MyDBA.dev vs pganalyze: Which PostgreSQL Monitor Should You Choose?
pganalyze vs MyDBA.dev -- A Practical PostgreSQL Monitoring Comparison I've been running PostgreSQL in production for years, and if there's one thing I've learned about monitoring tools, it's this: the best time to evaluate them is before you need them. Not during a 3am incident when you're staring at a chart that says "something is wrong" but gives you no idea how to fix it. Both pganalyze and MyDBA.dev are PostgreSQL-focused monitoring tools -- not generic infrastructure platforms that treat Postgres as an afterthought. But they have meaningfully different philosophies about what monitoring should do. Here's a practical comparison. pganalyze: The Established Player pganalyze has been around since 2013 and has built genuine depth in several areas. Their index advisor uses hypothetical ind
pganalyze vs MyDBA.dev -- A Practical PostgreSQL Monitoring Comparison
I've been running PostgreSQL in production for years, and if there's one thing I've learned about monitoring tools, it's this: the best time to evaluate them is before you need them. Not during a 3am incident when you're staring at a chart that says "something is wrong" but gives you no idea how to fix it.
Both pganalyze and MyDBA.dev are PostgreSQL-focused monitoring tools -- not generic infrastructure platforms that treat Postgres as an afterthought. But they have meaningfully different philosophies about what monitoring should do. Here's a practical comparison.
pganalyze: The Established Player
pganalyze has been around since 2013 and has built genuine depth in several areas.
Their index advisor uses hypothetical index simulation ("What If?" analysis) to recommend new indexes and predict their performance impact before you create them. You can see estimated query cost reduction for candidate indexes, which is valuable when you're weighing the write overhead of a new index against query speed improvements.
Their VACUUM advisor provides per-table autovacuum tuning recommendations, including freeze age analysis. If you're struggling with autovacuum configuration, pganalyze will tell you exactly which tables need what settings.
Log insights parse your PostgreSQL logs to surface connection errors, lock timeouts, checkpoint warnings, and other events that pg_stat_statements alone misses. This is well-executed and fills a real gap.
The tool is mature, stable, and well-documented. It does what it does reliably.
MyDBA.dev: The Opinionated Newcomer
MyDBA.dev (launched 2025) takes a different stance: monitoring should tell you what's wrong AND hand you the fix. Not just a red chart -- a diagnosis, an explanation, and a SQL script you can copy-paste.
75+ Health Checks with Fix Scripts
This is the core differentiator. MyDBA.dev runs 75+ automated health checks across 12 domains (indexes, vacuum, security, WAL, replication, storage, connections, configuration, queries, extensions, locks, XID). Each failing check includes:
-
A severity score
-
A plain-English explanation
-
A ready-to-run SQL fix script
pganalyze surfaces around 20-30 check-style findings, but doesn't generate fix scripts. At 3am, that difference matters.
Extension Monitoring (The Biggest Gap)
If you run TimescaleDB, pgvector, or PostGIS, this is the deciding factor. MyDBA.dev provides dedicated monitoring for all three:
-
TimescaleDB: chunk health, compression ratios, continuous aggregate staleness, job monitoring, 15+ extension-specific health checks
-
pgvector: index type analysis (IVFFlat vs HNSW), recall estimation, storage analysis, 13 health checks
-
PostGIS: spatial index coverage, geometry quality, SRID consistency, 19 health checks
pganalyze has no extension monitoring whatsoever. For teams whose workload is dominated by extension behavior (time-series ingestion, vector similarity search, spatial queries), this is a significant blind spot.
XID Wraparound Protection
Transaction ID wraparound can force your database into read-only emergency mode. MyDBA.dev provides a dedicated dashboard showing current XID age, tables approaching danger, and -- critically -- blocker detection with recovery scripts. It identifies the specific long-running transaction, abandoned replication slot, or prepared transaction preventing XID advancement, and gives you the command to fix it.
pganalyze shows basic xmin horizon data. When you're racing against an emergency wraparound vacuum, the difference between "your XID age is high" and "this replication slot is the blocker, here's the DROP command" is the difference between a quick fix and an hour of investigation.
Cluster-Aware Index Advisor
MyDBA.dev aggregates index usage across the entire replication topology. An index that looks unused on primary might be serving all your read-replica analytics queries. Dropping it based on primary-only stats would break things. pganalyze evaluates each server independently.
Pricing
This is where it gets interesting.
Plan pganalyze MyDBA.dev
Free None 1 server + 1 replica (all features)
Entry $149/mo (1 server) $19/mo (1 server)
4 servers $349/mo $76/mo
pganalyze has no free tier. MyDBA.dev's free tier includes every feature -- the paid plan adds more servers and longer data retention.
Which Should You Choose?
Choose pganalyze if you need mature VACUUM advisory with per-table recommendations, hypothetical index analysis, or if you're already invested in it and it covers your needs.
Choose MyDBA.dev if you need health checks with remediation scripts, extension monitoring (TimescaleDB/pgvector/PostGIS), XID protection with blocker detection, cluster-aware index analysis, or a free tier to evaluate with production data.
Both are solid, PostgreSQL-focused tools. The right answer depends on your workload. If you run extensions or need remediation guidance, MyDBA.dev. If you need deep VACUUM and hypothetical index analysis, pganalyze. If budget matters, the pricing difference alone may be decisive.
Full comparison with more detail: mydba.dev/blog/mydba-vs-pganalyze
Originally published at mydba.dev/blog/mydba-vs-pganalyze
DEV Community
https://dev.to/philip_mcclarence_2ef9475/mydbadev-vs-pganalyze-which-postgresql-monitor-should-you-choose-3e81Sign in to highlight and annotate this article

Conversation starters
Daily AI Digest
Get the top 5 AI stories delivered to your inbox every morning.
More about
launchproductplatformDesktop Canary v2.1.48-canary.32
🐤 Canary Build — v2.1.48-canary.32 Automated canary build from canary branch. Commit Information Based on changes since v2.1.48-canary.31 Commit count: 1 14cd81b624 ✨ feat(cli): add migrate openclaw command ( #13566 ) (Arvin Xu) ⚠️ Important Notes This is an automated canary build and is NOT intended for production use. Canary builds are triggered by build / fix / style commits on the canary branch. May contain unstable or incomplete changes . Use at your own risk. It is strongly recommended to back up your data before using a canary build. 📦 Installation Download the appropriate installer for your platform from the assets below. Platform File macOS (Apple Silicon) .dmg (arm64) macOS (Intel) .dmg (x64) Windows .exe Linux .AppImage / .deb

LangChain Just Released Deep Agents — And It Changes How You Build AI Systems
Most people are still hand-crafting agent loops in LangGraph. Deep Agents is a higher-level answer to that — and it’s more opinionated than you’d expect. 1.1 Deep agents in action There’s a pattern I’ve watched repeat itself across almost every team that gets serious about building agents. First, they try LangChain chains. Works fine for simple pipelines. Then the task gets complex — needs tool calls, needs to loop, needs to handle variable-length outputs — and chains stop being enough. So they reach for LangGraph, and suddenly they’re writing state schemas, conditional edges, and graph compilation logic before they’ve even gotten to the actual problem. It’s not that LangGraph is bad. It’s extremely powerful. But it’s a runtime — a low-level primitive — and most people are using it as if i

The Complete Architecture for Trustworthy Autonomous Agents
Four layers. Four questions. Missing any one of them is how production systems fail. Every serious conversation about securing AI agents eventually produces the same result: a list of things you need to do that don’t obviously fit together. Fine-grained authorization. Runtime monitoring. Capability scoping. Behavioral guardrails. Intent tracking. Wire-level enforcement. Each of these sounds right in isolation. None of them, in isolation, is sufficient. The reason production agentic systems fail is rarely that they’re missing everything. It’s that they have one or two layers and are missing the others — often without knowing it. The team that built a careful authorization system discovers their agent can still drift from its declared intent in ways that pass every check. The team that deplo
Knowledge Map
Connected Articles — Knowledge Graph
This article is connected to other articles through shared AI topics and tags.
More in Products
Desktop Canary v2.1.48-canary.32
🐤 Canary Build — v2.1.48-canary.32 Automated canary build from canary branch. Commit Information Based on changes since v2.1.48-canary.31 Commit count: 1 14cd81b624 ✨ feat(cli): add migrate openclaw command ( #13566 ) (Arvin Xu) ⚠️ Important Notes This is an automated canary build and is NOT intended for production use. Canary builds are triggered by build / fix / style commits on the canary branch. May contain unstable or incomplete changes . Use at your own risk. It is strongly recommended to back up your data before using a canary build. 📦 Installation Download the appropriate installer for your platform from the assets below. Platform File macOS (Apple Silicon) .dmg (arm64) macOS (Intel) .dmg (x64) Windows .exe Linux .AppImage / .deb

The Complete Architecture for Trustworthy Autonomous Agents
Four layers. Four questions. Missing any one of them is how production systems fail. Every serious conversation about securing AI agents eventually produces the same result: a list of things you need to do that don’t obviously fit together. Fine-grained authorization. Runtime monitoring. Capability scoping. Behavioral guardrails. Intent tracking. Wire-level enforcement. Each of these sounds right in isolation. None of them, in isolation, is sufficient. The reason production agentic systems fail is rarely that they’re missing everything. It’s that they have one or two layers and are missing the others — often without knowing it. The team that built a careful authorization system discovers their agent can still drift from its declared intent in ways that pass every check. The team that deplo




Discussion
Sign in to join the discussion
No comments yet — be the first to share your thoughts!