The Truth Engine for PostgreSQL
Prove performance fixes before production. Valk detects high-impact anomalies, explains the schema context, and validates recommended changes in isolated simulations. Built for engineers who want evidence — not guesswork.
Simulation isn't one-size-fits-all. Valk allows you to switch between deterministic benchmarking and chaotic stress testing with a single toggle.
Consistent inputs. Valk uses your primary predicates for all runs. Perfect for verifying if an index works under ideal, reproducible conditions.
Randomized variants. Valk samples your weights to generate chaotic, realistic traffic. Ideal for finding edge cases where performance degrades under skewed cardinality.
Valk ties every recommendation to the workload signal that triggered it — and gives you a safe sandbox to validate impact before you ship changes.
Query-level and snapshot-level rules with deduplication, confidence scoring, and traceable evidence.
Lifecycle management: ack, snooze, resolve, verify. One issue → one alert with history.
Isolated, ephemeral Postgres instances to validate before/after metrics with reproducible commands.
Normalized snapshots: tables, columns, indexes, constraints, triggers, routines — so recommendations stay grounded.
Stop guessing. Valk spins up a reality-distortion field to verify your fix before you merge.
Valk extracts schema metadata & creates an isolated testcontainer.
We run the query N times against seeded synthetic data.
Verified performance. We prove the fix works with 99% statistical confidence.
Valk operates with zero access to your row data. We only require read access to PostgreSQL system catalogs to understand your schema structure and workload distribution.
Valk doesn't force a one-size-fits-all definition of "slow." Use the Valk Dashboard to toggle rules, override thresholds, and suppress known noise — all without touching a line of code.
Set custom latency (ms) and call volume thresholds per database. Scale your alerts as your traffic grows.
Classify findings as Critical, Warning, or Info. Focus on high-impact regressions first.
Snooze or ignore specific queries (like background cleanup jobs) to keep your signal-to-noise ratio high.
P99 Regressions
Write Overhead Bloat
Dead Tuple Buildup
Index Mismatch
P99 Regressions
Write Overhead Bloat
Ever. Period. Valk only accesses PostgreSQL system catalogs — schema metadata and query statistics. Your actual table data never leaves your infrastructure.
Walk through your slowest queries with us. We'll show you exactly what Valk detects, how it builds simulation inputs, and the before/after proof you'd get — all in 20 minutes.
Tell us about your PostgreSQL setup and we'll show you what Valk would find.
Request demo