AI Database Index Advisor

How many of the indexes on your busiest table are actually being used? Almost nobody knows, and that uncertainty runs in both directions. Missing indexes make reads crawl. Surplus indexes make every write slower and quietly consume disk, and because nothing visibly breaks, they accumulate for years.

What is AI Database Index Advisor?

It is a free tool that turns index decisions into something you can reason about. You paste the queries that matter and the tables they hit, and it comes back with a recommended index set, the column order for each one, and an explanation of which query each index is there to serve.

That last part is what makes it usable. An index with no stated purpose becomes untouchable, because nobody dares delete something they cannot explain. An index documented as "supports the dashboard query filtering by tenant and status" can be reviewed like any other decision.

Why Use AI Database Index Advisor?

Because indexing is where intuition fails most reliably. The instinct is to index every column in the WHERE clause, which produces a table with nine single column indexes and still no index that fits the actual query. Composite ordering, selectivity and covering columns are the parts that matter, and they are the parts people skip.

There is also the cleanup side, which nobody volunteers for. AI Database Index Advisor will tell you when three indexes overlap, and that is usually the first honest look a table has had in years.

Who Should Use It?

• Developers whose application has grown past the point where every query is fast by default.
• Teams without a database administrator who need a defensible index strategy.
• Analysts whose reporting queries have started timing out.
• Anyone inheriting a schema with a suspicious number of indexes on one table.
• Engineers preparing a performance review where the index set needs justifying.

How Does AI Database Index Advisor Work?

You supply the evidence, it supplies the analysis. The evidence is your table definitions, your current indexes and the queries that actually run. The analysis is a proposed index set with reasons attached.

1. Open AI Database Index Advisor. Free, no account, nothing to install.
2. Paste the table definitions including every index that already exists.
3. Paste the three or four queries that run most often against those tables.
4. Add rough row counts and note which tables take heavy writes.
5. Choose a model. Google Gemini, Anthropic Claude AI, DeepSeek, Meta AI and others are listed.
6. Set Database to your engine and Output Type to Index Plan, then push Detail Level up.
7. Generate, then read the reasoning before you read the statements.

Best Use Cases

When the plan says the query itself is the problem rather than the index set, AI Query Optimizer is where that gets fixed.

Advanced Options Guide

Ten controls sit in the accordion. For index work, Output Type and Detail Level do the most, and Custom Instructions carries the operational limits.

Example Inputs

A useful prompt reads like an incident note rather than a request. Here is the shape.

PostgreSQL. Table orders (id, tenant_id, customer_id, status, placed_at,
total_cents), about 12 million rows, roughly 300 inserts per minute.
Existing indexes: primary key on id, index on customer_id, index on status,
index on placed_at.

Queries that matter:
1. SELECT * FROM orders WHERE tenant_id = ? AND status = 'open'
   ORDER BY placed_at DESC LIMIT 50;
2. SELECT count(*) FROM orders WHERE tenant_id = ? AND placed_at >= ?;
3. SELECT * FROM orders WHERE customer_id = ? ORDER BY placed_at DESC;

Three details are doing the work. The insert rate rules out an index for every query. The tenant filter appears in two of the three, which changes the leading column. And the ORDER BY on the same column as the range filter is the classic case where index order decides whether a sort happens at all.

Example Outputs

With Output Type on Index Plan and Detail Level high, the answer separates what to add from what to remove.

-- Serves query 1: tenant filter, status filter, ordered scan
CREATE INDEX orders_tenant_status_placed_idx
  ON orders (tenant_id, status, placed_at DESC);

-- Serves query 2: same leading column, range on the second
CREATE INDEX orders_tenant_placed_idx
  ON orders (tenant_id, placed_at);

-- Serves query 3
CREATE INDEX orders_customer_placed_idx
  ON orders (customer_id, placed_at DESC);

-- Redundant once the above exist:
--   orders_customer_id_idx  (leading column covered by orders_customer_placed_idx)
--   orders_status_idx       (low selectivity on its own, covered by the composite)
DROP INDEX orders_customer_id_idx;
DROP INDEX orders_status_idx;

The net effect is four indexes instead of four, but different ones, and each with a query it exists to serve. The DESC on placed_at is not decoration: it lets the engine walk the index in the order the query asks for instead of sorting afterwards.

Tips & Common Mistakes

• ✅ Paste the real queries with their real filter shapes, not tidied versions.
• ✅ Include every index that already exists on the table.
• ✅ State the write volume, since it is half of the trade off.
• ✅ Add indexes concurrently where your engine supports it.
• ✅ Verify with usage statistics before dropping anything.
• ✅ Measure the query again after the change rather than assuming.

The mistakes repeat across teams. Indexing each column in the WHERE clause separately, which serves none of the queries well. Ignoring the ORDER BY, which is where a sort quietly eats the gain. Adding an index on a boolean or status column with two values and expecting it to help. And treating index creation as free on a large live table, when it can hold locks or consume the disk you were short of.

Comparison Table

AIToolsay puts a dedicated page in front of each job so you are not describing your whole situation to a general assistant every time. On this one the prompt box expects queries and schema, the options carry the dialect and complexity settings that index advice depends on, and the model selector lets a second engine review the same plan when a recommendation looks surprising. Nothing costs anything and no account stands in the way, so putting four tables through it in one sitting is realistic. Session history keeps each analysis under the result while you compare them. The rest of the tools on AIToolsay are arranged the same way, so the migration that applies these indexes and the query rewrite that might remove the need for one are both a page away.

Frequently Asked Questions

Start with the table that appears most often in your slow query log. Paste it with its real queries and its real write volume, and read the reasoning before the statements. If the analysis surprises you, that is the useful part. The Telegram community is a good place to sanity check an index set, and the newsletter or push notifications will let you know when more database tools appear.

Let AI Speak.