multiply that by every incident and the real cost of logging is not the storage bill — it is the hours of skilled people doing by hand what the system should have done for them.
24Observe starts from a different premise: a log is only valuable if it is cheap to ask questions of, fast to read live, and actively working for you even when nobody is looking. So search is a readable query language rather than a dialect to certify in; live tail lets you watch a filtered stream as it happens; and the same logs are continuously evaluated by the detection engine, so a threat or an operational failure opens an investigated incident instead of waiting in the pile for you to notice. You are not paying to store data and then paying again to use it. Using it is the point.
And because the cost of looking is low, you stop rationing the truth. You can keep the noisy source, hold a sensible window, and send the lines that actually explain your failures — which is the only way logging ever pays back the bill it generates.
A log store that only stores has relocated your work, not removed it. Yours searches, watches, charts, and defends — out of the box.
The fastest observability project is the one with no custom agents to maintain. 24Observe accepts logs over open standards and the protocols your sources already speak, normalises the common ones on the way in, and asks nothing proprietary of the systems producing the data.
Applications and services send logs over OTLP — the open standard, no vendor SDK lock-in. Point your existing pipeline at the endpoint and they land. Collector setup →
Get a whole server reporting with a single command. It ships system logs and host metrics, installs from a signed package, and self-enrolls with a scoped token. The Sensor →
FortiGate, Palo Alto, Cisco, and endpoint telemetry arrive over HTTP and are normalised to a shared security schema, so detections behave the same across vendors.
Push platform, function, and edge-network logs straight in without standing up a collector — the events flow in over plain HTTP endpoints.
The ingest endpoint decompresses natively, so a busy log pipeline travels at a fraction of its raw size — a smaller bandwidth bill on your side, and batches that arrive faster.
Common formats map to a shared schema as they land, so a search behaves consistently whether the line came from an app, a firewall, or a cloud platform.
Search filters by field, matches text, groups, and aggregates in a syntax a practitioner reads at a glance — level:error service:checkout status:>=500 rather than a paragraph of proprietary scripting. The point is not minimalism for its own sake; it is that the same query you type into the search bar is the same query that backs a saved chart, drives an alert, and defines a detection. You learn it once, and the skill compounds across the whole platform instead of being trapped in a query console.
When you ship a change or chase a live problem, history is too slow. Live tail follows a filtered stream as it happens — pin it to one service while you deploy, or to a single request id and watch it travel through your system. Because it uses the same filter syntax as search, widening a live view into a historical investigation is a matter of changing the time range, not relearning a tool.
Stack traces are fingerprinted and grouped, so the same exception thrown ten thousand times becomes one error you can read, count, and trend — with a resolve-or-ignore workflow so a fixed bug stops nagging and a known noise stops cluttering. The wall of repetition that hides the one new failure becomes a short, honest list of what is actually breaking right now.
Any saved search can be promoted to a chartable time-series. The question you asked once during an incident — checkout failures per minute, errors from one region, auth rejections by service — becomes a line you can put on a board and alert on. Logs and metrics stop being two separate worlds you stitch together by hand; a log query is just a metric you have not charted yet.
The most dangerous log line is the one that accidentally contains a credential. Redaction is not an add-on you remember to configure — it runs by default, in two places, before anything is stored.
This is the difference between a log store and a security substrate. You are not running a separate tool over a copy of your data — the live log stream is what the detections evaluate, the moment events arrive.
Pouring your logs into 24Observe quietly turns on something most logging products never attempt: continuous detection. Eighty-seven prebuilt detections across fourteen packs run against your incoming events — authentication abuse, data exfiltration, secret exposure, web attacks, threat-intel matches, AI-agent threats, cloud control-plane tampering, Linux host security, and infrastructure and reliability signals. Most carry a recognised attack-technique mapping, and you enable a pack with one action and tune it to your environment.
When a detection matches, it does not produce a muted alert in a side panel — it opens a first-class incident, and the AI analyst immediately investigates it: gathering the related events, tracing what the signal touches, corroborating against threat intelligence, and returning a verdict with the evidence cited. The log line that would have sat unread until your next post-mortem instead becomes an investigated conclusion while it still matters.
You can write your own detections too, in the same readable query language you already use for search. A detection is simply a saved query with a window, a threshold, a severity, and an attack-technique tag — and the instant it matches, it flows through the exact same incident pipeline, routing, and investigation as everything else. There is no separate detection console to learn and no proprietary rule format to certify in. The query you can write, you can detect on.
The result is a feedback loop logging products rarely close: the data you collect to understand your system is the same data actively protecting it, and the cost of broad coverage is low because investigation is automatic. You are not choosing between a cheap log archive and an expensive security tool. You are running one substrate that does both.
Here is the concrete path a real problem travels — the kind of half-hour scavenger hunt a passive log store leaves you to run by hand, collapsed into a few minutes.
Checkout starts throwing errors. In a passive logging tool, the first you hear of it is a customer complaint or a dashboard you happen to glance at; then begins the dig — open search, filter to the service, scroll for the first occurrence, copy the timestamp, jump to host metrics, jump back. In 24Observe the story is already assembling itself. A reliability detection watching error-rate has crossed its threshold and opened an incident, so instead of discovering the problem you are notified of it, with a starting point attached.
You open live tail filtered to the service and watch the errors arrive in real time. Error tracking has already fingerprinted them: rather than ten thousand identical stack traces, you see one group — PaymentDeclinedError: gateway timeout, counted and trending — so the signal is obvious instead of buried. One glance tells you it is a single failure mode, not a dozen unrelated ones, and roughly when it began.
Now you ask the obvious question in one readable line: is this every region, or one? You add a filter, and the errors collapse to a single region — the dependency in that zone is timing out. Because you previously promoted "checkout failures per minute" to a chart, you can see exactly when the line bent and overlay it against your last deploy. The correlation that would have taken three tools and twenty minutes is a glance.
Meanwhile, nothing sensitive has leaked into any of this. The request bodies in those error logs would normally be a goldmine of tokens and credentials; redaction stripped them at the edge and again at ingest, so you are reading the shape of the failure without handling anyone's secrets. You resolve the incident, mark the error group resolved so it stops nagging once the fix ships, and the whole episode is recorded — what fired, what you saw, what you concluded — for the look-back.
And if that same spike had been something darker — a flood of auth failures, a credential surfacing in a URL, a known-bad address hammering the endpoint — the security packs would have opened a security incident in parallel and the analyst would already be investigating it. The logs did not just help you understand the outage. They were watching for the attack the whole time.
Point your sources at the platform and get search, live tail, error tracking, charts, redaction, and continuous detection — on the same data, in one place, without a per-query toll.