The State of Log Management 2025
Logs are exploding. Costs are climbing. Performance is stalling.
If you manage logs, you’re in the hot seat
Every app, every integration, every security risk—it all generates more data. And when something breaks, everyone turns to you for answers. Fast.
You’re expected to keep it all searchable, all the time—and do it within tight budgets, under mounting pressure, and with performance expectations that never let up.
Gartner is sounding the alarm
According to Gartner’s 2025 Magic Quadrant for Observability Platforms, organizations are investing more than ever—yet the same themes keep surfacing: spiraling complexity, rising costs, and the difficulty of scaling observability across modern environments.
It’s validation of what log admins have been saying for years (Network World).
So we dug deeper
Analyst reports tell one side of the story. We wanted to hear the other side—from the people who live this every day.
That’s why we partnered with UserEvidence to survey 132 observability and platform admins. Their answers were candid, raw, and strikingly consistent:
- “If we burst past our Splunk limits, we find we’re dropping events, plus we have to reach out and purchase a higher tier of licensing. We’re hemorrhaging money trying to keep these events and logs.” — Head of IT – Telecommunications Equipment Company
- “Most of the time when we’re going back into logs, it’s because of a problem. And in that moment, trying to pull from cold storage is just painful. We’ve had times where we really needed access, but the process was so slow and manual, it made the whole situation worse.” — Jon Scarpa, Director of Information Technology at RD Abbott
- “I’ve got Splunk in one place, Graylog in another, Loki in a third. Trying to unify all of that? It’s like herding cats.” — Krishnan Chandresekharen, Associate Director, IT Architect, IQVIA
And the numbers tell the same story.
These aren’t edge cases—they’re the daily reality of running log management today.
The retention reality
Storage pressure is forcing hard choices..
Over three-quarters of respondents keep data in hot storage for less than 90 days—and half keep it for just 30.
Once it cools, 60% store data in warm tiers for only one to six months, and 58% archive or delete logs from cold storage within six months.
That means most teams lose access to the very data they need for trending, forensics, or machine learning before it can drive insight.
Why it matters now
AI and ML aren’t easing the load—they’re raising the bar.
74% of organizations are already using or piloting AI/ML-based detection, and another 24% plan to do so in the next year. These workloads demand more data, longer retention, and faster queries.
Yet today’s trade-offs—short retention windows, filtered logs, cold-storage delays—undermine AI effectiveness before it even starts.
As one IT director put it, many teams are “monitoring but not really measuring”—flying a little blind when historical data isn’t readily available.
Imply Lumi: Built to End the Trade-Offs
Your job doesn’t need to get harder. It needs the architecture to catch up.
That’s why we introduced Imply Lumi, the industry’s first Observability Warehouse. Lumi was designed for the exact problems highlighted in this report (you can read more about the vision behind Lumi in our introduction blog).
- Scale without runaway costs
- Keep data hot longer—no painful rehydration
- Run sub-second queries—even at petabyte scale
- Work seamlessly with your existing tools like Splunk, Grafana, and Tableau
Early feedback from admins has been clear: they want a system that can handle heavy workloads, improve performance, and help reduce the trade-offs of managing hot and cold data. That’s exactly what Lumi was designed to do.
Read the full report
You’re in one of the most demanding roles in tech—and you’re not alone.
Download the full report: The State of Log Management 2025 to hear directly from your peers, and to see why a new approach to log management is long overdue.
Related articles
More from Imply on the same topic.
Search your Splunk DDSS archive without restoring it
Security teams often keep logs long after they have aged out of their SIEM platform’s searchable window. The data is there for investigations or audits, but accessing the data for insights can be complicated.
Claude makes dashboards now, Lumi is ready for them
This week Anthropic launched Claude Dashboards in beta. You connect a data source, ask a question in plain language, and Claude writes the queries and builds the dashboard.
Lumi Lounge Recap: 3 Takeaways from Splunk .conf26
The Imply Lumi Lounge sat directly across the street from Splunk .conf26 and reached venue capacity on both days. Digital signs around the Colorado Convention Center pointed attendees our way with a simple message: “Federated search, done right. Full SPL. Less spend.”
Agents don’t query like your detections do
A security team of four has run a major SIEM since February. Nobody on the team writes SPL. Nobody opens the console or looks at the dashboards. Every question goes through an AI agent, which writes the query, runs it, reads the result, and answers in plain language.
Why You Shouldn’t Have to Delete Your VPC Flow Logs
When a security incident happens, investigators almost always start with the same questions: Which systems communicated? Where did the traffic originate? What changed before the incident? Was data exfiltrated? AWS VPC Flow Logs are often the fastest way to answer those questions.
Splunk Smartstore vs Lumi Loglake: Two Very Different Ways to Search Logs in Object Storage
One copies data back before it can be searched. The other queries it where it lives. Lumi Loglake lets Splunk teams query logs directly in object storage, including AWS S3, Delta Lake, Apache Iceberg, using standard SPL.
Supercharging Schema-On-Read: Logs in Object Storage Don’t Need a Data Catalog
Machine data architectures are rapidly changing. As telemetry volumes continue to grow and as costs rise, organizations are increasingly moving logs and other machine data into object stores such as AWS S3.
Imply Lumi Loglake vs Splunk Federated Search for S3
Teams are increasingly moving log data into AWS S3 to reduce costs and extend retention. Both Lumi Loglake and Splunk Federated Search to S3 help you query data in AWS S3 to lower costs, however the two technologies have several notable differences.
A First Look at Lumi Loglake: Query Logs Where They Live
TL;DR: Imply Lumi Loglake is a lakehouse (separated compute/storage) architecture for unstructured logs that reduces costs from 40% up to orders of magnitude on your hardware/AWS/Azure bill used to run your SIEM/observability products.
Imply Lumi Major Release Preview: Continuing the Journey Towards Decoupled Observability/SIEM
We are getting ready to introduce the next major expansion of Imply Lumi and the observability warehouse. When we introduced the industry’s first observability warehouse, the goal was clear: decouple the observability stack by separating storage, compute, and access.
Query Lumi from Grafana: Now in Private Preview
Observability has a silo problem. Today, we’re removing another one. Imply Lumi’s Grafana Loki integration is now in Private Preview.
