Elevarq Analyzer

PostgreSQL expertise for the whole engineering team. Elevarq Analyzer proactively investigates your database and explains what's wrong, why it matters, and how to fix it — in language any engineer can act on, and credible to experienced DBAs. 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 — while staying valuable and credible to experienced DBAs. 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

Query plans drift, statistics go stale, replication lag creeps up, configuration stops matching the workload, WAL and I/O pressure build, and privileges sprawl — quietly, long before anything pages someone. Most of it is 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.

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

For DBAs — higher leverage, not replacement

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

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 tied to specific deployments, schema changes, or config updates
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 for PostgreSQL — and let you set the report language to one of five more: French, German, Italian, Portuguese, or Spanish (six to choose from, one at a time).

Elevarq Analyzer capabilities by plan: Starter, Professional, Business, and Enterprise.
Capability	Starter	Professional	Business	Enterprise
Database analysis
PostgreSQL analysis	Included on Starter	Included on Professional	Included on Business	Included on Enterprise
TimescaleDB analysis	Not included on Starter	Included on Professional	Included on Business	Included on Enterprise
Language
English analysis	Included on Starter	Included on Professional	Included on Business	Included on Enterprise
Multi-language analysis	Not included on Starter	Included on Professional	Included on Business	Included on Enterprise
Deployment
CPU deployment	Included on Starter	Not included on Professional	Not included on Business	Not included on Enterprise
GPU deployment	Not included on Starter	Included on Professional	Included on Business	Included on Enterprise
Local deployment	Included on Starter	Included on Professional	Included on Business	Included on Enterprise
Air-gapped deployment	Included on Starter	Included on Professional	Included on Business	Included 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.

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.