Mezmo and Grepr both promise to help engineering teams get control of observability costs through smarter data processing. Both use AI to analyze data streams. Both sit between your infrastructure and your observability platform. On paper, they sound similar.

In practice, they take fundamentally different approaches to the problem.

Mezmo builds an analytics layer on top of your observability data, with pipeline filtering as part of a broader platform. Grepr takes a narrower approach, cutting data volume by 90% or more without asking engineers to build or maintain pipeline rules or interrupt their current working practices.

Here's how they actually compare.

Mezmo

Mezmo, formally known as LogDNA, offers observability for agentic workflows. What this means is that the Mezmo platform ingests various observability data then using its AI platform analyses all that data. It provides a chat bot interface where SREs can investigate platform health via regular questions: "Review logs for the last 15 minutes and perform root cause analysis on any errors." The AI then attempts to correlate and deduce across all the ingested data to provide an actionable summary of what it finds. This has the potential to speed up root cause analysis by presenting a summary rather than the engineer running numerous queries and looking at various dashboards then performing the correlation manually.

This capability is intended to be used alongside existing observability platforms such as Datadog, New Relic, Splunk, etc. While it may speed up some root cause analysis, it does present engineers with two sources of truth: the incumbent observability platform and Mezmo.

As part of the solution suite, they also offer pipeline processing of observability data. This offers the possibility to route, optimize, and enhance observability data. For example, sending firewall logs straight to AWS S3 and application logs to Mezmo and Datadog. The pipeline processing is provided by Vector with the configuration performed via the Mezmo web dashboard.

Grepr

The Grepr Intelligent Observability Data Engine uses AI to continuously analyze observability data streams to automatically identify similar patterns in the data. Frequently occurring data is summarized, while unique data is passed straight through. For example, health check requests are summarized, while an error message is 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

Mezmo is primarily focused on providing an agentic (chat bot) interface to interrogate observability data for faster root cause analysis. The pipeline processing is a secondary capability. With copious ongoing manual configuration, data volume reductions of up to 50% are possible.

The primary focus of Grepr is the intelligent pipeline processing of observability data to automatically reduce the volume by 90% or more passed to observability platforms, thus reducing cost and increasing the signal-to-noise ratio. No data is lost; everything received by Grepr is retained in low-cost storage where it can be queried and/or immediately selectively backfilled to the observability platform when triggered by an incident.

Installation

Mezmo pipelines can receive data from a number of sources, or they provide their own log shipper for installation. If the currently installed observability agent is one of those supported, then it is just a reconfiguration of those agents to send their data to Mezmo. Note that the handling of agent keys is a bit clunky. Mezmo has its own ingest key separate to the ingest key of an existing observability platform. Therefore both the endpoint and key will need to be reconfigured for the observability agents.

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 is processed before being forwarded to the platform. This is not really an installation, just a small reconfiguration of an existing install with minimal impact on the targeted hosts. The ingest key is common across Grepr and the observability platform.

Pipeline Configuration

Mezmo pipeline configuration is entirely manual. The sources, sinks, and numerous filters must be manually added and configured for each pipeline. A misconfiguration of an edge pipeline can result in a significant increase in observability 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 and Backfill

To initiate a rehydration or backfill of retained data with Mezmo, an engineer must create and submit a rehydration job via the web dashboard. Only the source and time period for the rehydration may be selected; all available data for the selected time period will be pushed to the observability platform. This may be considerably more data than is required for the issue under investigation.

Grepr provides the ability to run a query against the retained data using any of the popular syntaxes (Datadog, New Relic, Splunk) and time period. Once the query is validated, it can 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

Mezmo will take longer to install and configure. Complexity increases due to the duplication of observability platform functionality. Engineers will have two sources of truth: one on the Mezmo chat bot and one on the existing observability platform. This will have a significant impact on existing workflows and engineer productivity until new working practices are adopted.

Grepr is easier and quicker to configure. There is 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.

The Mezmo pipeline filters will require continued maintenance of their configuration as services are updated and new ones are deployed. This will consume time and resources.

The Grepr Intelligent Observability Data Engine continuously analyzes the observability data stream and automatically maintains a working set of pattern filters, typically approximately 200,000 filter rules for high data volumes. Manual intervention is not required.

Grepr vs. Mezmo Feature Comparison

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While the principal goals of Mezmo and Grepr differ, there are some common features between the two solutions. The AI chat bot for querying by Mezmo is not present in Grepr. However, the pipeline processing and routing of observability data is common to both. Just considering this capability, it is clear that Grepr is the leading solution because it requires minimal initial setup and continuously adapts to changing environments. Grepr does not change existing working practices; it works with them, delivering rapid time-to-value.

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.

Grepr vs. Mezmo FAQs

Q: Does Mezmo replace my existing observability platform?

‍A: It sits alongside it, which is part of the friction. Mezmo provides its own AI chat interface for querying observability data, but your existing platform (Datadog, New Relic, Splunk, etc.) stays in place too. Engineers end up with two places to look for answers, and reconciling those takes time to sort out in practice.

Q: How much data volume reduction can Mezmo deliver?

‍A: Up to 50%, with ongoing manual configuration of pipeline filters. That number depends on how much time your team invests in building and maintaining those rules. As services change, so does the maintenance burden.

Q: Will Grepr disrupt how my engineers currently work?

‍A: No changes to existing workflows are required. Grepr reconfigures the existing agents to route through it, then handles everything automatically. Engineers keep using the same dashboards, the same alerting rules, and the same query syntax they already know.

Q: What's the difference between Mezmo's pipeline and Grepr's pipeline?

‍A: Mezmo's pipeline configuration is manual end-to-end: sources, sinks, filters, all of it. A misconfiguration can actually increase your data volume. Grepr sets up the source, sink, and data store once, then the AI continuously manages a working set of semantic pattern filters on its own, typically around 200,000 rules for high-volume environments.

Q: How does backfill work in Grepr compared to Mezmo?

‍A: In Mezmo, a rehydration job is submitted manually through the web dashboard, and it pulls everything from the selected time window whether you need it or not. Grepr lets you query retained data using Datadog, New Relic, or Splunk syntax, validate it, and submit a targeted backfill. More commonly, the backfill fires automatically when an observability alert triggers a webhook.