Best Database Performance Monitoring Tools: Top 5 Platforms Compared
Database issues cause application failures: A memory spike crashes your server, and a slow query times out user requests.
We analyzed six database monitoring platforms and benchmarked three of them extensively on MySQL and MongoDB by installing them from scratch, running identical workloads, and documenting every step of the setup and monitoring experience. The results show significant differences in setup complexity, query analysis capabilities, and metric accuracy:
DB monitoring benchmark results
We tested SolarWinds, New Relic, and Datadog with real database workloads on MySQL and MongoDB. All three platforms have received significant updates since testing. See individual vendor sections for the latest capabilities, including Datadog’s Query Regression Detection, Dynatrace’s Database Monitoring app, and Percona PMM’s platform independence updates
- Setup experience: SolarWinds completed integration in 5-8 minutes with automatic detection. New Relic and Datadog were slower and required manual configuration.
- Query profiling: Only SolarWinds provides query-level analysis that identifies slow queries, missing indexes, and resource-intensive operations.
- Metric accuracy: SolarWinds tracked operations with 100% accuracy. New Relic significantly undercounted operations in both tests and missed a memory spike entirely.
- Resource consumption: All three agents remained lightweight.
For database-specific benchmarks with detailed installation steps, resource consumption data, and dashboard comparisons:
- MySQL Monitoring: Setup process, query profiling, metric accuracy with 26GB import workload
- MongoDB Monitoring: NoSQL features, dashboard quality with document inserts
On-Premises Database Coverage
All providers support these databases: MySQL, PostgreSQL, MongoDB, MariaDB, Redis.
Cloud Database Support
Compare database monitoring tools:
Ratings are gathered from B2B review websites.
What Database Monitoring Actually Does
Database monitoring tracks performance, security, and availability in real-time. The goal: catch problems before users notice them.
What gets monitored:
- Resource usage (CPU, memory, disk I/O)
- Query execution times and patterns
- Connection counts and availability
- Error rates and types
- Security events and access anomalies
Best Database Performance Monitoring Platforms
1. SolarWinds Database Performance Analyzer
SolarWinds Database Performance Analyzer focuses on wait-time analysis instead of just tracking basic metrics. When your database slows down, it shows exactly which queries are waiting and why, whether it’s disk I/O, locks, or CPU constraints.
Key differences:
- Machine learning baselines adapt to your specific database patterns
- Query analysis works across SQL Server, Oracle, MySQL, PostgreSQL, and MongoDB in one interface
- Historical anomaly detection goes back months to compare current issues with past incidents
- Recommendations link directly to specific query execution plans
2. LogicMonitor
LogicMonitor uses an agentless architecture for monitoring databases across hybrid IT environments. Instead of installing software on each database server, a lightweight collector queries databases via standard APIs and protocols.
Key differences:
- Event Intelligence: Ingests metrics, logs, traces, and tickets into a single correlated stream with cross-domain correlation, reducing alert noise by 90-95 1
- AI Agents: Purpose-built frontier AI agents manage the full incident lifecycle with conversational troubleshooting, root cause analysis using correlated data patterns, and automated remediation
- Database-Specific Features: Automatically detects anomalies in slow queries, connection spikes, and resource bottlenecks with plain language incident summaries
- 3,000+ Tool Integrations: Connects observability, APM, security, and CMDB tools for unified incident response
3. Percona Monitoring and Management (PMM)
Percona focuses on open-source databases, with deep expertise in MySQL, PostgreSQL, and MongoDB. The platform provides query analytics and performance optimization tools without the enterprise software licensing costs.
Key differences:
- Built-in Advisors: All database advisors and alert templates (previously tiered as Basic, Standard, Premium) are now included by default with no subscription required. Works completely offline without internet dependencies.
- Valkey & Redis Support: Native monitoring for Valkey (high-performance Redis alternative) and Redis with ten dedicated dashboards covering performance, latency detection, replication issues, and bottleneck troubleshooting.
- PostgreSQL 18: Full support for PostgreSQL 18 Community Edition.
- Enterprise Kubernetes Features (PMM 3.4.0): Full OpenShift 4.16 support for both Client and Server deployments. Centralized VMagent configuration via environment variables automatically applies settings across all connected clients, optimizing Kubernetes shared storage usage
4. Dynatrace
Dynatrace delivers AI-powered observability with its Davis AI engine, providing insights from frontend to database with focus on user experience.
Database Monitoring App: Launched a dedicated database monitoring application providing unified visibility across the entire database estate with proactive health scoring, query-level analytics capturing actual execution plans, and seamless integration connecting database performance to application monitoring for faster root cause analysis.
Enhanced Davis AI Capabilities: Expanded predictive, causal, and generative AI capabilities, including:
- AI-powered artifact generation for automated remediation workflows (e.g., Kubernetes deployment resource adjustments)
- Natural language problem summaries with specific remediation steps
- Intelligent knowledge base learning from historical incidents for preventive operations
Business Observability: Dynatrace Intelligence positioned as first “agentic OS,” combining Causal AI with Smartscape topology for autonomous operations. Grail data lakehouse provides unified context across databases, AI models, applications, and infrastructure.
5. New Relic
New Relic treats databases as part of application performance monitoring rather than as isolated infrastructure. Their approach connects database calls to specific transactions in your code.
Key differences:
- Transaction tracing shows the full path from user request through database queries
- Slow query analysis includes the exact line of application code that triggered each query
- Dashboard operations for read-only users limited to view-only
- Supports MySQL, PostgreSQL, MongoDB, Redis, and Elasticsearch
- Enhanced database monitoring with RDS Instance ID tracking for Amazon RDS (better correlation between PMM and AWS console)
- Multiple End of Life announcements: infrastructure event drop rules, AI Monitoring UI drop rules, legacy APM embedded charts
6. Datadog Database Monitoring
Datadog integrates database metrics with your entire application stack. You see database performance alongside logs, traces, and infrastructure metrics in the same dashboard.
Key differences:
- Query samples capture actual execution plans and explain statements automatically
- Host-level metrics correlate database CPU with system-level resource usage
- APM traces connect slow queries back to specific application requests
- Works with PostgreSQL, MySQL, SQL Server, Oracle, and cloud databases
- Query Regression Detection: Establishes historical baselines and uses anomaly detection to automatically identify unintended query performance degradations in frequently used queries. System automatically runs diagnostics when query duration increases unexpectedly, helping identify and resolve issues before they impact users.
Standard Features in Database Monitoring Tools
Every database monitoring tool tracks similar metrics, but the depth and presentation vary significantly.
Performance Metrics
CPU Usage: Shows processing power consumption. When CPU hits 80%, your database struggles to handle requests. Spikes happen during complex queries or traffic surges.
Memory Consumption: Tracks RAM usage for caching data and query results. Running out of memory forces the database to read from disk, which is orders of magnitude slower than RAM.
Disk I/O Rates: Measures read/write speed. High I/O bottlenecks your entire system. This reveals whether you need faster storage or if queries scan unnecessary data.
Network Throughput: Monitors data transfer between the database and applications. High network usage may indicate excessive data transfer per query.
Query Execution
Slow Queries: Identifies queries exceeding time thresholds (typically 1-5 seconds). One slow query can lock resources and cascade into system-wide slowness.
Execution Plans: Shows the database’s strategy, which indexes it uses, and how it joins tables. Reveals why queries are slow.
Query Counts: Tracks execution frequency. A moderately slow query running 10,000 times per minute causes more damage than a very slow query running once hourly.
Average Response Times: Establishes baselines for detecting performance degradation.
Connection Monitoring
Active Connections: Each connection consumes memory. Too many connections exhaust resources.
Connection Pool Usage: Tracks how efficiently applications reuse connections. Pooling prevents constant open/close overhead.
Failed Connection Attempts: Signals connection limit hits, network issues, or authentication problems.
Resource Contention
Lock Waits: One query needs data another query has locked. The waiting query sits idle.
Deadlocks: Two queries each wait for locks the other holds. The database must kill one to proceed.
Blocking Sessions: Shows which queries prevent others from running. One long transaction can block dozens.
Storage Tracking
Database Size Growth: Helps capacity planning. You need to know when disk space runs out.
Table Space Usage: Identifies which tables consume most storage.
Index Fragmentation: As data changes, indexes scatter across disk. Fragmented indexes slow queries.
Backup Monitoring
Backup Job Status: Confirms backups actually ran. Failed backups mean no recovery option.
Backup File Sizes: Tracks the size over time. Sudden changes indicate problems.
Recovery Point Objectives: Measures potential data loss. Daily backups risk 24-hour data loss.
Replication Health
Lag Between Primary and Replicas: Shows how far behind replicas run. High lag creates stale data and consistency issues.
Replication Errors: Alerts when data fails to copy to replicas, risking data loss.
Sync Status: Confirms replicas actively receive updates.
Alert Mechanisms
Tools send notifications through email, Slack, PagerDuty (on-call rotations), webhooks (custom integrations), and SMS (critical emergencies).
Dashboard Customization
Ranges from drag-and-drop interfaces (beginner-friendly) to JSON configuration files (powerful but technical).
The key difference: All tools cover these basics. They differ in query analysis depth, database support, and integration quality. Our benchmarks revealed only SolarWinds provided query-level profiling the others showed only aggregate metrics.
Differentiating features analysis
AI and machine learning-powered insights
Database monitoring platforms increasingly leverage AI for predictive and autonomous operations:
AI and machine learning-powered insights
SolarWinds uses ML to predict anomalies based on database patterns. Dynatrace’s Davis AI provides automated, cross-stack root cause analysis, which is crucial for complex, high-transaction environments.
Agentless monitoring
LogicMonitor is the only tool offering agentless monitoring, using a lightweight collector to collect data via standard protocols and APIs, which simplifies deployment in complex hybrid and cloud environments.
Security and compliance features
Datadog stands out with automatic PII obfuscation and granular role-based access control. This automatically scrubs personally identifiable information from query data, ensuring compliance with data protection regulations for regulated industries (e.g., healthcare, financial services).
Full-stack observability
Dynatrace and New Relic provide visibility beyond the database, tracing transactions from end-user interactions through application code down to database queries. This accelerates troubleshooting by providing a comprehensive view of how database performance affects user experience.
Wait-time analysis
SolarWinds excels in wait-time analysis, which focuses on identifying the root cause of database slowness (e.g., disk I/O, lock contention) rather than simply acknowledging that it is slow. This provides more actionable insights for targeted optimization.
Integration ecosystem
Datadog leads with 600+ pre-built integrations, enabling seamless workflows with existing DevOps tools, CI/CD pipelines, and incident management systems.
Common challenges and solutions
Further reading
- Data Transformation: Challenges & Real-life examples
- Data Loss Prevention (DLP) Software
- Top 13 Training Data Platforms
Reference Links
Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE and NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and resources that referenced AIMultiple.
Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.
He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.
Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.
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