9 Best Database Performance Monitoring Tools in 2027

Database performance directly affects application reliability, response times, and the overall user experience. Slow queries, resource contention, connection bottlenecks, locking issues, and inefficient execution plans can make applications unresponsive even when the underlying database remains available. As organizations manage larger databases and distributed workloads, identifying these problems before they affect users becomes increasingly important.

Database performance monitoring tools help teams understand how database systems behave under different workloads. They collect performance metrics, track query execution, monitor resource consumption, detect anomalies, and provide diagnostic information that helps database administrators and developers investigate bottlenecks. Some platforms focus specifically on database performance, while others combine database monitoring with application performance monitoring and infrastructure observability.

The available tools also differ in the database systems they support. Organizations running SQL Server, Oracle, PostgreSQL, MySQL, or MongoDB may need a platform that provides visibility across multiple database engines. PostgreSQL-focused teams may prefer specialized query analysis, while cloud-native organizations may benefit from monitoring services integrated with AWS or other cloud platforms.

In this guide, we compare nine database performance monitoring tools based on their database compatibility, query-level diagnostics, performance metrics, alerting capabilities, deployment options, and pricing. The goal is to help you identify a monitoring platform that fits your database environment and makes it easier to detect, investigate, and resolve performance issues.

Why Do You Need Database Performance Monitoring Tools?

Database performance monitoring tools help teams track database health, identify performance bottlenecks, and investigate issues that affect query execution and application responsiveness. They collect information such as CPU utilization, memory consumption, disk activity, query latency, database load, and wait events to provide visibility into how database systems perform.

Without continuous monitoring, teams may discover database problems only after users experience slow applications, failed requests, or service interruptions. Monitoring platforms provide historical performance data, alerts, and diagnostic information that help teams investigate issues and take corrective action.

Key reasons to use database performance monitoring tools include:

• Identify slow queries: Track query execution time, frequency, resource consumption, and other performance indicators to find statements that affect database responsiveness.
• Monitor resource utilization: Observe CPU, memory, disk I/O, connections, and other infrastructure metrics to identify resource bottlenecks.
• Detect blocking and locking: Identify blocked sessions, long-running transactions, deadlocks, and wait events that can delay database operations.
• Improve availability: Configure alerts for performance anomalies, resource thresholds, and other conditions that could affect database health.
• Understand historical trends: Review performance data over time to identify recurring issues, workload changes, and the impact of deployments.
• Troubleshoot application problems: Correlate database activity with application traces and infrastructure metrics to understand where performance issues originate.

Database Performance Monitoring Tools Comparison

Compare the leading database performance monitoring tools based on their database coverage, query diagnostics, performance analytics, alerting capabilities, deployment options, and pricing. The table below highlights the key differences to help you evaluate each platform for your database monitoring requirements.

Tool	Monitoring Approach	Database Support	Best For	Open Source	Pricing	G2 Rating
Datadog Database Monitoring	Full-stack observability and query-level monitoring	PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, MongoDB, ClickHouse, and more	Monitoring database performance alongside applications	No	Paid; usage-based	N/A
Dynatrace	AI-powered database observability	PostgreSQL, MySQL, SQL Server, Oracle, and supported cloud databases	Enterprise database monitoring and root-cause analysis	No	Paid; consumption-based	N/A
New Relic Database 360	Database, application, and infrastructure observability	MySQL, PostgreSQL, Oracle, SQL Server, and supported cloud environments	Correlating database performance with application activity	No	Free tier; paid usage-based plans	N/A
SolarWinds Database Performance Analyzer	Wait-based database performance analysis	SQL Server, Oracle, PostgreSQL, MySQL, Db2, and others	Query bottleneck investigation and database tuning	No	Paid; subscription and licensing options	N/A
Redgate Monitor	Dedicated multi-platform database monitoring	SQL Server, PostgreSQL, Oracle, MySQL, and MongoDB	Monitoring mixed database environments	No	Paid; per-server or monitored-resource licensing	4.4/5
Percona Monitoring and Management (PMM)	Open-source database observability	MySQL, PostgreSQL, MongoDB, and related database environments	Self-hosted database performance monitoring	Yes	Free; open source	N/A
pganalyze	PostgreSQL query and performance monitoring	PostgreSQL	PostgreSQL query diagnostics and index analysis	No; commercial product	Paid; free trial	N/A
Grafana Cloud Database Observability	Database metrics, logs, and query monitoring	MySQL and PostgreSQL	Database observability with Grafana dashboards	No; managed cloud service	Free entry options; paid plans	N/A
AWS CloudWatch Database Insights	AWS-native database performance monitoring	Amazon RDS, Aurora, and supported PostgreSQL environments	Monitoring AWS database workloads	No	AWS service pricing	N/A

Leading Database Performance Monitoring Software to Consider in 2027

Database performance monitoring platforms serve different operational requirements. Some provide dedicated database diagnostics and query analysis, while others correlate database metrics with application traces, infrastructure telemetry, and logs. Cloud-native services also provide monitoring capabilities tailored to databases running within their respective ecosystems.

Here are nine database performance monitoring tools worth evaluating in 2027, along with their monitoring features, supported database environments, and practical applications.

#1. Datadog Database Monitoring

Datadog Database Monitoring is part of the Datadog observability platform and helps teams investigate database performance issues across self-managed and cloud-hosted environments. It provides visibility into database activity, query execution, resource utilization, and performance trends. The platform supports several database systems, including PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, MongoDB, Amazon DocumentDB, and ClickHouse.

Its query monitoring capabilities help users identify expensive SQL statements, slow queries, and performance changes over time. Query Metrics provides historical statistics for normalized queries, while Query Samples helps teams investigate individual executions and identify unusual performance behavior. Datadog also provides explain plans for supported databases, allowing users to examine how statements are executed and identify potential bottlenecks.

Another important capability is its integration with application performance monitoring and infrastructure telemetry. Teams can correlate database queries with application traces and host-level metrics to understand how database performance affects services. Custom dashboards, monitors, and alerting help organizations track database health across multiple environments. Datadog is particularly useful for teams that already use its observability platform and want database monitoring within the same environment.

Key Features

• Query performance monitoring: Tracks normalized query performance, execution trends, and resource consumption to help identify expensive database statements.
• Query samples: Provides details about individual query executions to help investigate unusual latency and query behavior.
• Explain plans: Helps users examine database execution plans and identify operations that contribute to query bottlenecks.
• Multi-database support: Monitors supported PostgreSQL, MySQL, MariaDB, Oracle, SQL Server, MongoDB, and ClickHouse environments.
• Application correlation: Connects database activity with distributed traces and application performance information to support root-cause analysis.
• Alerts and dashboards: Provides configurable monitors and dashboards for tracking database health and identifying performance anomalies.

G2 Rating: N/A

#2. Dynatrace

Dynatrace is an enterprise observability platform that provides database monitoring alongside application performance, infrastructure, and distributed tracing capabilities. Its database monitoring features help organizations track database health, query performance, resource consumption, and performance anomalies across cloud and on-premises environments. Supported database technologies include PostgreSQL, MySQL, Microsoft SQL Server, Oracle Database, and selected managed database services.

The platform provides visibility into database resource utilization, including CPU, memory, I/O, and query execution. It helps identify slow queries, inefficient execution plans, and resource contention that can affect application performance. Dynatrace also uses its AI-assisted analysis capabilities to help investigate performance anomalies and identify potential root causes across connected systems.

One of Dynatrace’s key strengths is its ability to correlate database performance with application and infrastructure telemetry. Teams can investigate whether a slow database query is affecting an application service, whether infrastructure resource constraints are contributing to latency, or whether a database issue started after a deployment. Its unified observability approach makes it useful for enterprises that need performance monitoring across complex application environments.

Key Features

• Database performance visibility: Tracks database health, resource consumption, and query execution across supported database environments.
• Slow query detection: Helps identify slow SQL statements and inefficient query execution patterns that affect application responsiveness.
• AI-assisted root-cause analysis: Uses Dynatrace’s analysis capabilities to investigate anomalies, performance issues, and potential bottlenecks.
• Execution plan insights: Provides diagnostic information about query execution to help users investigate inefficient database operations.
• Full-stack correlation: Connects database metrics with application performance, logs, traces, and infrastructure information.
• Enterprise monitoring: Supports monitoring across cloud and on-premises database environments through its observability platform.

G2 Rating: N/A

#3. New Relic Database 360

New Relic provides database monitoring through its broader observability platform, including Database 360 capabilities built around an OpenTelemetry-based collection approach. It helps engineering teams investigate database performance by connecting database entities with application services and infrastructure metrics. This provides a broader view of database behavior than monitoring individual performance counters alone.

Database 360 provides visibility into query execution plans, wait states, locks, and database activity. It helps teams identify long-running sessions, blocking queries, and resource-heavy workloads that may affect application performance. Its database monitoring capabilities also connect database information with application traces, allowing developers to investigate which database operations are associated with slow application requests.

The platform supports monitoring for MySQL, PostgreSQL, Oracle Database, and Microsoft SQL Server through its documented Database 360 environment. It uses OpenTelemetry collection components, including the New Relic Distribution of OpenTelemetry Collector, to gather relevant database and infrastructure information. New Relic is useful for teams that want database performance monitoring within an existing application observability workflow rather than managing a separate database-only platform.

Key Features

• Database 360 monitoring: Provides a unified view of database entities, application services, and infrastructure metrics.
• Query execution plans: Helps users examine query execution paths and identify potential inefficiencies.
• Wait-state monitoring: Tracks database wait events, locks, and long-running sessions that can affect workload performance.
• Application-to-database correlation: Links application traces with the database queries and sessions responsible for processing requests.
• Infrastructure context: Displays database information alongside CPU, memory, I/O, and other host-level metrics.
• OpenTelemetry collection: Uses supported OpenTelemetry collection components to gather database performance information.

G2 Rating: N/A

#4. SolarWinds Database Performance Analyzer

SolarWinds Database Performance Analyzer (DPA) is a database performance monitoring and analysis platform designed to help teams identify and investigate database bottlenecks. It supports multiple database systems, including SQL Server, Oracle, PostgreSQL, MySQL, Db2, and other supported platforms. Organizations can use it to monitor database workloads across on-premises, cloud, and virtualized environments.

One of DPA’s central capabilities is wait-based analysis. Instead of relying only on general resource metrics, the platform examines how database activity spends time waiting for resources. This helps database administrators identify whether performance problems are related to I/O, locking, CPU, or other database wait conditions. Its query-level analysis also helps users identify SQL statements that contribute significantly to database response time.

DPA provides performance dashboards, historical reporting, anomaly detection, and tuning recommendations. Its agentless architecture is designed to reduce the monitoring overhead on production database systems. The platform also supports monitoring database workloads hosted on public cloud infrastructure, including AWS and Azure. SolarWinds DPA is particularly useful for database teams that need detailed wait-time diagnostics and cross-platform performance analysis.

Key Features

• Wait-based analysis: Identifies database wait events and helps prioritize the bottlenecks that contribute most to application response time.
• SQL query analysis: Tracks SQL statements and helps users investigate queries responsible for database performance issues.
• Cross-platform monitoring: Supports multiple database systems, including SQL Server, Oracle, PostgreSQL, MySQL, and Db2.
• Performance dashboards: Provides charts and historical metrics for reviewing database activity and performance trends.
• Anomaly detection: Uses machine learning capabilities to help identify unusual database performance behavior.
• Tuning recommendations: Provides diagnostic guidance to help database administrators investigate query and resource-related bottlenecks.

G2 Rating: N/A

#5. Redgate Monitor

Redgate Monitor is a dedicated database monitoring platform that helps database administrators track performance, availability, and operational health across database environments. It supports SQL Server, PostgreSQL, Oracle, MySQL, and MongoDB, allowing organizations to monitor different database systems through a unified interface. It can monitor on-premises, cloud-hosted, and hybrid database environments.

The platform collects database and server metrics, including CPU utilization, memory consumption, disk activity, connections, query performance, and other workload information. Users can review current and historical performance data, investigate slow queries, identify blocking activity, and track database resource consumption. Its query monitoring capabilities help teams understand which statements are consuming resources and contributing to performance problems.

Redgate Monitor also provides configurable alerts, dashboards, reporting, and deployment tracking. Its self-hosted and SaaS deployment options give organizations flexibility based on their infrastructure and operational requirements. Enterprise editions provide additional capabilities for larger and more regulated environments. Redgate Monitor is a strong option for DBA teams that want dedicated database monitoring rather than a general-purpose observability platform.

Key Features

• Multi-platform database monitoring: Supports SQL Server, PostgreSQL, Oracle, MySQL, and MongoDB through a centralized monitoring interface.
• Query performance analysis: Helps identify expensive and slow queries using execution statistics and available query diagnostics.
• Resource monitoring: Tracks server and database metrics such as CPU, memory, disk usage, connections, and workload activity.
• Intelligent alerting: Provides configurable alerts to notify teams about performance issues and database health conditions.
• Historical performance analysis: Allows users to investigate performance trends and compare database behavior over time.
• Deployment tracking: Helps teams understand how database deployments and changes affect monitored environments.

G2 Rating: 4.4/5

#6. Percona Monitoring and Management (PMM)

Percona Monitoring and Management (PMM) is an open-source database observability and monitoring platform designed for organizations that manage their own database infrastructure. It provides performance monitoring for MySQL, PostgreSQL, and MongoDB environments, with visibility ranging from database clusters to individual queries. PMM can be deployed in on-premises, cloud, and hybrid environments.

The platform provides dashboards for monitoring database performance, resource utilization, query activity, and server health. Its query analytics capabilities help users identify expensive SQL statements and understand how database workloads behave over time. Database administrators can investigate query patterns, examine performance metrics, and use the available information to identify potential bottlenecks.

PMM also integrates with monitoring and visualization technologies to provide a unified view of database infrastructure. Its open-source deployment model gives technical teams control over how monitoring data is collected and stored. Organizations can install PMM using its server and client components and configure monitoring for supported database instances. It is particularly useful for teams that want database observability without purchasing a commercial monitoring subscription, although deployment and maintenance require internal technical resources.

Key Features

• Open-source monitoring: Provides a self-hosted database observability platform that organizations can deploy and manage themselves.
• Multi-database support: Monitors MySQL, PostgreSQL, and MongoDB environments through its collection and monitoring components.
• Query analytics: Provides query-level performance information to help users identify expensive statements and investigate database workloads.
• Centralized dashboards: Combines database and server metrics in a unified interface for monitoring database health.
• Flexible deployment: Supports on-premises, cloud, and hybrid infrastructure environments.
• Performance visibility: Helps teams examine database resource usage and query behavior to identify performance bottlenecks.

G2 Rating: N/A

#7. pganalyze

pganalyze is a PostgreSQL performance monitoring and optimization platform designed specifically for teams that manage PostgreSQL databases. It collects query statistics, database activity, wait events, and other performance information to help users identify slow queries and investigate database bottlenecks. It supports self-managed PostgreSQL installations as well as supported managed environments, including Amazon RDS, Aurora, Azure Database for PostgreSQL, and Google Cloud SQL.

Its Query Performance interface uses PostgreSQL query statistics to help teams examine execution patterns and identify statements that consume significant database resources. Users can review query latency, execution frequency, and workload trends, then investigate individual queries through available samples and execution plans. This helps developers understand which SQL statements contribute most to database load.

pganalyze also provides Index Advisor capabilities, VACUUM insights, alerts, and database health checks. These features help teams investigate missing indexes, unused indexes, autovacuum behavior, and other PostgreSQL-specific performance concerns. It is available as a cloud service and through a self-hosted Enterprise Server option. Unlike general-purpose monitoring platforms, pganalyze focuses on PostgreSQL, making it particularly useful for teams that need deeper diagnostics for that database engine.

Key Features

• Query performance tracking: Collects PostgreSQL query statistics and helps users identify slow or resource-intensive statements.
• Query plan analysis: Provides access to execution plan information to help investigate query behavior and performance changes.
• Index Advisor: Identifies potential missing or unused indexes and provides recommendations for improving query performance.
• VACUUM insights: Helps users investigate autovacuum behavior and database maintenance-related performance concerns.
• Wait-event monitoring: Tracks database waits and activity to help identify bottlenecks and resource contention.
• Health checks and alerts: Runs database checks and notifies teams about potential performance and availability issues.

G2 Rating: N/A

#8. Grafana Cloud Database Observability

Grafana Cloud Database Observability is a managed monitoring solution that provides performance visibility for MySQL and PostgreSQL databases. It collects database metrics and logs through Grafana Alloy and presents query-level performance information through dashboards and diagnostic workflows. The platform supports self-managed database servers as well as supported managed services, including Amazon RDS, Amazon Aurora, Azure Database, and Google Cloud SQL.

Its monitoring capabilities help teams track query execution times, error rates, throughput, and other performance indicators. Users can investigate query patterns, identify slow statements, and examine query samples to understand specific database executions. The platform also supports visual explain plans, helping database teams understand how queries are processed and where execution time is spent.

Grafana Cloud Database Observability can connect database activity with application traces and other observability signals. This makes it easier to investigate whether performance issues originate in the database or are associated with application behavior. Because it is part of Grafana Cloud, teams can use managed dashboards and monitoring infrastructure rather than operating the entire observability stack themselves. It is a useful option for organizations that already use Grafana or want managed database observability for MySQL and PostgreSQL.

Key Features

• MySQL and PostgreSQL monitoring: Provides database performance visibility for supported self-managed and managed database environments.
• Query performance dashboards: Tracks query execution time, errors, throughput, and other database performance indicators.
• Query samples: Helps users investigate individual query executions and understand unusual performance behavior.
• Visual explain plans: Displays query execution plans to help identify inefficient operations and potential bottlenecks.
• Application trace correlation: Connects database query information with application traces for more detailed troubleshooting.
• Managed observability: Uses Grafana Cloud infrastructure and Alloy-based collection to simplify database monitoring deployment.

G2 Rating: N/A

#9. AWS CloudWatch Database Insights

AWS CloudWatch Database Insights is an AWS-native monitoring service designed to help organizations monitor and troubleshoot database performance across supported Amazon RDS and Aurora environments. It provides fleet-level dashboards, database metrics, query performance information, and diagnostic capabilities within the Amazon CloudWatch ecosystem. This makes it particularly useful for organizations that already operate their databases on AWS.

Database Insights supports several database engines and services, including Amazon Aurora MySQL, Aurora PostgreSQL, Aurora DSQL, RDS for MySQL, PostgreSQL, MariaDB, Oracle, and SQL Server, along with supported self-managed PostgreSQL environments. Its dashboards provide an overview of database fleet health and allow teams to investigate individual database instances. Users can examine database load, identify performance contributors, and review query-related information depending on the selected monitoring mode.

The service provides Standard and Advanced modes with different diagnostic capabilities and retention options. Advanced mode adds features such as detailed per-query statistics, operating system process analysis, and additional query diagnostics for supported engines. AWS migrated Performance Insights users to Database Insights following the July 31, 2026 end-of-life date for Performance Insights. Organizations monitoring AWS databases should therefore evaluate Database Insights as the current native monitoring experience rather than relying on the retired service name.

Key Features

• AWS database monitoring: Supports performance monitoring for selected Amazon RDS, Aurora, and self-managed PostgreSQL environments.
• Fleet health dashboards: Provides centralized dashboards for reviewing the health and performance of multiple database instances.
• Database load analysis: Helps users identify the database activity and performance contributors responsible for increased load.
• Query performance insights: Provides query-related performance information and diagnostics based on the selected monitoring mode.
• Advanced diagnostics: Offers additional capabilities such as per-query statistics, operating system process analysis, and selected execution-plan insights.
• CloudWatch integration: Connects database monitoring with AWS metrics, dashboards, alarms, and other CloudWatch capabilities.

G2 Rating: N/A

Conclusion

Database performance monitoring tools help organizations maintain responsive, reliable database environments by identifying slow queries, resource bottlenecks, blocking activity, and other performance issues. They provide the metrics and diagnostic information needed to understand database behavior and investigate problems before they significantly affect applications.

Datadog, Dynatrace, and New Relic are useful options for organizations that want database monitoring connected with application performance and infrastructure observability. SolarWinds Database Performance Analyzer and Redgate Monitor provide dedicated database monitoring capabilities, with detailed performance diagnostics for different database environments.

For teams that prefer open-source monitoring, Percona Monitoring and Management offers a self-hosted approach for MySQL, PostgreSQL, and MongoDB. pganalyze provides deeper PostgreSQL-specific performance analysis, while Grafana Cloud Database Observability focuses on managed MySQL and PostgreSQL monitoring. AWS CloudWatch Database Insights is a natural option for organizations running supported database workloads on AWS.

The right platform depends on your database engines, query-level diagnostic requirements, infrastructure, deployment preferences, and monitoring budget. Before selecting a tool, evaluate how much performance detail it provides, which databases it supports, and how easily your team can use its alerts and diagnostics to resolve real production issues.

Summary: Best Database Performance Monitoring Tools

Tool	Best Use Case
Datadog Database Monitoring	Full-stack observability and database query monitoring
Dynatrace	Enterprise database monitoring and AI-assisted root-cause analysis
New Relic Database 360	Correlating database performance with application and infrastructure activity
SolarWinds Database Performance Analyzer	Wait-based performance analysis and query diagnostics
Redgate Monitor	Dedicated monitoring across mixed database environments
Percona Monitoring and Management	Open-source, self-hosted database observability
pganalyze	PostgreSQL query performance and index analysis
Grafana Cloud Database Observability	Managed MySQL and PostgreSQL performance monitoring
AWS CloudWatch Database Insights	Native monitoring for supported AWS database services

Frequently Asked Questions

1. What are database performance monitoring tools?

Database performance monitoring tools help teams track database health, query execution, resource utilization, and performance trends. They collect metrics such as CPU usage, memory consumption, disk activity, query latency, wait events, and database load to help identify bottlenecks and investigate performance issues.

2. What are the best database performance monitoring tools in 2027?

Leading database performance monitoring tools in 2027 include Datadog Database Monitoring, Dynatrace, New Relic Database 360, SolarWinds Database Performance Analyzer, Redgate Monitor, Percona Monitoring and Management, pganalyze, Grafana Cloud Database Observability, and AWS CloudWatch Database Insights. These platforms support different requirements, from query-level diagnostics and PostgreSQL monitoring to enterprise observability and cloud-native database monitoring.

3. What are some free database performance monitoring tools available?

Some database performance monitoring tools offer free or open-source options. Percona Monitoring and Management provides an open-source platform for monitoring MySQL, PostgreSQL, and MongoDB. Grafana’s open-source stack can also be used to build database monitoring dashboards with suitable exporters and integrations. Other platforms, including Datadog, Dynatrace, New Relic, and pganalyze, offer trial periods or free entry options with limitations.

4. What are some open-source database performance monitoring tools?

Some open-source database performance monitoring tools and projects include:

• Percona Monitoring and Management: Provides open-source monitoring, dashboards, and query analytics for MySQL, PostgreSQL, and MongoDB.
• Grafana: Provides open-source dashboards and visualization capabilities for database metrics collected through compatible integrations.
• Prometheus: Collects and stores time-series metrics from databases through exporters and integrations.
• pgwatch: Provides an open-source monitoring solution focused on PostgreSQL performance and health.

These tools may require additional configuration, exporters, infrastructure, and technical maintenance to build a complete monitoring environment.

5. What is the difference between database monitoring and database performance monitoring?

Database monitoring covers the broader health and availability of a database system, including uptime, connectivity, resource usage, and operational status. Database performance monitoring focuses more specifically on how efficiently the database processes workloads, including query execution, latency, wait events, locking, and resource bottlenecks. Many database monitoring platforms provide both capabilities.

6. Which database performance monitoring tools support multiple database systems?

Datadog, Dynatrace, SolarWinds Database Performance Analyzer, Redgate Monitor, and Percona Monitoring and Management support multiple database technologies. Their supported engines differ, so organizations should verify compatibility with their specific database versions and deployment environments before selecting a platform.

7. How do database performance monitoring tools identify slow queries?

Database performance monitoring tools collect query statistics and execution information from database systems. They examine metrics such as execution duration, frequency, resource consumption, wait events, and query plans to identify statements that contribute significantly to database load. Some platforms also collect individual query samples to investigate unusual executions.

8. Can database performance monitoring tools detect locking and blocking issues?

Yes. Several platforms provide visibility into database locks, blocked sessions, wait events, and long-running transactions. These capabilities help database administrators identify queries that are waiting for resources or preventing other transactions from completing. New Relic Database 360, SolarWinds DPA, and other dedicated monitoring platforms provide relevant diagnostic capabilities, depending on the database engine.

9. Which database performance monitoring tools are best for PostgreSQL?

pganalyze is a specialized option for PostgreSQL performance monitoring, with query statistics, execution plans, index analysis, and VACUUM insights. Percona Monitoring and Management and Grafana Cloud Database Observability also support PostgreSQL monitoring. Teams managing PostgreSQL alongside other database systems may prefer broader platforms such as Datadog or Redgate Monitor.

10. Which database performance monitoring tools are best for SQL Server?

Redgate Monitor, SolarWinds Database Performance Analyzer, Datadog, Dynatrace, and New Relic are options for SQL Server performance monitoring. SQL Server teams should evaluate query execution statistics, blocking analysis, execution plans, resource monitoring, alerting, and historical performance data when comparing these platforms.

11. Can database performance monitoring tools monitor cloud databases?

Yes. Many platforms support cloud-hosted databases, including Amazon RDS, Amazon Aurora, Azure Database services, and Google Cloud SQL. AWS CloudWatch Database Insights is designed for supported AWS database environments, while Datadog, Dynatrace, Redgate Monitor, and other platforms offer monitoring integrations for selected cloud databases.

12. What metrics should you monitor for database performance?

Important database performance metrics include:

• Query latency: Measures how long database queries take to execute.
• Database load: Indicates the amount of work being processed by the database.
• CPU utilization: Helps identify processor-related resource constraints.
• Memory utilization: Tracks memory consumption and potential memory pressure.
• Disk I/O: Measures database storage activity and read/write performance.
• Connection usage: Tracks active and available database connections.
• Lock waits: Identifies transactions waiting for database locks.
• Query throughput: Measures how many queries or transactions the database processes over time.
• Replication lag: Tracks delays between primary and replica database systems.

13. What should you consider when choosing a database performance monitoring tool?

Consider the following factors before selecting a platform:

• Database compatibility: Confirm that the tool supports your database engines, versions, and cloud environments.
• Query-level diagnostics: Evaluate whether it provides query statistics, samples, execution plans, and wait-event analysis.
• Resource monitoring: Check its ability to track CPU, memory, disk, connections, and other database metrics.
• Alerting: Review anomaly detection, configurable thresholds, notifications, and incident integrations.
• Historical data: Determine how long the platform retains performance metrics and query information.
• Deployment: Compare cloud-hosted, self-hosted, and hybrid monitoring options.
• Pricing: Evaluate costs based on monitored instances, hosts, database nodes, telemetry volume, and retention requirements.