Stop database problems before they reach production.

DBGorilla watches every query, catches performance regressions and cost leaks, then tests the fix on an isolated clone, so your team sees proof before anything changes.

Get started Book a demo

No card. Connects out, so your credentials stay in your network.

3.8s → 40ms

A single index DBGorilla suggested and tested on a clone can make all the difference.

30%

Execution cost cut on one customer's worst query, inside 24 hours

340%

Latency rise caught six days before anyone was paged

Free

Start free, no card. $105/mo when you scale: whole team, no per-seat fees.

Three commands

$brew install dbgorilla/tap/dbgorilla

$dbgorilla login

$dbgorilla collector install

What early customers are telling us

"I just ran a benchmarked query with the indexes applied and 🤯 157x faster."

CTO, no-code application platform

"Using the config feedback from DBGorilla sped up one of our workflows by 30%, from 12 hours down to 8."

Senior Consultant, real estate data platform

"In the first few hours, DBGorilla delivered the visibility I needed, a breakthrough I had not been able to achieve before."

CTO, large financial company

"DBGorilla helped us immediately gain visibility into our PL/pgSQL functions for the first time, making it easier to identify issues and understand our databases."

Founder, security services startup

The same six days

One query gets slower. DBGorilla catches, optimizes and reports back before it pages someone.

Without DBGorilla

Found by a page

day 0 · plan flipssilenceday 6 · paged

A query gets slower over weeks and nobody notices until it pages someone.
The investigation starts at a dashboard and ends at a guess.
The fix is tested in production, because there is nowhere else to test it.
What you learned lives in one engineer's head.

production is the test environment

With DBGorilla

Found by the agent

day 0 · caughtthe six days never happen

The regression is found the night it starts, with the cause attached.
The investigation arrives finished: symptom, cause, evidence.
The fix is benchmarked before it reaches you.
What it learns about your database stays with the database.

Clone lane · the fix is measured here first

~~3.8s~~→40msone index · verified on a production-shaped clone

Everything it does. Pick the part you were sent here for.

Query optimization

Find the query costing you money

Plans read, root cause identified, and the exact index or rewrite recommended, with the before and after measured on a clone.

See it on /solutions →

query · plan

validated on a clone

SELECT o.id, c.name FROM orders o
  JOIN customers c ON c.id = o.customer_id
WHERE o.created_at > now() - interval '7 days';

- Seq Scan on orders  (rows=4,100,000)
+ Index Scan using idx_orders_created_at

Query time: ~~3.8s~~ → 40ms
Rows scanned: ~~4.1M~~ → 2,140
Lock contention: none

Regression detection

Catch it the night it starts

Continuous watch over queries, schema and configuration, so a plan that flipped three deploys ago is explained before it pages anyone.

See it on /solutions →

overnight · 02:14

delivered to Slack

Regression detected on the orders table

Symptom     Query latency up 340%, rising for 6 days
Cause       Missing index on (tenant_id, created_at)
            after last week's migration
Related     Autovacuum behind · dead tuples at 22%
Next        Composite index, benchmarked and ready

Found at: 02:14
You read it: 08:30

Proof before production

Nothing reaches you unproven

Every fix, index and migration is validated on a production-shaped clone before it is recommended, so you review measured evidence instead of a plausible answer. Nothing to clean up afterwards.

See it on /solutions →

validation · clone

before it was recommended

Proposed  composite index on (tenant_id, created_at)

  ✓ applied to a production-shaped clone
  ✓ measured against the real query
  ✓ row counts match production
  ✓ no lock held longer than 50ms

Verdict   safe to apply

Query time: ~~3.8s~~ → 40ms
Evidence: measured, not predicted

Schema & health

See the whole estate

Schema diagrams, health indicators and history you can question, across every connected instance.

See it on /solutions →

schema · orders

3 instances connected

Dead tuples: 22%
Autovacuum: behind
Indexes: 1 missing

AI-DBA chat

Ask it anything about your database

It knows your schemas, your workloads and your past incidents, so the answer is about your database and not about Postgres in general.

See it on /solutions →

ai-dba · chat

knows your schema

Why did the orders page get slow this week?

Last week's migration dropped the composite index on (tenant_id, created_at), so that query went to a sequential scan over 4.1M rows. I have the replacement benchmarked on a clone.

Context: your database
Not: Postgres in general

In your IDE

It shows up where you already work

MCP in any IDE or AI app, and one command wires up every client it detects. Ask about a query where you are writing it, not in another tab.

See it on /solutions →

terminal

one command

$ dbgorilla setup-ide

  ✓ Claude Code
  ✓ Cursor
  ✓ VS Code

Ready  ask about a query where you write it

Protocol: MCP
Clients: any that speaks it

In your channels

And where your team already talks

Findings arrive in Slack or Teams, incidents reach your on-call through a webhook into whatever you already page with, and the API and CLI are there when you would rather script it than click it.

See it on /solutions →

#db-alerts

02:14

Regression detected on the orders table. Latency up 340% over six days. Cause: missing index on (tenant_id, created_at). Fix benchmarked and ready.

Chat: Slack · Teams
On-call: webhook
Scripted: API · CLI

Query Optimization

We find the thing costing you money

DBGorilla analyzes your query plans, identifies the root cause of slowdowns, and recommends the exact index or rewrite needed, with no manual digging required

measured~~3.8s~~→40ms

query · plan

validated on a clone

SELECT o.id, c.name FROM orders o
  JOIN customers c ON c.id = o.customer_id
WHERE o.created_at > now() - interval '7 days';

- Seq Scan on orders  (rows=4,100,000)
+ Index Scan using idx_orders_created_at

Query time: ~~3.8s~~ → 40ms
Rows scanned: ~~4.1M~~ → 2,140
Lock contention: none

Analyzer

We tell you what happened while you slept

Historical trend tracking: Find out what happened last night, last week or last month with one question

340% latency rise · caught six days early

overnight · 02:14

delivered to Slack

Regression detected on the orders table

Symptom     Query latency up 340%, rising for 6 days
Cause       Missing index on (tenant_id, created_at)
            after last week's migration
Related     Autovacuum behind · dead tuples at 22%
Next        Composite index, benchmarked and ready

Found at: 02:14
You read it: 08:30

Proof before production

The agent proves it before you ever see it

Every recommendation is run on a production-shaped clone first, and the before and after are measured. So what reaches you is evidence, not a confident guess, which is the failure mode of every other AI that writes SQL.

validation · clone

before it was recommended

Proposed  composite index on (tenant_id, created_at)

  ✓ applied to a production-shaped clone
  ✓ measured against the real query
  ✓ row counts match production
  ✓ no lock held longer than 50ms

Verdict   safe to apply

Query time: ~~3.8s~~ → 40ms
Evidence: measured, not predicted

Runs where your database already runs. Including nowhere near us.

The SaaS is a collector install

There is no agent to embed and no database to hand over. You run `dbgorilla collector install`, it connects out to DBGorilla, and your credentials never leave your network.

Or run the whole thing yourself

Fully self-managed, in your own VPC or air-gapped, where data never leaves your network.

It fits the governance you already have

SSO, RBAC and ticketing integration, so access follows the rules you already wrote.

Any Postgres speaking the native protocol. These are examples, not an allowlist

Amazon RDS
Amazon Aurora
Neon
Instaclustr
Google Cloud SQL
Azure Database for PostgreSQL
Supabase
Self-managed
Bare metal
EKS · GKE · AKS
Rancher
Private cloud

Deploy anywhere

DBGorilla can run fully-hosted in our cloud, in your own VPC, or fully self-managed in air-gapped environments where data never leaves your network, so our Agentic Database Operator adapts to your strictest security and compliance needs

Fits your IT governance

Seamlessly integrate via SSO, RBAC, and ticketing systems for IT governance

And the rest of it

Three commands, and it starts watching tonight

$105/mo for three clusters and the whole team. No per-seat fees.

Get started Book a demo