Why is observability data expensive to process?

Vendors price ingestion, indexing, and storage by volume. As logs, metrics, and traces grow, platform costs rise in direct proportion to the data you send.

How does Grepr lower observability costs without losing visibility?

Grepr analyzes streaming data with machine learning, summarizes repetitive messages, and passes unique or important events through unchanged. This cuts volume by about 90 percent while preserving critical insight.

Does Grepr discard any data?

No. Every event is retained in low cost storage. You can search and retrieve full records at any time and backfill them to your observability platform when needed.

What is a Grepr pipeline?

A pipeline defines sources, datasets, and sinks. Sources include agents such as Datadog and OpenTelemetry. Datasets point to low cost storage. Sinks route reduced data to backends like Splunk, Datadog, or New Relic.

Can Grepr help avoid vendor lock in?

Yes. Grepr decouples data collection from backend processing. You can run multiple pipelines and move data between platforms at your pace while keeping a complete copy in storage.

How does backfill work with Grepr?

Query Grepr storage using familiar syntax and send the matching full fidelity events back to your chosen backend for a time window. This restores full context during incidents or audits.

Will our dashboards and alerts still work?

Yes. Reduced data continues to flow to your existing platform. You can set exceptions for signals used by monitors and metrics to keep alert accuracy intact.

What reduction should we expect?

Most teams see around 90 percent reduction in volume. The exact level depends on data shape and repetition in your environment.

How do we migrate between platforms with Grepr?

Point agents to Grepr, add a new sink for the target platform, and use dynamic backfill to load history. You can phase traffic over without redeploying instrumentation.

What storage does Grepr use for retained data?

Grepr supports low cost S3 based storage. You can use Grepr hosted storage or your own buckets, organized into datasets for environments such as production and staging.

Use Grepr to Avoid Observability Vendor Lock-In

Steve Waterworth

July 8, 2025

Observability data is now a commodity. The agents to collect metrics, logs and traces are freely available from both open source and commercial vendors. Collecting the data is the easy part, the tricky bit is processing the enormous amount of data generated and turning it into actionable information. This is where the platforms from the usual suspects come into play; at a cost. That cost is directly proportional to the volume of data processed. The vendors like to generate fear, uncertainty and doubt about how essential every byte of that data is; the piece of data you chose not to send will be the piece of data you need to solve an issue. Of course, the more you send, the more it costs and the greater the return to the vendor’s shareholders. Once committed to a platform it takes considerable effort to break free, until now.

Grepr Gives You Options

Grepr slips in like a shim between the agents collecting the data and the observability platform processing the data. Using AI (machine learning) Grepr automatically identifies similar patterns across the data then uses these to send summaries for frequent entries while letting unique entries straight through; reducing data by 90%.

No data is ever discarded, all data sent to Grepr is retained in low cost storage where it can be queried and optionally backfilled (on demand) to the observability platform. Grepr provides 100% insight with 10% of the data.

A Grepr pipeline configures how this should happen. It defines the source(s) of the data, for example it could include an endpoint for Datadog and Open Telemetry agents. It specifies which low cost storage to use for all the data received. Finally it sets the ultimate destination(s) for the processed data, for example New Relic and Splunk. Optionally it may configure static rules to always include/exclude certain data and/or transform/enhance data.

This may be an extreme example but it shows what is possible when mixing and matching observability platforms.

No Longer Shackled

Changing the observability platform you use is typically a non-trivial task. Redeploying innumerable application services to change the instrumentation code, deploying and configuring new agents to ship the data. Running two observability platforms simultaneously during and after the migration to ensure no data is lost and ensuring continuity with the existing operational processes. It’s all a lot of effort and presents an insurmountable barrier for most organisations.

With Grepr the observability platform used to process the data is independent of the agents used to ship the data. Using multiple Grepr pipelines observability data can be incrementally moved from one backend to another with zero risk of losing any data; all data is retained in low cost storage. Utilising Grepr Dynamic Backfill the new observability platform can soon be up and running complete with historical data, reducing the duration needed to run two platforms simultaneously.

The highly configurable Grepr pipelines release you from vendor lock-in as well as saving 90% on platform costs by optimising the data. Exploiting low cost storage for all data means you no longer have to balance retention against cost; keep more data for longer at a lower cost. You now have the flexibility and control to manage and arbitrate where your observability data is processed