Opportunities › AI-driven database optimization and monitoring › Proposed app
Tunewell
Self-hosted agent that finds slow queries and schema bloat in Postgres and MySQL
- Score
- 34
- confidence 61
- Evidence
- 11
- items on the page
- Rivals
- 3
- named in sources
- First version
- 60 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2016
- Established market · AI estimate
ScreensConcept
Desktop app: Database teams analyze schemas, review slow query rewrites with evidence, and manage dashboards—dense, analytical work requiring sustained focus at a keyboard and detailed screen real estate. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Health overview
Last scan 6 min ago7 issues
About this appAI proposal
Tunewell is a self-hosted agent for teams running production Postgres or MySQL. It reads your schema, application code and query logs, then watches for slow queries, bloated tables and unused indexes. It proposes fixes with the evidence behind them, so you do not need a full-time DBA to review every change.
Findings arrive where your team already works: a web view, a command line and Slack. Sensitive columns are redacted before anyone, or any connected AI assistant, sees query results. Nothing leaves your network unless you allow it.
It is built for engineers and data operators who today read query logs by hand and juggle monitoring tools and separate dashboards. Tunewell keeps health, cost and access in one place and suggests changes before users feel the slowdown.
- Tagline
- Keep your database fast without hiring a DBA.
- Voice
- PlainCalmPrecise
- Why this name
- Like tuning an instrument: small, steady adjustments keep it well. Two plain syllables, easy to say in a stand-up.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Learns your schema from code and logs
Maps tables, indexes and the application code that calls them, so advice refers to your real queries and not generic rules.
No member yet
Slow query rewrites with proof
Suggests a rewrite or index, shows the plan before and after on a copy, and leaves approval to you.
No member yet
Bloat and drift watch
Tracks table and index bloat, unused indexes and growth trends, and flags them before they hurt performance.
No member yet
PII redaction on every read
Mask rules for sensitive columns apply to the web view, CLI, Slack and AI assistants, with a log of who asked what.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C5CDeep Teal · primary#F6F4EEPaper · surface#16232AInk · text#E09F3EAmber · accent#5E8C61Moss · data, healthy#B5453ABrick · data, warning
Typography
Aa
Tunewell
PricingAI proposal
The material gives no price for a database optimization product. Competitor pages show internal-tool and BI plans from $15 to $2,500 a month (Appsmith), €9 to €46 (Retool), $15 to $40 (Tableau). Tiers are a starting point to test.
Community
Free
- One database
- Weekly scans
- Web view and CLI
Team
$49 a month
- Up to 5 databases
- Continuous monitoring
- Slack and MCP
- PII redaction rules
Scale
$499 a month
- Unlimited databases
- Audit log export
- SSO
- Setup and tuning support
Would you pay Team · $49 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Partners and integrations. Slack and MCP connections put Tunewell answers in team channels and in AI assistants, where other engineers see it. Open-source connectors could list it in database tool directories.
- Do they become customers?
- Teammates in Slack are engineers who can start a free trial, but whether they have budget authority is untested. Test how many channel viewers install it on their own database.
- First customers
- Engineering leads at 50-300 person software companies running one large production Postgres database with no dedicated DBA.
- Where to reach them
- Hacker News and Postgres forums: Publish write-ups of real slow-query fixes and bloat findings, and offer free scans to commenters.
- Direct outreach to engineering leads: Message leads who post about database costs or slowdowns, and offer a free audit of one database.
- Postgres meetups and Slack groups: Give short talks on bloat and index cleanup, with the Community tier as a follow-up.
- First milestone
- Five paying teams on Team or Scale, each with a measured cut in p95 query time, within 90 days.
Why people already pay for this
- VerifiedHacker News post received 52 points and 35 comments
- Public claimDatabase performance and cost were becoming operational bottlenecks at scale
- Public claimAI-driven optimization can reduce database infrastructure spending dramatically
- Public claimExisting BI and database tools create separate tool sprawl and expense
- Public claim
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Self-hosted agentContainer image with a read-only DB connectorRuns inside the customer's network and reports to the web app; the shared stack cannot reach private databases.
- This app · Query analysisPostgres and MySQL log and plan parsersNeeded to read slow query logs, statistics and explain plans on a copy.
RisksAI inference
- TechnicalSafe plan testing on a copy of production data is hard across hosting setups; wrong advice on a busy database could cause an outage if applied without review.
- AIGenerated rewrites may change query results or miss edge cases. Every suggestion needs measured proof and human approval.
- CommercialCloud providers and monitoring vendors may offer similar features; the evidence is a single discussion thread with 52 points and public claims.
- DataRedaction gaps could expose sensitive data to AI assistants; customers will expect strong guarantees and audit trails.
- PlatformSelf-hosted installs across many database versions and managed services raise support cost for a small team.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.