How two data optimization platforms compare on installation, pipeline configuration, rehydration, and time to value.

Both platforms use AI to process and reduce observability data volumes, but they take fundamentally different approaches to installation complexity, pipeline automation, and backfill capabilities.

Observo and Grepr both provide an AI-driven data pipeline to process security and observability data. They both offer the capability to direct noisy data to low-cost storage with the ability to access it and rehydrate/backfill it later. On the surface, they sound very similar. As always, the devil is in the details. Read on to find out more.

Observo AI

Observo AI Data Pipeline uses AI to optimize data for Security (SIEM) and DevOps observability platforms. The primary focus is on security use cases. They have recently been acquired by Sentinel One, the leading endpoint protection platform according to the Gartner 2025 Magic Quadrant for this sector. This recent acquisition will inevitably cause some confusion while the two product lines are harmonized and the operations are integrated. The current Observo platform provides data optimization and reduction, anomaly detection, routing, data lake, enrichment, and sensitive data detection.

Grepr

The Grepr Intelligent Observability Data Engine uses AI to continuously analyze observability data streams and automatically identify similar patterns. Frequent data is summarized, while unique data is passed through unchanged. For example, health check requests will be summarized, while an error message will be passed through. No data is lost when summarized because all data received by Grepr is retained in low-cost storage for compliance and/or later use. The Grepr Intelligent Observability Data Engine currently operates on log and trace data; support for metrics will be available in the future.

Comparison

A detailed comparison between the two platforms is difficult because Observo does not publish its platform documentation; only marketing information from the public website is available.

Observo is primarily focused on security use cases that forward data to SIEM systems. Its pipeline processing can optimize and enhance security logs with the application of various filters in the pipeline. There is limited anomaly detection that replicates a limited set of the capabilities of an observability platform.

Grepr is primarily focused on AI analysis of observability data streams to optimize the volume of data sent to observability platforms, consequently increasing the signal-to-noise ratio and controlling platform costs. Existing workflows, dashboards, health rules, etc., remain on the incumbent observability platform.

Installation

Observo requires a collector to be installed on each host; this appears to be based on either the OTel Collector or Vector. This collects data from various sources, performs filtering and/or enhancement processing at the edge before forwarding it to the configured sinks. Configuration of the installed collectors is managed centrally from the Observo web dashboard. Existing log shippers/agents must be reconfigured to send data to the Observo collectors. The Observo collector will consume additional compute resources; this should be considered when planning an installation.

Grepr fits in like a shim between the existing observability agents and the platform. The existing agents are reconfigured to send the data to Grepr where it will be processed before being forwarded to the observability platform. This is not really an installation, just a small reconfiguration of an existing install with minimal impact on the targeted hosts.

Pipeline Configuration

Observo pipeline configuration is entirely manual. When a new pipeline is created, the source and sink are defined. Any additional processing that is required must be manually configured; this includes data optimization, routing to low-cost storage, data enrichment, etc. A misconfiguration of a pipeline can result in a significant increase in data volume along with additional egress charges.

Grepr pipeline configuration is more automated. The pipeline source, sink, and data store are configured manually; after that, the AI continuously manages the pipeline. It automatically manages a working set of semantic pattern filters, reducing the data volume by 90% or more. All data received by Grepr is automatically retained in low-cost storage for potential use later.

Rehydration / Backfill

Both Observo and Grepr have the capability to query and rehydrate/backfill data retained in low-cost storage. Both solutions use open formats, Apache Iceberg and Apache Parquet, to store the data in AWS S3 buckets.

The details on how Observo handles rehydration/backfill are limited. Both technical (unspecified) and natural language (Beta) queries against the data are supported. The granularity of data rehydration is not given. Based on the available information for the Observo product, triggering automatic rehydration is not supported.

Grepr provides the ability to run a query against the retained data using familiar search syntaxes, such as Datadog, New Relic, and Splunk-like syntaxes. After a query is validated, it may be submitted as a backfill job. A more typical use case is to have the backfill triggered automatically via an alert from the observability platform. Grepr can receive a webhook to trigger a targeted backfill.

Time to Value

Observo will take longer to install and configure due to requiring an agent install together with end-to-end manual configuration of each pipeline. Ongoing maintenance of filter rules for each pipeline will require additional time and resources. Optimization levels on the data will be lower due to requiring ongoing manual configuration; Observo quotes up to 80% reduction.

Grepr is easier and quicker to configure, taking as little as 20 minutes to get started. There is a negligible impact on engineer productivity because existing workflows will continue to be used without disruption. Productivity should increase as a result of the improved signal-to-noise ratio in the observability data, making it easier to find important log messages and non-optimal traces. Optimization levels on the data of 90% or more are readily achievable due to the AI continuously managing the working set of semantic filters; approximately 200,000 for high data volume pipelines.

Summary

While the principal goals of Observo and Grepr differ, there are some common features between the two solutions. Observo is focused on security logging and feeding SIEM systems with some minimal alerting capabilities. Grepr focuses on data optimization for observability platforms, reducing data volumes by 90% or more while maintaining 100% visibility. Additionally, providing targeted backfill of data triggered by observability alerts.

Both solutions have pipeline processing and routing at their core. The superior AI automation of Grepr for managing the optimization rules, along with the minimal initial installation requirements, makes Grepr the better choice.

Ready To See The Difference?

For teams that want observability cost control without overhauling existing workflows, Grepr delivers results from day one. There's no complex installation, no manual pipeline maintenance, and no second dashboard competing for your engineers' attention.

Your existing agents, dashboards, and alerting rules stay exactly where they are. Grepr works alongside them, automatically optimizing data volume while retaining everything in low-cost storage for when you need it.

Schedule a demo to see how Grepr's Intelligent Observability Data Engine can reduce your observability costs by 90% or more, with zero disruption to your current workflows.

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Grepr vs. Observo FAQs

Q: Is Observo a good fit for DevOps observability teams?

‍A: Observo was built primarily around security use cases, specifically feeding SIEM systems. DevOps teams evaluating it for general observability cost reduction will find the feature set skewed toward that direction, and the recent Sentinel One acquisition adds some near-term uncertainty about where the product roadmap goes from here.

Q: How long does it take to get Grepr up and running?

‍A: As little as 20 minutes. There's no new agent to install; existing log shippers are reconfigured to point to Grepr, and the AI takes over from there. Your dashboards, alerts, and workflows stay exactly where they are.

Q: What happens to data that Grepr summarizes?

‍A: Nothing is discarded. Every piece of data Grepr receives is retained in low-cost storage in open formats (Apache Iceberg, Apache Parquet) on AWS S3. If an incident triggers a backfill, that specific data gets pushed to your observability platform automatically via webhook.

Q: Can Observo automatically trigger a backfill when an alert fires?

‍A: Based on publicly available information, automatic backfill triggering is not supported by Observo. Rehydration appears to be a manual process. Grepr handles this via webhook, so an alert from your observability platform can kick off a targeted backfill without any engineer intervention.

Q: How does Grepr's compression compare to Observo's?

‍A: Observo quotes reductions of up to 80%, and that requires continuous manual maintenance of pipeline filter rules. Grepr achieves 90% or more automatically, managing roughly 200,000 semantic filter rules for high-volume pipelines without anyone touching the configuration.