Imply launches Lumi Loglake to expand object storage search

Imply has launched Lumi Loglake, adding direct log search in object storage to its Lumi platform.

The product targets observability and security teams that want to query unstructured logs without first indexing or restructuring the data.

Loglake is designed to let organisations search data where it is already stored, including AWS S3, Delta Lake and Apache Iceberg. It does not require data catalogues, schema definitions or rehydration workflows before searches can begin.

The launch reflects pressure on companies handling rising volumes of telemetry from AI systems, cloud infrastructure and security tools. Many teams rely on observability systems that require machine data to be organised and indexed in advance, a model that can increase storage and infrastructure spending as data volumes grow.

Loglake separates compute from object storage so customers can retain more historical telemetry in lower-cost storage and bring in compute resources only when queries are run. The approach is intended to reduce the need for systems that continuously index data whether it is being queried or not.

Imply said organisations using Loglake can reduce software costs by 70% or more and hardware costs by 40% or more by shifting data from indexed storage into open storage environments such as Amazon S3. The company also positioned the product as an option for Splunk users who want to keep historical telemetry outside Splunk's indexing tier while continuing to query it with SPL and receive native Splunk events.

Teams can query the same datasets across several platforms without duplicating storage, according to Imply. These include Splunk through SPL, Databricks through Spark SQL, Grafana through LogQL, and AI and business intelligence systems through ANSI SQL and JDBC.

Cost pressure

Eric Tschetter, Chief Architect, Imply, linked the launch to the economics of managing machine data at scale.

"AI is generating more telemetry than organizations can afford to index," said Eric Tschetter, Chief Architect, Imply. "Lumi Loglake gives teams a new way to retain, search, and investigate that data without the cost and complexity of always-on indexing architecture. By bringing interactive log search directly to open storage, Loglake applies the economics and flexibility of lakehouse architectures to operationalize log data."

Industry analysts also pointed to changing economics in observability and security operations, saying organisations want broader access to telemetry data without sharply increasing the cost of storing and preparing it for use.

"Traditional observability pricing models are forcing teams into visibility tradeoffs at the exact moment AI systems are driving unprecedented telemetry growth," said Stephen Catanzano, Principal Analyst, Omdia. "As infrastructure complexity and data volumes continue to rise, organizations are looking for more scalable approaches that improve operational flexibility without significantly increasing costs."

Kevin Petrie, Vice President of Research, BARC, said broader use of cloud lakehouse storage for logs could have effects beyond observability workflows.

"Observability and SIEM require analyzing mountains of data without prohibitive cost or complexity," said Petrie. "Lumi reduces this tradeoff by searching logs in object storage without the overhead of indexing, cataloging, or schemas. Collecting all these logs in a cloud lakehouse also creates opportunities beyond observability, because telemetry data can enrich other analytics and AI initiatives."

The launch underscores a wider shift in how companies treat machine-generated data. Rather than deciding in advance which logs deserve full indexing, vendors and customers are increasingly exploring models that preserve larger volumes in object storage first and optimise selected datasets later.

For Imply, that places Lumi Loglake at the intersection of observability, security information and event management, and data lakehouse infrastructure. The product is available immediately.