Buyer’s Guide - August 2026

The 10 best database monitoring tools, ranked by query visibility

Database monitoring is not infrastructure monitoring with a different logo. The hard part is query-level visibility: slow queries, execution plans, lock contention, and replication lag. We ranked ten tools on how deep their query analysis goes, how many engines they cover natively, and what the bill looks like a year in.

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The 10 best database monitoring tools, ranked by query visibility product interface

Why this list exists

Most “database monitoring” shortlists mix two very different categories. One is general observability platforms that show you host CPU and a connection count but cannot tell you which query is eating the database. The other is database-specific tuners that show execution plans in exquisite detail but have no idea the disk underneath is saturated. Buyers get burned when they pick one side and assume they got both.

The tools below span Postgres, MySQL, MongoDB, Redis, Cassandra, ClickHouse, CockroachDB, Elasticsearch, SQL Server, and Oracle. Three dimensions decide which one fits:

Query-level depth. Can it show slow queries, execution plans, wait events, and per-query metrics, or only server-level gauges? This is the sharpest differentiator in the category.
Engine coverage. A native, database-specific collector is not the same as a generic host check. Coverage claims deserve scrutiny engine by engine.
Cost shape. Per-node, per-host, per-instance, per-server, per-monitor, and per-GB bills all grow differently. The cheapest entry point is rarely the cheapest at fleet scale.

One ground rule for this page: we do not quote competitor list prices. List prices for these tools change, are negotiated, and are often meaningless without your fleet size. We describe the pricing shape and what makes the bill grow, and we link each vendor’s pricing page so you can get current numbers. Netdata’s own pricing is stated plainly because it is published and flat. For a focused walkthrough of the discipline itself, see our database monitoring use case page.

The shortlist was assembled from vendor documentation, independent comparison coverage (AIMultiple, Parseable, Geekflare), and hands-on practitioner experience. Every tool here monitors databases as a first-class workload; tools that only check a TCP port were excluded.

Query-level analysis depth carries the most weight because it is the reason the category exists. Engine coverage and cost shape follow, since a tool that misses half your estate or doubles its bill at renewal fails in production no matter how good the demo was. Collection interval matters more than most buyers realize: a 1-minute scrape interval structurally cannot see a 5-second spike.

Tester credit

Compiled by the Netdata team - Updated August 12, 2026

Sources verified

Scoring criteria

Query-level analysis depth 25%
Slow queries, execution plans, wait events, per-query metrics.
Database engine coverage 20%
Native collectors across the ten engines in scope, not generic checks.
Alerting and anomaly detection 15%
ML anomaly detection and noise reduction vs alert floods.
Deployment flexibility 15%
SaaS, self-hosted, air-gapped; agent vs agentless.
Cost shape and scalability 15%
How the bill grows with hosts, instances, servers, or data volume.
Time-to-value 10%
Auto-discovery and time from install to a useful dashboard.

Vendor 01 / 10 · #netdata

Netdata

Real-time database monitoring with per-second collection, ML anomaly detection, and interactive query analysis across 15+ database platforms.

Best for

Teams that want per-second database visibility without assembling a metrics stack
Organizations monitoring many engines (Postgres, MySQL, MongoDB, Redis, Cassandra, ClickHouse, CockroachDB, Elasticsearch, SQL Server, Oracle) with one tool
Buyers who want ML anomaly detection and root-cause assistance without tuning models

Pricing

Per-node: Netdata Cloud Business starts at $4.50/node/month on annual plans, and the per-node price decreases as node count grows
Unlimited metrics, logs, users, and retention included; no per-GB or per-custom-metric charges
Free Cloud tier for small fleets; the agent is open source (GPLv3+) and free for unlimited nodes
Self-hosted path: run the open-source agent and Parent nodes without Netdata Cloud

Pros

Native collectors for 15+ database platforms, covering every engine in this comparison, auto-discovered with zero configuration
Per-second collection across database metrics, 15-60x faster than traditional monitoring intervals, so short query spikes are actually visible
Interactive query analysis for PostgreSQL, MySQL, MongoDB, SQL Server, Oracle, ClickHouse, and Redis: slow queries, running operations, deadlocks
ML anomaly detection runs 18 unsupervised models per metric with a 99% false-positive reduction claim
400+ pre-configured alerts covering slow queries, replication lag, lock contention, and connection utilization
Install to per-second database dashboards in about 60 seconds, with the infrastructure under the database monitored in the same tool

Where teams pair it

Query analysis is newer than dedicated tuners: no per-query execution plan comparison or index advisors like pganalyze or SolarWinds DPA, so deep tuning shops pair it with one of those
Agent-based only: the agent runs on each database host, and teams committed to agentless monitoring should weigh that operational choice
Query-level depth varies by engine; Cassandra and Elasticsearch are covered at the metrics level rather than with interactive query views

Verdict

Netdata is the only tool in this list that combines per-second collection across all ten engines in scope with built-in ML anomaly detection and interactive query analysis, auto-discovered with zero configuration. Because it monitors the host, the disk, and the network alongside the database, it closes the blind spot that pure query tuners leave open. The honest caveat: if your daily work is comparing execution plans and testing index hypotheses on Postgres or Oracle, a dedicated tuner goes deeper, and many teams run both. For fleet-wide database health with query-level triage and a flat per-node bill, it leads the category.

Start free -> See database monitoring ->

Vendor 02 / 10 · #datadog

Datadog

SaaS observability platform with deep query-level database monitoring across Postgres, MySQL, Oracle, SQL Server, MongoDB, and ClickHouse.

Best for

Enterprises already standardized on Datadog for APM, logs, and infrastructure
Teams running cloud-managed databases (RDS, Aurora, Cloud SQL, Azure SQL, AlloyDB)
Organizations that want query metrics correlated with application traces

Pricing

Database Monitoring is priced per database host per month, on top of separate per-host Infrastructure and APM products
The bill grows with database host count, custom metrics, and cardinality
SaaS-only; no self-hosted option

Pros

Query Metrics, Query Samples, and Explain Plans for normalized queries across six major engines
Query Regression Detection uses historical baselines to flag unintended query performance degradation
Correlates database metrics with APM traces to attribute slow queries to application requests
Recommendations page surfaces problems and optimization opportunities with next steps
Broad managed-database support including RDS, Aurora, Cloud SQL, Azure, AlloyDB, and MongoDB Atlas

Cons

SaaS-only: database telemetry leaves your network, with no self-hosted path
Per-database-host pricing layered on separate infra and APM products makes the total bill complex and growing
Setup is more involved than agentless competitors, per AIMultiple’s benchmark
No native Redis, Cassandra, CockroachDB, or Elasticsearch database monitoring

Verdict

Datadog DBM is the deepest multi-engine query analysis in the SaaS category, and the APM correlation genuinely answers “which endpoint triggered this query.” It earns its rank on query depth. The trade-offs are structural: telemetry must leave your network, the pricing is a stack of per-host products that compounds, and full value requires buying into the broader platform. If you are already a Datadog shop, DBM is an easy add; if you are not, the bill deserves a hard look first.

Read full Netdata vs Datadog -> Datadog pricing ->

Vendor 03 / 10 · #solarwinds-dpa

SolarWinds Database Performance Analyzer

Cross-platform database performance analyzer built around wait-time analysis and expert tuning advisors.

Best for

DBAs who want to know why queries wait (I/O, locks, CPU), not just that they are slow
Enterprises running Oracle, SQL Server, PostgreSQL, MySQL, or MariaDB across on-prem and cloud
Teams that prefer agentless monitoring with minimal resource overhead

Pricing

Per-database-instance subscription, with volume licensing available
The bill grows with the number of monitored database instances
Quote-based pricing; 14-day fully functional trial

Pros

Wait-time analysis pinpoints the root cause of slowness: disk I/O, lock contention, CPU
Machine learning baselines adapt to your database patterns and detect anomalies
Expert tuning advisors link recommendations to specific query execution plans
Agentless design with minimal resource usage on monitored systems
Cross-platform coverage: Oracle, SQL Server, PostgreSQL, MySQL, MariaDB, SAP HANA, DB2, Sybase

Cons

Per-instance pricing adds up quickly across large fleets
No MongoDB, Redis, Cassandra, ClickHouse, or Elasticsearch coverage; SQL engines only
Limited broader observability (logs, traces, infrastructure) compared to full-stack platforms
No free tier; pricing is quote-based

Verdict

DPA’s wait-time analysis remains the sharpest root-cause method for relational databases: it tells you what a query waited on, which is usually the actual question. The agentless design and on-prem option suit regulated environments. Its limits are scope and cost shape. It sees nothing of your NoSQL or columnar engines, nothing of the host underneath, and per-instance quotes scale against you as the fleet grows. A strong specialist, not a platform.

Read full Netdata vs SolarWinds -> SolarWinds DPA ->

Vendor 04 / 10 · #percona-pmm

Percona Monitoring and Management (PMM)

Open-source database observability platform with Query Analytics for MySQL, PostgreSQL, MongoDB, and Redis/Valkey.

Best for

Teams running MySQL, PostgreSQL, or MongoDB who want deep query analytics without license costs
Organizations that can operate their own monitoring infrastructure
Percona customers who want advisors aligned with Percona best practices

Pricing

Open source and self-hosted: you run and operate the PMM server, its storage, and its upgrades
No license fee; commercial support contracts sold separately
Real cost is the infrastructure and operator time for the PMM server itself

Pros

Query Analytics (QAN) provides real-time per-query metrics for MySQL, MongoDB, and PostgreSQL
Built-in advisors for configuration, performance, security, and queries (index checks, vacuum checks, CVE checks)
Dedicated dashboards for MySQL, MongoDB, PostgreSQL, ProxySQL, Valkey/Redis, and HA clusters
Flexible deployment: Docker, Podman, VM, AWS Marketplace, Kubernetes
Works completely offline with no internet dependency, suitable for air-gapped environments

Cons

Self-hosted only: you operate the server, storage, upgrades, and high availability
No managed SaaS option at all
Engine coverage stops at MySQL, PostgreSQL, MongoDB, and Redis/Valkey; no SQL Server, Oracle, Cassandra, ClickHouse, or Elasticsearch
No cross-stack APM correlation

Verdict

PMM is the strongest open-source answer for MySQL, PostgreSQL, and MongoDB query analytics, and its advisors encode real operational knowledge. QAN holds its own against commercial query analyzers. The costs are operational rather than licensed: someone owns the PMM server, its storage, and its upgrades, and open source is a distribution model, not a price of zero. If your estate is exactly those engines and you have the operators, it is excellent value.

Visit Percona PMM -> PMM docs ->

Vendor 05 / 10 · #dynatrace

Dynatrace

AI-powered observability platform with a dedicated Databases app for health scoring, statement performance, and execution plans.

Best for

Enterprises already using Dynatrace who want database context in the same platform
Teams that want Davis AI connecting database performance to application behavior
Organizations running hybrid on-prem and cloud database estates

Pricing

Per-host pricing based on host memory (full-stack tiered per 8 GiB of host memory)
Database monitoring requires the higher platform tiers
The bill grows with host count and memory allocation, which can be unpredictable

Pros

Databases app gives unified visibility across the estate with health scores
Statement performance analysis filters queries by time, CPU, disk, or wait metrics and can request execution plans
Davis AI detects anomalies, pinpoints root causes, and recommends remediation
Integrates with the Grail data lakehouse and Smartscape topology mapping
Supports on-premises, cloud, and hybrid database deployments

Cons

Database instance metrics are collected every 1 minute, coarser than per-second tools and blind to short spikes
Full database monitoring requires the most expensive platform tier
Memory-based per-host pricing can surprise as instances scale up
Platform complexity is overkill if database monitoring is the primary need

Verdict

Dynatrace brings genuine AI-assisted root cause analysis and estate-wide health scoring, and for existing customers the Databases app is a natural extension. Two things hold it back for buyers focused on databases: the 1-minute collection interval structurally misses transient spikes, and the capability sits behind the top pricing tier. If you are already on Dynatrace full-stack, use it. If databases are the reason you are shopping, the cost-to-depth ratio is hard to justify.

Read full Netdata vs Dynatrace -> Dynatrace pricing ->

Vendor 06 / 10 · #pganalyze

pganalyze

PostgreSQL-specialist monitoring with automatic EXPLAIN plan collection, index and VACUUM advisors.

Best for

Postgres teams that want the deepest query-level analysis available
DBAs tuning slow queries with EXPLAIN plans, index recommendations, and VACUUM analysis
Teams on RDS, Aurora, Cloud SQL, Azure Database for PostgreSQL, Heroku, or self-managed Postgres

Pricing

Per-server subscription; the bill grows with the number of Postgres servers
Self-hosted option available for on-premise deployment
No free tier for production use

Pros

Automatic EXPLAIN plan collection via auto_explain, with plan comparison over time
Index Advisor tests hundreds of index combinations with What-If analysis
VACUUM Advisor provides per-table autovacuum recommendations
Log Insights extracts structured data from Postgres logs with 100+ PII filters
Runs on-premise in a Docker container behind your firewall

Cons

PostgreSQL only; no MySQL, MongoDB, Redis, or any other engine
Per-server pricing accumulates across large fleets
No broader infrastructure or APM context around the database

Verdict

For PostgreSQL query tuning, pganalyze is the deepest tool on this page, full stop. The Index Advisor and plan comparison workflow answer questions nothing else here attempts. Its rank reflects scope, not quality: it monitors one engine out of ten and knows nothing about the host it runs on. Most serious Postgres shops pair it with a general monitor. If Postgres is your estate, it belongs on the shortlist; if not, it is not for you.

Visit pganalyze -> pganalyze pricing ->

Vendor 07 / 10 · #redgate-monitor

Redgate Monitor

Multi-platform database monitoring for SQL Server, PostgreSQL, Oracle, MySQL, and MongoDB with Monitor AI.

Best for

SQL Server-centric teams that also run PostgreSQL, Oracle, MySQL, or MongoDB
DBAs who want estate-wide visibility from a single pane of glass
Organizations that want deployment tracking correlated with database performance

Pricing

Per-server per-year subscription, with volume discounts for larger estates
The bill grows with the number of monitored servers
Self-hosted and SaaS editions; 14-day trial

Pros

Monitors SQL Server, PostgreSQL, Oracle, MySQL, and MongoDB from one dashboard
Monitor AI surfaces performance bottlenecks and assists query tuning
Real-time alerting via Slack, email, PagerDuty, and webhooks
Deployment tracking correlates code releases with database performance impact
Self-hosted or fully managed SaaS, across on-prem, cloud, or hybrid estates

Cons

Per-server per-year pricing is expensive for small teams and MSPs
No free edition
Broader observability (logs, traces, infrastructure) is limited
NoSQL coverage is limited to MongoDB

Verdict

Redgate Monitor is the natural pick for SQL Server-first estates that have picked up Postgres or Oracle along the way. The deployment tracking feature is genuinely differentiated: correlating a release with a performance regression is a question most tools cannot answer. Its gaps are the engines it does not cover (Redis, Cassandra, ClickHouse, Elasticsearch) and a per-server annual bill that punishes small teams. A DBA’s tool, in the good sense.

Visit Redgate Monitor -> Redgate pricing ->

Vendor 08 / 10 · #new-relic

New Relic

Usage-based observability platform that correlates database performance with application transactions.

Best for

Application teams that want to trace slow queries back to the exact line of application code
Organizations already using New Relic APM who want database context in the same UI
Teams that want a free tier to start evaluating

Pricing

Usage-based: per-GB data ingest plus per-seat pricing for full-platform users
Free tier with limited ingest; the bill grows with data volume and user count
SaaS-only

Pros

Database UI tracks query performance over time to pinpoint when problems occurred
Transaction tracing shows the full path from user request through database queries
DBmarlin integration adds database time, top SQL statements, and top wait events with pre-built dashboards
Supports MySQL, PostgreSQL, MongoDB, Redis, and Elasticsearch
Free tier lowers the barrier to evaluation

Cons

Native database monitoring depth is thin; the deep data comes via the separate DBmarlin integration
Usage-based ingest pricing can grow unpredictably with data volume
Multiple end-of-life announcements for legacy features, per AIMultiple’s coverage
SaaS-only, with no self-hosted path

Verdict

New Relic’s angle is attribution: it is very good at showing that this endpoint is slow because of that query. For application teams that already live in its APM, that is valuable. As a database monitor in its own right, it is thinner than the dedicated tools, and the depth most DBAs want arrives through a third-party integration, which is an extra dependency and an extra decision. Evaluate it as APM with database context, not as a DBA tool.

Read full Netdata vs New Relic -> New Relic pricing ->

Vendor 09 / 10 · #site24x7

Site24x7

SaaS infrastructure monitoring with multi-database coverage and wait-time analysis for query troubleshooting.

Best for

SMB teams that want affordable multi-database monitoring without enterprise per-host pricing
Teams monitoring a mix of relational, NoSQL, and in-memory databases
Organizations that want cross-stack visibility from frontend to database

Pricing

Per-monitor plans; database monitoring is included in infrastructure and application plans
The bill grows with the number of monitors
Free tier available with limited monitors

Pros

Covers Oracle, MSSQL, MySQL, PostgreSQL, SAP HANA, MongoDB, Cassandra, and Redis in one console
Wait-time analysis identifies the specific cause of database delays
AI-powered anomaly detection and query execution path drill-down
Cross-stack observability correlates query performance with application behavior
Strong alert integrations with PagerDuty, Slack, and Jira

Cons

Query-level diagnostic depth is limited compared to dedicated database tools
Not suited for very large enterprise fleets
Advanced customization is constrained

Verdict

Site24x7 covers a genuinely wide engine mix, including Cassandra, at a price point small teams can actually sign. The wait-time analysis is a welcome surprise at this tier. It is an SMB tool and honest about it: query diagnostics are shallower than the dedicated analyzers, and very large estates will hit its ceiling. For a small to mid-size mixed estate with no dedicated DBA, it is a sensible, affordable choice.

Read full Netdata vs Site24x7 -> Site24x7 pricing ->

Vendor 10 / 10 · #manageengine

ManageEngine Applications Manager

Agentless application and database monitoring with the broadest engine coverage across relational, NoSQL, and in-memory stores.

Best for

Enterprises that want agentless monitoring across a very wide range of database engines
IT teams that also need application and infrastructure monitoring in one tool
Organizations with on-prem compliance requirements

Pricing

Per-edition licensing based on monitor count; free edition with limited monitors
The bill grows with the number of monitors
On-prem and SaaS editions available

Pros

Broadest engine coverage on this list: Oracle, MSSQL, MySQL, PostgreSQL, MongoDB, Cassandra, Couchbase, Redis, Memcached, DB2, Informix, SAP HANA, and more
Agentless monitoring via standard database APIs and protocols
Database Query Monitor for MySQL, Oracle, DB2, MSSQL, Sybase, and PostgreSQL
Integrates with ITSM tooling including ServiceNow, Jira, and PagerDuty
On-prem deployment satisfies compliance requirements

Cons

Query diagnostics are shallower than dedicated database tuning tools
On-prem architecture requires internal infrastructure to run
The interface feels dated next to newer platforms
Not a natural fit for cloud-native or containerized workloads

Verdict

Applications Manager wins on raw coverage: if your estate includes DB2, Informix, or Sybase alongside modern engines, it may be the only single-console option here. Agentless collection and an on-prem edition suit compliance-bound IT organizations. The trade for breadth is depth; query analysis is competent but not diagnostic-grade, and the platform’s heritage shows. A pragmatic choice for heterogeneous legacy estates, less so for cloud-native teams.

Read full Netdata vs ManageEngine -> ManageEngine pricing ->

Frequently Asked Questions

What is database monitoring and what metrics should I track?

Database monitoring tracks the health and performance of the database server and the query layer on top of it. The core metrics are query performance (slow queries, execution time, throughput), connections and connection pool usage, lock contention, blocking sessions, and deadlocks, replication lag and sync status, resource usage (CPU, memory, disk I/O), and backup job status plus database size growth. Tools differ most on the first item: server-level gauges are common, but per-query visibility is not.

What is the best open-source database monitoring tool?

The strongest options are Netdata, whose open-source agent (GPLv3+) provides per-second collection and ML anomaly detection across 15+ database platforms, and Percona PMM, which offers open-source Query Analytics for MySQL, PostgreSQL, MongoDB, and Redis. DIY stacks built on Prometheus and Grafana with exporters like mysqld_exporter and postgres_exporter work, as does Zabbix agent 2 with out-of-the-box database plugins for MySQL, Oracle, and PostgreSQL. Remember that open source is a distribution model, not a price: self-hosting carries real operating costs in infrastructure and operator time.

How do I monitor slow queries in PostgreSQL or MySQL?

You need query-level analysis: slow query logs, per-query metrics, and execution plans, not just server-level graphs. Netdata provides interactive query analysis covering slow queries, running operations, and deadlocks for both engines. For PostgreSQL specifically, pganalyze goes deepest with automatic EXPLAIN plan collection and index advisors. Datadog offers Query Metrics, Query Samples, and Explain Plans across both engines, and SolarWinds DPA approaches the problem through wait-time analysis, showing what slow queries waited on.

What is the difference between database monitoring and APM?

Database monitoring focuses on the database server and query layer: query performance, connections, locks, and replication. APM traces application requests end-to-end, including the database calls those requests make. Platforms like Datadog, New Relic, and Dynatrace combine both, which lets them attribute a slow query to the application code that issued it. Database-only tools like pganalyze and SolarWinds DPA go deeper on query tuning but lack that application context. The right answer depends on whether your question is “which query” or “which endpoint.”

How much does database monitoring cost?

Pricing shapes vary widely: per-node (Netdata), per-database-host (Datadog), per-instance (SolarWinds DPA), per-server (pganalyze, Redgate), per-monitor (Site24x7, ManageEngine), and per-GB ingest (New Relic). What makes bills grow is host count, instance count, server count, data volume, or monitor count, depending on the model. Open-source self-hosted options include the Netdata agent, Percona PMM, Prometheus plus Grafana, and Zabbix, each with real operating costs. Netdata Cloud Business starts at $4.50/node/month on annual plans with unlimited metrics, logs, and retention included.

Can I monitor cloud databases like RDS, Aurora, Cloud SQL, or Azure SQL?

Yes, though coverage differs by tool. Datadog supports RDS, Aurora, Cloud SQL, Azure SQL, AlloyDB, and MongoDB Atlas. SolarWinds DPA supports Amazon RDS and EC2-hosted databases, and Dynatrace supports cloud-hosted databases including AWS RDS. pganalyze covers RDS, Aurora, Cloud SQL, Azure Database for PostgreSQL, and Heroku. Netdata monitors self-hosted and cloud databases via its agent on the host or through remote collection, which matters when you cannot install anything on the managed instance itself.

What is the best database monitoring tool for SQL Server?

For SQL Server-first estates, Redgate Monitor offers the deepest diagnostics plus Monitor AI, and SolarWinds DPA brings wait-time analysis that includes Azure SQL. Datadog DBM provides query metrics, samples, and explain plans for SQL Server if you already run its platform. ManageEngine offers agentless SQL Server monitoring with a query monitor for compliance-bound environments. Netdata covers SQL Server with a native collector at per-second resolution plus interactive query analysis, alongside the host infrastructure.

How often should database metrics be collected?

As often as your overhead budget allows, because collection interval determines what you can see. Per-second collection (Netdata’s default, configurable) catches spikes that coarser intervals structurally miss. Typical intervals elsewhere run from 15 to 60 seconds for most agents, and Dynatrace collects database instance metrics every 1 minute. Higher frequency does affect CPU overhead and storage cost, which is why Netdata’s interval is tunable. Note that query-level metrics are often sampled less frequently than host metrics even in the same tool.

The 10 best database monitoring tools, ranked by query visibility

How we evaluated database monitoring tools

Netdata

Datadog

SolarWinds Database Performance Analyzer

Percona Monitoring and Management (PMM)

Dynatrace

pganalyze

Redgate Monitor

New Relic

Site24x7

ManageEngine Applications Manager

Frequently Asked Questions

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