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Elevarq Analyzer

Elevarq Analyzer is a senior PostgreSQL assessment on demand. It investigates your database and explains what's wrong, why it matters, and how to fix it — in language any engineer can act on. You don't need to know which questions to ask.

Not a dashboard, and not a chat prompt you have to drive: an automated, system-led diagnosis that improves every database decision your team makes.

It is built for the whole team that owns the database — developers, DevOps/platform/SRE, and engineering leaders. Every recommendation lands as a ticket in GitHub, GitLab, Jira, or Linear and flows through your normal review and CI/CD controls; nothing is executed on your production database for you.

Elevarq Analyzer analyzes scheduled snapshots from your database, building context across collection runs. Early on, it finds the obvious wins. Over weeks and months, it catches slow regressions, identifies configuration drift, and guides capacity decisions.

See how Elevarq Analyzer works, stage by stage →

What it solves

PostgreSQL problems rarely begin as incidents

It starts small and invisible: queries that were fast slow down, a routine change quietly makes things worse, and the database drifts out of shape — long before anything pages someone. Under the hood that’s plan drift, stale statistics, replication lag, configuration that no longer fits the workload, WAL and I/O pressure, and sprawling privileges — mostly never examined, because there was never engineering time. Elevarq analyzes every snapshot against every applicable detection rule, surfaces the developing conditions the evidence supports, and hands your team an implementation-ready fix before they become incidents.

The compounding effect shows up on the bill. A database with real performance headroom can be right-sized instead of over-provisioned, and workloads can be consolidated — several applications or databases on one replicated instance — because now you have the headroom and the confidence to do it.

Built to be right about your database

Advice on a production database is only worth acting on if it is accurate. That is what Elevarq is built for.

  • Purpose-trained, not general-purpose

    Our analysis runs on models trained specifically for PostgreSQL work. They run locally on your own infrastructure, so your data never leaves it, and every result is validated against the cited evidence before you see it. A bring-your-own general-purpose model is built for conversation, not for correctness on your database.

  • Version-correct by design

    Recommendations match the PostgreSQL version you actually run. You will not get syntax, settings, or features that belong to a different release, the version mixup a generic model produces when it averages over everything it has seen.

  • Grounded and verifiable

    Every finding is tied to an explicit rule set and checked against cited evidence, so you can trust what it says and confirm it yourself. Generic model output gives you prose with no evidence trail.

  • Recommendations, not automation

    Elevarq analyzes and recommends. It cannot modify your database and never runs anything against it. Each finding arrives as an implementation-ready recommendation that feeds into your own change process; whether and when to apply it is entirely your team's decision.

  • The real cost is wrong advice, not the tool

    On a production database, the expensive mistakes come from acting on wrong advice, not from the price of the analysis, which is modest. A free or general-purpose tool that gets it wrong pushes that cost onto your team; purpose-trained, version-correct results are built to keep you out of that trouble.

Components

Each part is independently understandable. Together they form a single flow: collect evidence → diagnose → review → create ticket.

Elevarq Signals

Read-only PostgreSQL telemetry collector. Gathers execution statistics, wait events, connection metrics, and configuration state into portable snapshots. Open source; runs in your infrastructure with no schema changes and no data exfiltration.

Elevarq Analyzer

Evidence-grounded PostgreSQL diagnosis engine. Applies deterministic detection rules — vacuum lag, index bloat, query regressions, WAL retention, and more — to Signals snapshots, then enriches each finding with Elevarq Insight: explanations from local models trained specifically for PostgreSQL, and for TimescaleDB on Professional and above — validated against the cited evidence before they reach you.

Elevarq Workbench

Self-hosted set-up and control surface — connect your databases and trackers, tune what gets raised, and dig into the full evidence when you want to. Day to day, findings arrive as ready-to-ship tickets in the tracker your team already uses. One instance per organization.

What Elevarq Analyzer produces

One rendered example. Every finding has the same shape — a deterministic diagnosis, its severity and confidence, the cited evidence, and a recommended action — extended in the review workflow into implementation steps, the risk, how to validate, how to roll back, and when to escalate. A senior DBA's read of the database, written so any engineer can act on it.

Illustrative scenario

Before: a change no one can safely make yet

An internal feature that reads per-user rows from public.events has been getting slower as the table grows. The engineer who owns it — on a team with no dedicated DBA — can see the slowdown, but not the cause. The dashboards show the symptom, not whether adding an index to a busy production table is safe, which column it should cover, or whether the build would lock writes. Changing a hot table on a hunch is not a decision they can make with confidence.

What happens next

  1. 1. The team notices the change and wants to act — safely.
  2. 2. Elevarq Signals captures the relevant read-only evidence from the instance; nothing is modified, nothing leaves the infrastructure.
  3. 3. The Analyzer correlates it into a deterministic finding (indexes.missing.v1) with a calibrated severity and confidence.
  4. 4. Elevarq Insight explains only what the evidence supports — it cannot invent findings or raise severity, and anything beyond the evidence is discarded.
  5. 5. A human reviews the recommendation. Elevarq pitches the change and shows the evidence; the person decides.
  6. 6. The implementation-ready work enters the tracker the team already uses.
Sample finding · rule indexes.missing.v1

Missing index candidate on public.events.user_id

Severity: mediumConfidence: 0.85

Problem

Frequent sequential scans on public.events filter on user_id. The evidence indicates that this access pattern is worth an operator's review.

Why it matters

This query is on the hot read path and runs frequently. Current execution wastes shared-buffer cache on rows that the predicate then discards. The gap widens as public.events grows; addressing it now is cheap and reversible.

Evidence

  • queryid = 0xab12cd34
  • calls = 1,247,392 over 24 hours
  • mean_exec_time_ms = 11.7
  • Source refs: indexes.missing.v1, table=events, column=user_id

Recommended action

Create a single-column index on user_id. Use CONCURRENTLY so the build does not block writes onpublic.events; note that CREATE INDEX CONCURRENTLY cannot run inside a transaction (a common gotcha for migration tools that wrap each statement by default).

CREATE INDEX CONCURRENTLY idx_events_user_id
  ON public.events (user_id);
The names, query identifier, and numerical values above are illustrative. The fields shown — diagnosis, why it matters, cited evidence, recommended action, plus the calibrated severity and confidence in the header — are what Elevarq Analyzer emits for every finding. The richer triage shape that downstream operators expect in their tracker (implementation steps, rollback, when to escalate) is part of the review workflow before push. Talk to us about an evaluation to see findings like this against your own database.

After: a decision they can defend

Now the reviewer has what the moment lacked: a cited reason (the query's call volume and mean execution time), a safe way to act (build the index CONCURRENTLY, outside a transaction), and a concrete choice — approve, reject, defer, or escalate — shipped in their existing tracker. Nothing was ever run against the database for them. And where there is a DBA, this is their work moving up a level: less manual scan-hunting, more reviewing and governing what ships.

This is one finding. See everything Elevarq detects — vacuum, indexing, configuration, replication, security, and TimescaleDB →

Evaluate Analyzer on your terms

See exactly what Analyzer produces before you talk to anyone. Every path is read-only and self-hosted — Elevarq never touches your production database and sends no telemetry.

Start here

See a sample finding

Inspect a full Analyzer finding — the evidence it gathered, the reasoning, the confidence, and the exact recommended change your team reviews and ships.

Run Signals locally

Download the read-only Signals collector and point it at a PostgreSQL you can reach. It captures a diagnostic snapshot — nothing leaves your environment. Prerequisites and the exact next step are in the release notes.

Download Signals →

Book a technical review

Evaluating Analyzer for a team? Walk through your PostgreSQL estate and a real finding with us — a working technical conversation, not a sales pitch.

Book a technical review →

For DBAs — higher leverage, not replacement

Elevarq changes a DBA's work; it does not remove the DBA.

What makes the findings hold up to a DBA’s scrutiny: Analyzer grounds them in how PostgreSQL would actually plan the query — worked out in isolation, away from your production system — not in generic best practices. Evidence, not rules of thumb.

Elevarq Analyzer takes the repetitive tactical analysis off a DBA’s plate — the query-plan reading, index tuning, and vacuum forensics that fill the day and the 3am pages — and returns it as evidence-grounded findings the whole team can act on.

That frees the DBA to do the higher-leverage work: fleet strategy, governance, capacity planning, and incident-response coordination across the estate. The findings stay precise and cite their evidence, so an experienced DBA can verify each one at a glance and decides what ships — the review and escalation calls remain theirs.

Key capabilities

  • Root-cause grouping: correlated symptoms surfaced as a single finding, not separate alerts
  • Regression detection across snapshots — correlated with the schema and configuration changes captured between runs, by snapshot timing rather than deploy-system integration
  • Execution-based analysis using actual row counts and statistics
  • Targeted recommendations tied to cited evidence, not generic best practices
  • Operator-ready findings that support controlled remediation review
  • On-premise deployment: your data stays in your network

Understands TimescaleDB, not just core PostgreSQL

Extension-aware analysis for time-series workloads.

Most PostgreSQL tools stop at the core engine. Analyzer recognises TimescaleDB hypertables and reasons about the operational choices that are specific to time-series data:

  • Compression — what to compress, and when it pays off
  • Retention and data-tiering policies
  • Background jobs and the scheduler
  • Chunk layout and skew across the hypertable

TimescaleDB analysis is available on Professional and above. See capabilities by plan.

Capabilities by plan

Every plan brings deep PostgreSQL expertise, with analysis in English by default. Professional and above add TimescaleDB — the time-series extension — and let you set the report language to one of six (English plus five more), one at a time.

Elevarq Analyzer capabilities by plan: Starter, Professional, Business, and Enterprise.
CapabilityStarterProfessionalBusinessEnterprise
Database analysis
PostgreSQL analysisIncluded on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
TimescaleDB analysisNot included on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Language
English analysisIncluded on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Multi-language analysisNot included on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Deployment
CPU deploymentIncluded on StarterNot included on ProfessionalNot included on BusinessNot included on Enterprise
GPU deploymentNot included on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Local deploymentIncluded on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Air-gapped deploymentIncluded on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Integrations
Ticket integrationsNot included on StarterIncluded on ProfessionalIncluded on BusinessIncluded on Enterprise
Reporting
Automated report exportNot included on StarterNot included on ProfessionalIncluded on BusinessIncluded on Enterprise
  • TimescaleDB analysis. TimescaleDB is the time-series extension for PostgreSQL — the same database, extended. Available from Professional up.
  • Multi-language analysis. Starter analyzes in English; Professional and above can set the report language to French, German, Italian, Portuguese, or Spanish — one language per deployment, six to choose from.
  • Ticket integrations. Push every finding straight into GitHub, GitLab, Jira, and Linear — all four are available on every paid tier. Configure one integration on Professional, up to 10 on Business, or unlimited on Enterprise.
  • Automated report export. Set it up once and every completed analysis is written to your own storage automatically — the full report as machine-readable JSON, ready to feed your dashboards, ticketing, or archival. No manual step per run. Available on Business and Enterprise.

Starter runs the analysis on CPU; Professional and above run on GPU — the same analysis, much faster, so it can run continuously across a fleet rather than one report at a time.

See what each plan covers →See everything it detects →Works with your PostgreSQL →

Deployment

Elevarq Analyzer runs in your infrastructure. The Elevarq Signals collector connects to PostgreSQL using a read-only role and produces a local snapshot; the Analyzer works from that snapshot, not from a live database connection. No agents to install on database hosts, no schema changes, no sidecar processes.

When analysis needs to see how PostgreSQL would plan a query, Elevarq reproduces that plan in an isolated, same-major replay database it reconstructs from the Signals snapshot — using the actual row and page counts captured there. No customer query is executed, and no query-execution load is added to production; the plan is reproduced away from your live system, without the locks or cache disruption that running it on production would cause.

Designed for environments subject to SOC 2, PCI DSS, and financial regulatory requirements. Query text and result data never leave your network.

Works with the tracker you already use

Every finding becomes a ticket in the system your team already runs on. Elevarq integrates with GitHub, GitLab, Jira, and Linear — the fix lands where your team already works.

  • GitHub
  • GitLab
  • Jira
  • Linear

GitHub, GitLab, Jira, and Linear are trademarks of their respective owners. Elevarq is not affiliated with, or endorsed by, them.