Grepr Blog

Observo and Grepr both use AI-driven pipelines to reduce observability data volumes, but where Observo requires complex manual configuration, Grepr automates 90% or more of data reduction from day one.

Grepr and Mezmo both promise observability cost control, but where Mezmo layers on complexity, Grepr cuts data volume by 90% or more with zero disruption to existing workflows.

Jitsu's CTO joined us to explain why the real cost of observability isn't your monitoring bill, it's how long it takes your engineers to find the problem when something breaks.

Observability was never reliability - it was a proxy that's finally broken under the weight of non-linear complexity, runaway telemetry costs, and a fundamental inability to move from seeing problems to stopping them.

Modern microservices applications generate petabytes of observability data monthly, and most of it is noise Datadog still charges you to store.

Observability was supposed to help teams control complexity in the cloud era. For many organizations, it has become one of the fastest-growing line items in the budget.

The observability industry keeps building smarter tools on top of the same noisy data, and a recent post from a Sr. Engineering Manager at Walmart shows exactly why that approach hits a wall.

The same discipline required to right-size AI compute infrastructure applies directly to observability, where defaulting to ingest everything drives massive costs for data that never gets queried.

Grepr reduces New Relic costs by applying ML-based log reduction upstream of ingest, summarizing high-volume patterns while preserving unique events, anomalies, and any logs referenced by your existing dashboards and alerts.

Grepr lets you keep your raw log data in your own S3 bucket while still getting the benefits of a managed observability platform.

Both Edge Delta and Grepr use AI to process observability data streams, but Grepr's automatic pipeline management delivers faster time-to-value with minimal configuration while Edge Delta requires ongoing manual maintenance.

The logging paradox forces organizations to index everything at massive cost because they cannot predict which fraction of data a future incident will require.

Most observability overspending comes from paying premium prices to store logs nobody queries.

Observability spending hit $28.5 billion in 2025, and 96% of organizations are now actively working to bring costs under control.

First mile log processing with Grepr filters and routes logs before they reach expensive observability platforms, reducing costs by 90% while preserving 100% visibility by sending high-signal data to premium platforms and routing routine logs to low-cost storage.

Grepr's SQL transform enables real-time redaction of sensitive data like passwords from log events before they reach your data lake or monitoring platform, using familiar SQL syntax within your log processing pipeline.

Across KubeCon, AWS re:Invent, and Gartner's IT Infrastructure conference, one message was consistent: observability costs are rising faster than value, and teams need high-signal data that supports AI scale, infrastructure decisions, and budget accountability.

Live View clones your production pipeline so you can test configuration changes against real data streams without any deployment risk.

Grepr was recognized by Gartner as a Cool Vendor in AI for IT Operations for its ability to give AI driven systems cleaner signal, lower cost, and real-time pattern detection that powers advanced LLM workflows.

Grepr cuts Grafana Cloud log costs by up to ninety percent through a simple configuration change that redirects your existing shippers to use semantic machine learning for automatic pattern aggregation while preserving all raw data in low cost storage.

Grok lets teams fill the gaps when automatic field detection misses something, giving Grepr a simple way to extract key details from unstructured logs and keep data accurate.

Grepr pipeline exceptions let engineers surface full raw logs on demand during incidents while keeping everyday log reduction in place.

Grepr’s Signature Sampling brings high-fidelity observability to modern systems by capturing every unique execution path without the noise or cost of traditional APM.

FOSSA cut Datadog observability costs by over 90% with Grepr, reducing log volume instantly while keeping full visibility, no workflow changes, and zero data loss.

Modern observability generates too much telemetry data and too little insight, and Grepr solves this by providing an intelligent observability platform that automates data processing, routing, and storage to cut costs by over 90% while preserving full visibility.

Grepr enables financial institutions to stay compliant with DORA by maintaining full log visibility and audit readiness at a fraction of traditional costs.

Jitsu used Grepr to cut Datadog log volume and costs by over 90 percent while keeping complete visibility, fast troubleshooting, and 13-month log retention.

Grepr integrates with Cloudflare to reduce log volume and costs while retaining complete visibility into traffic, performance, and security insights.

Grepr helps teams gain clear visibility into Kubernetes audit logs by filtering noise, detecting anomalies, and keeping observability costs low.

Grepr connects seamlessly with Splunk, using machine learning to cut log volume and costs by 90 percent while preserving full data access and visibility.

Structured logging turns unstructured text logs into clear, queryable data that improves visibility, collaboration, and operational efficiency.

Grepr uses AI and semantic analysis to reduce observability data by 90% while retaining complete visibility for faster, more cost-efficient incident response.

Grepr’s Live Edit lets you test and validate pipeline changes safely in a temporary clone, so you can fine-tune processing without affecting live data.

Grepr automates backfill with webhooks and rule-based triggers, restoring full log data on demand for faster, more efficient incident investigation.

Grepr’s Intelligent Observability Engine uses pipelines, clustering, and adaptive sampling to process and reduce log data by up to 90% while preserving full visibility.

FOSSA cut Datadog logging costs by 95% with Grepr, keeping full visibility and dashboards intact while scaling their software supply chain platform.

Grepr reduces observability costs by up to 98% through intelligent data summarisation while preserving complete access to all logs when needed.

Grepr uses machine learning to cut log volume by 90% while keeping every log searchable and recoverable, giving teams lower costs and full control.

Grepr is building the foundation for AI-powered monitoring that understands context, reduces noise, and helps engineers catch issues before they escalate.

Grepr SQL Operators let you reshape live log data into real time insights using simple SQL, giving you full control over metrics, traces, and alerts.

Grepr connects directly with Datadog to reduce log volume and costs by up to 90 percent while keeping every log accessible for analysis and compliance.

Grepr decouples data collection from observability platforms, cutting costs and eliminating vendor lock-in while retaining complete visibility and control.

Grepr uses unsupervised machine learning to reduce log volume by over 90% while preserving important data through smart, configurable aggregation. It passes low-frequency messages through unmodified, allows engineers to retain specific parameters like user IDs, and supports backfilling logs via API triggers when deeper detail is needed—such as during support tickets. For added flexibility, trace sampling can capture full logs for a subset of users, and all original logs are archived in a searchable data lake. This gives teams control, reduces noise, and enables cost-effective observability without sacrificing access to critical information.

Grepr helps teams keep full visibility while reducing observability data volume and costs through intelligent summarization and instant backfill.

Vector and Grepr both route observability data between sources and sinks, but they take fundamentally different approaches. Vector offers extensive manual configuration options, while Grepr uses machine learning to automatically optimize your data pipeline and cut costs by 90%.

Grepr reduces Datadog browser logging costs by 90% by receiving all frontend logs, storing them cost-effectively, and using AI to filter repetitive data while maintaining full query access to original logs through a Datadog-compatible dashboard.

This tutorial demonstrates reducing log volume by 90% by adding Grepr between Fluent Bit and New Relic to filter noise while retaining raw data in low-cost storage for on-demand backfilling.

Goldsky's logging costs had outpaced their value. After a four-week rollout, Grepr reduced their indexed logs from 5.7 billion to 250 million messages, cutting Datadog spend by 96% while keeping their observability workflows intact.

Grepr uses AI to automate observability data filtering with 90% less manual configuration than Cribl's powerful but complex platform that requires dedicated teams and custom query language expertise.

Grepr uses machine learning to filter observability noise at ingest, storing all raw data in low-cost storage while forwarding only meaningful signals to your existing tools like Datadog, Splunk, and New Relic.

Grepr is an intelligent observability engine that uses machine learning and a rules engine to detect patterns, suppress noise, and forward only essential data to your existing platforms, cutting costs by up to 90% with a single configuration change.

Grepr automatically parses your existing Datadog alerts and routes the logs that power them through unchanged, letting you cut observability costs in production without rewriting a single alert.

Grepr receives logs from Splunk Heavy Forwarders via S2S, compresses data by 90% while retaining everything in a queryable data lake with SPL support and one-click backfill to Splunk when needed.

Grepr's semantic pipeline automatically masks variable data, clusters similar messages, and samples or summarizes noisy patterns in real time, reducing log volume by 90% or more without dropping a single message.

Grepr retains all your logs in low-cost storage and backfills exactly what you need into your existing tools the moment an incident hits, so you never lose the data that explains what went wrong.

Grepr uses distributed stream partitioning and a forgetful Bloom filter to guarantee complete log sets for sampled traces, without exhausting memory or disrupting your existing workflows.

Discover how Grepr's intelligent log management solution can reduce your logging costs by 90% without sacrificing visibility. Our two-tier storage system uses machine learning to identify patterns and store less critical logs in low-cost storage, while maintaining immediate access to important data. When incidents occur, Grepr's dynamic backfill feature automatically retrieves relevant logs to your existing tools. Implement smarter logging today without changing your workflows or compromising on troubleshooting capabilities.

Storing logs long-term doesn't have to be super expensive. Using a data lake can reduce storage costs by more than 90% while still keeping the logs queryable and immediately accessible.

Three advanced techniques for reducing log volumes by 90% or more, including automatic pattern sampling, logarithmic sampling, and sampling with automatic backfilling, each designed to scale for enterprise environments without sacrificing critical troubleshooting data.

Grepr's Monitored Objects approach groups metrics by entity, aggregates normal behavior, and only sends detailed data to Datadog when anomalies occur, dramatically reducing billable custom metrics and hosts without sacrificing troubleshooting capability.

Observability demands low-latency queries and flexible schemas that most open-source data lake tools can't deliver. Here are six ways Grepr closes that gap.

Four foundational techniques for reducing log volumes, including severity thresholds, log-to-metrics conversion, uniform sampling, and drop rules, along with the tradeoffs engineering teams should weigh before implementing each one.

No engineer has ever been excited about configuring tools to drop metrics and logs just to stay within budget. But that's exactly what observability has become for most teams: an expensive, frustrating compromise between cost and visibility. Grepr was built to end that tradeoff.