Grepr

Machine learning to separate signal from noise, manage observability data, and reduce spend by 90% without migrations.

[Grepr @grepr_ai]

Grepr's free tier is live and perpetual. No credit card, unlimited users, 4 TB logs and 8 TB traces a month, two pipelines. Same pattern engine as Pro. One config line, patterns in 30 minutes, all on your own data.

[Grepr @grepr_ai]

More details here: https://t.co/0iLPPjZmzs

[Grepr @grepr_ai]

Grepr's free tier is live and perpetual. No credit card, unlimited users, 4 TB logs and 8 TB traces a month, two pipelines. Same pattern engine as Pro. One config line, patterns in 30 minutes, all on your own data.

[Jad Naous @jadtnaous]

@Intellyx recognized @grepr_ai with a 2026 Digital Innovator Award, and what I am proud of is the reason: we shred the bill while keeping full fidelity. https://t.co/9V6DAzCC99

[Grepr @grepr_ai]

Most observability stacks collect everything, then ask engineers to find signal in the wreckage.

@jadtnaous sat down with @mirko_novakovic at @dash0hq to talk about why that model broke a long time ago, and what comes next.

https://t.co/vUPl8cLLKe

[Grepr @grepr_ai]

Grepr is sponsoring Observability Summit NA, May 21-22 in Minneapolis.

Find the team at Table 16.

https://t.co/1D8c5zrT8f

[Jad Naous @jadtnaous]

New piece on TFiR: observability at scale has become a configuration management problem.

95% of log messages are repeated patterns. Most teams are paying to ingest the noise around the signal.

Detection times drop from hours to minutes when the pipeline does the configuration toil for you.

https://t.co/wSvXV1b9le

[Grepr @grepr_ai]

132GB of data served to users. 38 petabytes of telemetry to do it.

That's the actual ratio. And it's why the Datadog bill keeps going up while nobody's resolving incidents any faster.

We got into the math: https://t.co/wrEvoNBIn7

[Grepr @grepr_ai]

HIPAA wants six years of audit logs. Your observability platform wants six figures a month to store them.
The fix is a two-tier architecture: reduced signal to your platform, raw events to S3 with Parquet and Iceberg. Query years of history in seconds. https://t.co/oiNsyHhaYN

[Grepr @grepr_ai]

The Grepr team will be at #O11ySummit next month in Minneapolis.

Still need to grab your ticket? Head here: https://t.co/Ds0P9xQwtL

We'll see you at table T16!

[Grepr @grepr_ai]

Most observability platforms charge you to keep your own logs.
The architecture for getting out from under that, Parquet + Iceberg + a query layer that actually works:
https://t.co/GozEosBS3E

[Grepr @grepr_ai]

Here's what we learned at #Kubecon last month:

#AI workloads are generating #telemetry at volumes that most budgets were never built to handle, and teams feel it every time they open an invoice.

#Grepr COO @Johnymkim wrote a recap here: https://t.co/060Zk4PB4h

[Grepr @grepr_ai]

Your healthcheck logs are probably 15-40% of your total log volume. Millions of identical lines per day, billed at the same rate as the logs that actually matter. Here's how to fix that: https://t.co/1edxlE4rI0

[Grepr @grepr_ai]

keeping every log "just in case" isn't a monitoring strategy... it's what happens when retrieval is broken.

https://t.co/DIEPZDB8Gj

[Grepr @grepr_ai]

Application logs and APM traces aren't the same data type. Here's how they differ, why it matters for your observability bill, and when to use each.
https://t.co/HIzorCjTlF

[Nick Lippis @NickLippis]

More data ≠ better visibility.

Teams are generating petabytes of telemetry, but the real challenge is cost and making that data useful.

I talk with Jad Naous of @grepr_ai about how observability is evolving.

🎧 Full episode here: https://t.co/3LPrd3GRn5

[Grepr @grepr_ai]

Our CEO @jadtnaous talked about the rising costs of infrastructure and it's autonomous future with @NickLippis. Between a mountain of noise and a rapidly changing environment, SREs can't catch up...

[Nick Lippis @NickLippis]

Telemetry is exploding. Systems are getting more complex.

Waiting for failures and then troubleshooting doesn’t scale.

Jad Naous of @grepr_ai and I discuss moving toward autonomous operations.

🔗 Full episode here: https://t.co/IopcgO3ojg

[Grepr @grepr_ai]

Learn about our latest release here: https://t.co/8vs3yTFver

[Jad Naous @jadtnaous]

Today, I'm excited to announce the industry's first proactive AI SRE agent with @grepr_ai. It works by finding novel behaviors in your environment and only asking an LLM to investigate those. By focusing on novel behaviors, we make applying LLMs on an entire stream of observability data possible. Read more: https://t.co/7wHDy5g9g1

[Grepr @grepr_ai]

#KubeCon Amsterdam is almost here. Who else is heading to the Netherlands next week?

Find us at booth 491. We have some new product updates to share and would love to connect in person. Come say hi.

[Grepr @grepr_ai]

Our founder, @jadtnaous, just published the first in a two-part series on "Observability Debt" and why it's bankrupting engineering teams. https://t.co/Z2e0ccxarj

[Jad Naous @jadtnaous]

At @grepr_ai, we realized that to solve reliability, we first had to solve the "Data Tax." We built Synapse, our pattern detection technology, to eliminate the 90% of noise that clogs your systems, clearing the deck for what comes next. https://t.co/A8aqCV97Hh

[Grepr @grepr_ai]

Your Datadog bill scales with data volume. Your MTTR doesn't.
50 req/sec = 26 petabytes of traces/month. Most of it is noise.
New post: the math behind observability costs, and how to cut them by 90%+ without changing your workflows.
https://t.co/jJvziMXvvn