Observability and

Gain cost-effective observability for any database to quickly
surface and diagnose latency, errors, and other issues.
Observability and 

Full Visibility into Database Health and Performance

  • Monitor key performance and query metrics: monitor throughput, average query latency, errors, infrastructure consumption, and other critical database metrics.
  • Correlate infrastructure and query metrics: Analyze query latency alongside infrastructure usage to visualize how resource constraints impact database performance.
  • Monitor historical trends: retain metric data for 18 months out-of-the-box to monitor database performance over time.
Full Visibility into Database Health and Performance

Troubleshoot issues quickly with complete database observability

  • Quickly explore database logs: aggregate, monitor, and query database logs to quickly troubleshoot issues.
  • Distributed tracing: Visualize database executions within the context of the entire application request to easily pinpoint costly and slow queries.
  • Correlate your telemetry data: correlate across logs, metrics, and traces to easily gather context during root cause analysis.
  • See it all in one place: unify observability across your entire stack – from your infrastructure, to databases, to custom code.
Troubleshoot issues quickly with complete database observability

Gain Full Database Observability in Minutes

Collect data from any database with integrations including:

  • Telemetry Collector: collect logs, metrics, and traces in a single deployment with’s OpenTelemetry-based agent.
  • Open source technologies: Use popular open source technologies like Prometheus, OpenTelemetry, Fluentd, Telegraph, to collect database telemetry data.
  • Cloud-native integrations: stream log and metrics data directly from database cloud services without installing any software.
Gain Full Database Observability in Minutes

The most cost effective path to database observability

  • Eliminate the noise: Easily identify and filter out unneeded data on the fly with’s Data Optimization Hub.
  • High performance cold storage: send low priority and/or aging telemetry data to cold storage to reduce costs, without compromising on query speed.
  • Manage costs, teams, and data across clusters: Easily monitor data volumes across teams and set caps to prevent bursty data from running up your bill.

Alert on critical database performance metrics

  • Automatically surface new incidents: Alert on sudden slow-downs for database query latency, throughput upticks, errors, or other database performance metrics.
  • Integrate with any alerting end-point: Immediately trigger notifications for production issues on your favorite notification system, including Slack, PagerDuty, email, VictorOps, and other channels.

Simple deployment and zero maintenance at any scale’s cloud-native SaaS platform seamlessly scales with the cloud workloads and telemetry data volumes of Fortune 100 companies. You’ll have constant visibility into the health and performance of your infrastructure and applications without any effort needed to scale your observability infrastructure.

Simple deployment and zero maintenance at any scale


How do I implement database observability?

Observability starts with telemetry data – including logs, metrics, and traces – which contain insights into the current state of your databases (along with the rest of your stack). For this reason, the first step to gaining database observability is often collecting this data and storing it for analysis in an observability platform.

However, observability goes beyond collecting this data. Database observability should quickly answer questions like, what caused this sudden jump in latency? Or why are my databases consuming more infrastructure resources over time?

For this reason, it’s important to correlate signals from your database with other related components, such as the infrastructure that powers your database, or your applications that send requests to your database. Consider diving into full stack observability best practices to learn more.

What are the key metrics for database monitoring?

Consider starting with the four golden signals for cloud monitoring, which include latency, traffic (which may be considered ‘throughput’ in database language), saturation (which measures infrastructure usage), and error rates. The four golden signals are industry standards for ensuring the reliability of modern cloud components.

Many get started with database monitoring before implementing full observability. Dive deeper into the four golden signals here.

How can I reduce the cost of database observability?

Database observability has become increasingly expensive as digital traffic increases and cloud environments become larger, which both create larger telemetry data volumes.

The best way to reduce these costs is by minimizing the computing footprint of large data volumes via data optimization. By implementing data optimization techniques that eliminate useless data and reduce the cost of storage, engineering teams can reduce observability costs by 30-50%. Check out’s automated data optimization capabilities here.

What tools should I use for database observability?

For those interested in basic database monitoring, the best options are open source tools, which include Prometheus, OpenTelemetry, Telegraph, Grafana, Fluentd, FluentBit, and OpenSearch for logs. They’re free and provide everything needed to surface new latency, monitor throughput, and collect other essential database observability metrics.

However, for those who need to troubleshoot and diagnose database performance problems as well, they’ll need an observability platform to unify and correlate the data. At, we pride ourselves on being the most cost effective path to full database observability, but there are plenty of other options, including Datadog, Dynatrace, and New Relic.

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