Auditty vs Cribl
Cribl Stream is the default answer in this category and it is a good product. It moves any source to any destination, translates formats, redacts, routes and reduces, and it is genuinely the right tool for a large heterogeneous estate.
Auditty does one of those things, on Kubernetes, without being told how. If your problem is the whole pipeline, Cribl. If your problem is the log bill, read on.
Cribl reduces what you tell it to. Auditty decides, and you override it.
Reduction in Cribl is authored: an operator writes an ordered sequence of functions with filter expressions, which means somebody has to know in advance which patterns are worth dropping and keep that knowledge current as services ship. Auditty starts with no rules at all and a pattern has to prove itself genuinely repetitive before it is summarised, with rules existing to scope which workloads are read and to override the automatic decision. Neither is strictly better. One asks for your judgement and rewards it; the other asks for nothing and gives you a veto.
Side by side
5 questions a buyer actually asks.
Every claim about Cribl below is a quote from their own documentation, with the link. We would rather you checked than took our word for it.
Who decides what gets dropped?
Auditty
Auditty does, per pattern, with no configuration. Errors and warnings are never suppressed automatically, and a pattern that is merely widespread across many pods is treated as telemetry rather than noise. Rules are optional and exist to override it.
Cribl
You do, as an ordered list of functions evaluated top down. Cribl provides prebuilt packs to start from and a natural-language editor that generates pipelines, so this is assisted authoring rather than hand-written regex, but the judgement about what is noise remains yours.
As with Routes, the order in which you list the Functions in your Pipeline matters. Cribl Stream evaluates the Functions in a Pipeline in top-down order.
What infrastructure am I running to do this?
Auditty
One DaemonSet bounded at 1.5 vCPU and 768 MiB per node, and those limits are enforced rather than advisory. There is no aggregation tier to size, and node count is not a billing lever.
Cribl
Cribl Edge is a first-class Kubernetes DaemonSet, so the per-node part is comparable. Aggregation is different: Stream is a worker tier you provision, size and load-balance, and Cribl publishes the arithmetic for it.
The general sizing guideline from Cribl is to plan for a maximum of 200 GB/day in+out per vCPU when hyperthreading is enabled (2 vCPUs per physical core on Intel/AMD).
Where does the retained copy live?
Auditty
A bucket you own, in your account, written by your own agent with credentials you issue. Auditty has no path to that storage. Not a restricted one, not an audited one.
Cribl
Cribl Lake is a cloud-only product. Customer-owned buckets are supported through bring-your-own-storage, on an Enterprise billing plan.
You can host Cribl Lake Datasets on Amazon S3 or Azure. Storage Locations are available on an Enterprise billing plan.
Can I read the archive with my own tools?
Auditty
Yes. It is an open format your own tools can read, with or without us, and the setup statement for querying it is generated for you.
Cribl
Cribl also writes open formats and supports replay back through Stream. One boundary is worth knowing about in advance: data written to your bucket outside Cribl is not visible to Cribl.
However, any data that you send directly to the storage container/bucket outside of Cribl products will not be visible to Cribl Search or Stream.
What does it cost when nothing is happening?
Auditty
Nothing extra. Licensing is on ingested volume, quoted in annual tiers, and object storage is billed to you directly by your cloud provider.
Cribl
Stream is priced in credits per GB, one credit being one US dollar. For Cribl-managed workers those credits also cover the infrastructure itself, not only the data you push through it.
Cribl will draw down your Cribl.Cloud credits to cover the base cost of maintaining the infrastructure, as well as your normal cost for ingested data.
Honestly
Neither of these is the right answer for everyone.
Choose Cribl if
- Your estate is heterogeneous. Windows event logs, network gear, syslog from appliances, three SIEMs and a data lake. Cribl is built for exactly that and Auditty is not.
- The job is routing and translation as much as reduction. Getting one source to four destinations in three formats is a Cribl problem.
- You want to control precisely what happens to each stream, and you have someone who will own those rules. Authored reduction is a feature when the person authoring it knows the data.
- Your use case is security operations, where what you keep is often decided by a detection engineer rather than by how repetitive a line is.
Choose Auditty if
- Your logs are mostly Kubernetes container output and the bill is the problem you are solving.
- Nobody is going to maintain a reduction ruleset. This is the common case, and an unmaintained ruleset quietly stops matching as services ship.
- You do not want a second tier to size, scale and page someone about.
- You want the retained copy in your own bucket without it being a plan-tier question.
Cribl's documentation was read on 3 September 2026, and every quote above links to the page it came from. Products change, and a comparison page that is not re-read is a slow way of becoming wrong. If something here is out of date or unfair, tell us at [email protected] and we will correct it.
About Auditty
The questions this raises about us.
How does Auditty decide what to forward and what to suppress?
In almost all cases, automatically and without configuration. A pattern has to prove itself genuinely repetitive before Auditty will summarise it, and volume alone is not proof: a message emitted once by each of two hundred pods is treated as telemetry, not as noise. Errors, warnings and fatal levels are never suppressed automatically at all.
Three things additionally override automatic suppression: the first time a pattern is ever seen, a sudden spike in a pattern's rate, and a value far outside the established range for a numeric field you opted to track. So a single anomalous request still reaches you even when the pattern it belongs to is being summarised.
Explicit rules are optional and exist for the two decisions statistics cannot make for you: which workloads the agent may read at all, and the specific patterns where you want to overrule the result. Where a rule exists it wins outright: a line you marked preserve is never suppressed by any automatic mechanism.
How much CPU and memory does the Auditty agent use?
In steady state, 60 to 80 MB of memory per node. The agent also ships with an enforced ceiling of 768 MiB and 1.5 vCPU, which it cannot exceed however much log volume you put through it, so the typical figure is what you should expect and the ceiling is what protects you if you are the exception.
That headroom exists for sustained back-pressure, where the worst case we size for is around 420 MB, rather than for normal operation. Consumption is flat with respect to log volume rather than proportional to it, so a tenfold burst in logging does not become a tenfold burst in the agent, and it does not degrade into unbounded growth when a cache or buffer fills.
Both figures are the defaults in the Helm chart, and you can lower them. On a high-pod-density node the guidance goes the other way: raise the ceiling or reduce the event buffer.
Where is my log data stored, and for how long?
Your log data is stored in your own Amazon S3, Google Cloud Storage or Azure Blob Storage bucket, under a prefix you choose, compressed and partitioned by time in an open format your own tools can read. Auditty never holds a copy.
Retention is whatever your own bucket lifecycle policy says, because it is your storage and your billing relationship. Auditty applies no retention limit and takes no storage margin.
How is Auditty priced?
Auditty is licensed on ingested volume, meaning the bytes the agent reads on the node, and quoted in annual tiers so the rate falls as volume rises. We meter what we read rather than what we forward, so tightening your rules cuts your log vendor bill without changing what you pay us.
Node count, pattern volume, rule count, retrieval count and forwarder types are not billing levers, because high cardinality is the problem customers arrive with and metering it would penalise them for it.
Object storage is billed by your cloud provider directly to you. Auditty never holds your data, so there is no storage or egress margin in between.
Other comparisons
Auditty vs Grepr
Grepr is a hop in your log path. Auditty is not in the path at all.
Auditty vs Edge Delta
Edge Delta wants to be your observability platform. Auditty wants to lower its bill.
Auditty vs Datadog’s own log cost controls
Exclusion filters move the indexing line. They do not move the ingestion line.
The fastest way to settle this is your own logs.
We will run a sample of them and show you what would have been forwarded, what would have been summarised, and what it would have cost.