DeepSQL
Summary
Your monitoring dashboard flags slow queries, your DBA tunes an index, and three weeks later the same query pattern regresses because nothing learned the workload — it just watched it. DeepSQL is built to close that loop: a self-hosted agent that monitors Postgres and Aurora continuously, builds a schema-and-business-logic context it calls the 'brain,' and ships index recommendations and BI queries against that accumulated knowledge.
The core workflow runs on read-only credentials inside your VPC — DeepSQL connects to a replica, ingests pg_stat_statements, clusters thousands of query fingerprints down to a manageable set, and starts recommending. The agent answers natural-language questions about workload patterns, executes validated queries on the read replica, and returns results with estimated plan costs. Index recommendations come with write-amplification analysis, so you see the trade-off before you apply it. The ceiling appears when your optimization problems live outside Postgres and Aurora — MySQL support is listed but the depth of Postgres-specific features is where the tooling is concentrated. Teams running mixed database estates will run a second tool alongside it.
Bottom line: Pick this when your Aurora I/O bill is climbing and you want plan-aware recommendations that already know what 'MRR' means in your schema — skip it when your workload spans multiple database engines and you need a single pane of glass across all of them.
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Pros
Sign in to edit- Self-hosted deployment with read-only replica access and no data leaving your VPC, which means your security team can approve it without a weeks-long vendor review cycle.
- Business-context encoding in plain English before query generation, so the agent's recommendations already understand what your internal metrics mean rather than requiring post-hoc correction every time it drafts an aggregation.
- Index recommendations include write-amplification analysis, which means you see the storage and write-throughput cost before applying an index — not after your Aurora bill arrives.
- pg_stat_statements ingestion clusters raw query fingerprints into a ranked working set, so instead of triaging thousands of slow query variants you work from a deduplicated list of patterns that actually matter.
- MCP server exposes the agent to Claude, Codex, and Cursor, so engineers get plan-aware query suggestions inside the tools they are already working in without a context switch to a separate dashboard.
Cons
Sign in to edit- The tooling is built around Postgres and Aurora internals — MySQL is listed as supported, but the depth of index analysis, plan inspection, and vacuum guidance that the vendor documents publicly is concentrated on Postgres. Teams running MySQL as their primary database will hit gaps in recommendation depth and likely need a supplementary tool.
- The 'brain' context layer requires upfront encoding of business rules and metric definitions. Teams without a DBA or data engineer available to populate and maintain that context will get generic query recommendations — the same output any query analyzer provides — until the context is built out, which takes deliberate effort, not setup time.
- When optimization requirements move beyond query tuning and index selection into structural schema redesign or cross-database federation, the agent does not have a migration planning or multi-database join analysis capability. Teams at that stage typically move to a dedicated schema management tool and keep DeepSQL for ongoing operational tuning — which means running two systems.
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About
- Platforms
- CLI, Slack, Docker, AWS
- API Available
- Yes
- Self-Hosted
- Yes
- Last Updated
- 2026-07-21T08:25:40.110Z
Best For
Who it's for
- Postgres and Aurora teams managing high-write or cost-sensitive workloads
- DBAs and platform engineers seeking automated index and vacuum tuning
- Organizations replacing general monitoring tools with plan-aware analysis
What it does well
- Optimizing slow Postgres queries and reducing Aurora I/O costs
- Recommending and evaluating indexes with full ROI analysis including write amplification
- Auditing materialized views and dashboard queries for hidden billing impact
- Providing schema-level performance guidance beyond query tuning
Integrations
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Frequently Asked Questions
- Is DeepSQL free?
- DeepSQL is a paid tool. No permanent free tier is offered.
- Is DeepSQL open source?
- No — DeepSQL is a closed-source tool. Source code is not publicly available.
- Does DeepSQL have an API?
- Yes. DeepSQL exposes a developer API. See the official documentation at https://deepsql.ai for details.
- Can I self-host DeepSQL?
- Yes. DeepSQL supports self-hosting on your own infrastructure.
- What platforms does DeepSQL support?
- DeepSQL is available on: CLI, Slack, Docker, AWS.
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Curated lists that include this category
Aurora and Postgres performance problems compound quietly: a query that passed review in staging starts dominating I/O at production volume, an index that looked good on write throughput turns out to double your Aurora storage bill, and your DBA learns this three weeks late from a dashboard alert. DeepSQL positions itself as a continuously available DBA agent — self-hosted in your VPC, connected to a read replica with read-only credentials, ingesting slow query logs from pg_stat_statements, and building a persistent context of your schema, workload patterns, and business-defined metrics before it issues a single recommendation.
The differentiating feature the vendor calls the ‘brain’ is a structured context layer where teams encode business rules in plain English — definitions like MRR, active-customer thresholds, and currency conventions. When the agent answers a workload question or drafts a query, it resolves those definitions against the actual schema before generating a plan. The demo trace on the vendor page shows the agent checking that a drafted GROUP BY query matches governed metrics before executing, returning results with a validated index scan and an estimated latency. The vendor states this context is indexed at setup and updated as workload patterns evolve.
DeepSQL fits Postgres and Aurora teams that have outgrown general-purpose monitoring and need recommendations that account for write amplification and I/O cost — not just query latency. The docs describe index ROI analysis and materialized view audits as first-class features. Where it does not fit: teams running heterogeneous database estates will find the tooling weighted heavily toward Postgres internals, with MySQL listed as supported but without the same depth of coverage evident in the vendor’s public writing. Schema-level guidance is available, but the agent does not replace a migration planner for structural redesigns.
Deployment is a single shell command on any Linux box, EC2 instance, or container inside your VPC. The vendor states setup completes in 15 minutes. The MCP server and CLI install alongside the agent, enabling Claude, Codex, and Cursor to connect to your DeepSQL instance for in-editor query assistance. Slack integration allows teams to query the agent directly from a channel. Role-based access control, query policies, and audit logs are described as built-in — relevant for teams that need to demonstrate that automated queries stayed within approved read-only boundaries.