Database Observability for AI Workloads: Monitoring pgvector Queries

When a vector similarity search starts degrading at 3 AM, you will not know it is happening unless you built observability before the first embedding landed in production. Most teams add pgvector to an existing PostgreSQL instance, run a few benchmarks that look fine, and ship — without wiring up a single metric specific to how their ANN indexes behave under real query patterns. Three weeks later, a product manager notices that AI-powered search results are slow and inconsistent, and the on-call engineer is staring at a blank Grafana panel wondering what to instrument first. The failure mode is predictable, and the fix is instrumentation, not more hardware.

pgvector workloads have distinctly different failure signatures than standard relational workloads. A slow SELECT against a btree index usually points to a missing index or a bad plan; a slow <=> cosine search might mean your HNSW graph is being built on disk, your ef_search is too high for your SLA, your IVFFlat nprobe is scanning too many lists, or your index has never been built at all and PostgreSQL is doing a sequential scan on 40 million 1536-dimensional vectors. These are fundamentally different problems with fundamentally different solutions, and you cannot tell them apart without the right signals.

This guide covers the exact metrics, queries, and alerting patterns you need to operate pgvector in production: what to instrument in pg_stat_statements, how to interpret index hit rates, how to distinguish HNSW from IVFFlat performance degradation, and how to build dashboards that surface problems before users report them.

TL;DR

Track index hit rate per vector table using pg_stat_user_tables; a sequential scan on a vector table with an HNSW or IVFFlat index almost always means the planner is not using the index — investigate enable_indexscan, query structure, and ef_search settings.
Use pg_stat_statements filtered on '<=>' (cosine) to isolate slow similarity searches; sort by mean_exec_time descending and focus on anything above your latency SLA.
HNSW delivers recall of 95%+ and lower query latency but requires 2–4x the raw vector data size in memory; if the index does not fit in shared_buffers or the OS page cache, you will see extreme p99 variance.
IVFFlat uses significantly less memory and lets you tune recall at query time via SET ivfflat.probes, but builds must happen after representative data is loaded — an index built on an empty or sparse table degrades recall silently.
SET hnsw.ef_search = 100 raises recall at the cost of query latency; tune this per workload path, not globally.
Alert on: sequential scans on vector tables, mean similarity search latency above SLA, index bloat from high-churn embedding columns, and pg_stat_statements call count growth for vector queries.

Key Metrics for pgvector Workloads

Standard PostgreSQL metrics cover buffer hit rates, transaction throughput, and table bloat well. Vector workloads add three categories of signal that generic monitoring misses entirely: ANN index utilization, distance operator query performance, and embedding column storage health.

Index Hit Rate by Table

The most important signal for any pgvector table is whether queries are hitting your HNSW or IVFFlat index or falling back to a sequential scan. A sequential scan on a vector table with millions of rows is catastrophic — it computes exact cosine or L2 distances across every row, which for 1536-dimensional embeddings means floating-point arithmetic at scale with no shortcuts.

sql

-- Index hit rate and sequential scan count per table
-- Run this for any table that holds a vector column
SELECT
    schemaname,
    relname                                         AS table_name,
    seq_scan,
    seq_tup_read,
    idx_scan,
    idx_tup_fetch,
    ROUND(
        idx_scan::numeric / NULLIF(seq_scan + idx_scan, 0) * 100,
        2
    )                                               AS index_hit_pct,
    n_live_tup                                      AS live_rows
FROM pg_stat_user_tables
WHERE relname IN (
    -- Replace with your actual vector table names
    'documents', 'embeddings', 'document_chunks'
)
ORDER BY seq_scan DESC;

For a healthy pgvector table, index_hit_pct should be above 95% during normal operation. A value below 80% warrants immediate investigation. Common root causes: the query is using a WHERE clause filter that forces a sequential scan before the vector operator can use the index; the table is small enough that the planner prefers a seqscan (common during development, dangerous when the table grows); or the index was dropped and never recreated after a schema migration.

Warning

The PostgreSQL query planner may legitimately choose a sequential scan over an HNSW index when the result set is expected to be large relative to total table size, or when the index pages are not in the buffer cache. If you see unexpected sequential scans on a table you know has an HNSW index, run EXPLAIN (ANALYZE, BUFFERS) on the specific query — look for Seq Scan instead of Index Scan using ... hnsw and check whether shared buffer hits are low, which indicates the index is being read from disk rather than cache.

Buffer Cache Efficiency for Vector Indexes

HNSW graph traversal is memory-bound. If the HNSW index pages are not resident in the PostgreSQL buffer cache (or the OS page cache), every query pays disk I/O for random page reads through the graph layers. This shows up as high p99 latency with relatively normal p50.

sql

-- Buffer cache hit rate broken down by index, including vector indexes
SELECT
    c.relname                                       AS table_name,
    i.relname                                       AS index_name,
    pg_size_pretty(pg_relation_size(i.oid))         AS index_size,
    SUM(s.heap_blks_hit)                            AS buffer_hits,
    SUM(s.heap_blks_read)                           AS disk_reads,
    ROUND(
        SUM(s.heap_blks_hit)::numeric /
        NULLIF(SUM(s.heap_blks_hit) + SUM(s.heap_blks_read), 0) * 100,
        2
    )                                               AS table_buffer_hit_pct
FROM pg_stat_user_tables s
JOIN pg_class c ON c.relname = s.relname
JOIN pg_index ix ON ix.indrelid = c.oid
JOIN pg_class i ON i.oid = ix.indexrelid
WHERE c.relname IN ('documents', 'embeddings', 'document_chunks')
GROUP BY c.relname, i.relname, i.oid
ORDER BY pg_relation_size(i.oid) DESC;

If table_buffer_hit_pct is below 90% for a table whose HNSW index is actively queried, your shared_buffers or total memory is insufficient to hold the working set. For a 1M-row table with 1536-dimensional embeddings and m=16, the HNSW index alone is 7–10 GB. If shared_buffers is set to the default 128 MB, the index will never be cached — every query will read from disk.

Embedding Column Storage

sql

-- Size breakdown for tables with vector columns, including TOAST usage
SELECT
    c.relname                                       AS table_name,
    pg_size_pretty(pg_total_relation_size(c.oid))   AS total_size,
    pg_size_pretty(pg_relation_size(c.oid))         AS table_size,
    pg_size_pretty(
        pg_total_relation_size(c.oid)
        - pg_relation_size(c.oid)
    )                                               AS index_plus_toast_size,
    c.relpages,
    c.reltuples::bigint                             AS estimated_rows
FROM pg_class c
JOIN pg_namespace n ON n.oid = c.relnamespace
WHERE n.nspname = 'public'
  AND c.relkind = 'r'
  AND c.relname IN ('documents', 'embeddings', 'document_chunks')
ORDER BY pg_total_relation_size(c.oid) DESC;

Monitoring with pg_stat_statements

pg_stat_statements is the single most valuable tool for identifying slow vector queries in production. It aggregates query execution statistics — call count, total time, mean time, min/max time, and rows returned — across all queries that share the same normalized query text. For pgvector workloads, the operators <=> (cosine), <-> (L2), and <#> (inner product) are your filter targets.

Enable and Verify pg_stat_statements

sql

-- Verify the extension is installed and active
SELECT * FROM pg_available_extensions WHERE name = 'pg_stat_statements';

-- Enable if not already (requires postgresql.conf change and restart)
-- Add to postgresql.conf:
--   shared_preload_libraries = 'pg_stat_statements'
--   pg_stat_statements.track = all
--   pg_stat_statements.max = 10000

-- Create the extension (once per database)
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;

-- Confirm it is capturing data
SELECT count(*) FROM pg_stat_statements;

Isolate Slow Similarity Searches

The key insight: every pgvector cosine similarity query will contain the <=> operator in its normalized text. Filtering pg_stat_statements on this string isolates your entire vector search workload from the rest of your query traffic, regardless of which tables or columns are involved.

sql

-- Find slow vector similarity queries using the cosine distance operator
-- Sorted by mean execution time descending to surface worst offenders first
SELECT
    LEFT(query, 120)                                AS query_preview,
    calls,
    ROUND(mean_exec_time::numeric, 2)               AS mean_ms,
    ROUND(min_exec_time::numeric, 2)                AS min_ms,
    ROUND(max_exec_time::numeric, 2)                AS max_ms,
    ROUND(stddev_exec_time::numeric, 2)             AS stddev_ms,
    ROUND(total_exec_time::numeric / 1000, 2)       AS total_secs,
    rows
FROM pg_stat_statements
WHERE query LIKE '%<=>%'
ORDER BY mean_exec_time DESC
LIMIT 20;

High stddev_ms relative to mean_ms is a red flag: it means query latency is inconsistent, which typically indicates the HNSW index is being partially served from disk (cache pressure) or that ef_search is set inconsistently across sessions. A well-tuned HNSW index with adequate memory should have a stddev no more than 20–30% of the mean.

sql

-- Also capture L2 and inner product operator queries
SELECT
    LEFT(query, 120)                                AS query_preview,
    calls,
    ROUND(mean_exec_time::numeric, 2)               AS mean_ms,
    ROUND(total_exec_time::numeric / 1000, 2)       AS total_secs
FROM pg_stat_statements
WHERE query LIKE '%<->%'
   OR query LIKE '%<#>%'
ORDER BY mean_exec_time DESC
LIMIT 10;

Track Vector Query Volume Over Time

pg_stat_statements accumulates since the last reset. To track query volume trends, snapshot the table periodically into a monitoring schema or push the data to your metrics pipeline:

sql

-- Snapshot vector query stats for trending (run on a schedule, e.g., every 5 minutes)
INSERT INTO monitoring.vector_query_snapshots (
    snapshot_time,
    query_fingerprint,
    calls_delta,
    mean_exec_time,
    total_exec_time_delta
)
SELECT
    NOW()                                           AS snapshot_time,
    MD5(query)                                      AS query_fingerprint,
    calls                                           AS calls_delta,
    ROUND(mean_exec_time::numeric, 2)               AS mean_exec_time,
    ROUND(total_exec_time::numeric, 2)              AS total_exec_time_delta
FROM pg_stat_statements
WHERE query LIKE '%<=>%'
ORDER BY total_exec_time DESC
LIMIT 50;

Tip

Reset pg_stat_statements statistics after any major schema change, index rebuild, or PostgreSQL configuration update that affects vector query planning. Stale statistics from a pre-index-rebuild period will inflate mean execution times and produce misleading trend data. Use SELECT pg_stat_statements_reset(); — this is safe to run on a live system and takes effect immediately.

HNSW vs IVFFlat Index Performance Trade-offs

Choosing the wrong index type for your workload is the most common pgvector production mistake, and it almost never surfaces during initial testing because both indexes perform acceptably on small datasets. The divergence becomes visible at scale, under memory pressure, and when the query recall requirements of the application differ from the benchmark conditions used during evaluation.

Dimension	HNSW	IVFFlat
Recall at default settings	95%+ (ef_search=40)	85–92% (probes=1)
Query latency (10M rows)	8–25ms p99 (memory-resident)	15–40ms p99 (probes=10)
Index build time (1M rows, 1536-dim)	20–90 min (single-threaded)	3–8 min
Index memory footprint	2–4x raw vector size	~1.1x raw vector size
High-churn insert workloads	Handles incremental inserts well	Degrades if data distribution shifts post-build
Recall tuning at query time	`SET hnsw.ef_search = N`	`SET ivfflat.probes = N`
Best fit	Latency-sensitive AI features, RAG pipelines, <50M rows with adequate RAM	Memory-constrained environments, batch embedding jobs, datasets requiring frequent full rebuilds

HNSW: Tuning ef_search for Recall vs Speed

HNSW's ef_search parameter controls the size of the dynamic candidate list during graph traversal. A higher value means more candidates are evaluated before returning the top-K results, which increases recall at the cost of query time. The default is 40. This parameter can be set at the session level without a restart, which makes it the primary operational lever for trading off quality against latency on a per-workload basis.

sql

-- High recall path: RAG context retrieval, document search, semantic deduplication
-- Use when result quality matters more than milliseconds
SET hnsw.ef_search = 100;
SELECT
    id,
    title,
    1 - (embedding <=> $1::vector)  AS cosine_similarity
FROM documents
WHERE user_id = $2
ORDER BY embedding <=> $1::vector
LIMIT 20;

-- High throughput path: autocomplete, real-time recommendation, low-latency feature stores
-- Acceptable for top-1 or top-3 retrievals where a slight recall drop is tolerable
SET hnsw.ef_search = 20;
SELECT
    id,
    title,
    embedding <=> $1::vector        AS cosine_distance
FROM documents
WHERE user_id = $2
ORDER BY embedding <=> $1::vector
LIMIT 5;

-- Check current session setting
SHOW hnsw.ef_search;

A practical calibration exercise: run your typical production query at ef_search values of 20, 40, 80, and 100 against a known ground-truth dataset (exact nearest neighbors computed by brute force), and plot recall rate against p99 latency. The inflection point — where additional recall gain is small but latency increase is large — is your optimal operating point for that query pattern. For most text embedding RAG pipelines, ef_search = 60–100 is the right range.

IVFFlat: Tuning nprobe and Recognizing Build-Time Pitfalls

IVFFlat partitions the vector space into lists clusters at build time. At query time, ivfflat.probes controls how many of those clusters are searched. Higher probes increase recall but increase query time proportionally. Unlike HNSW, IVFFlat recall degrades permanently if the cluster structure built at index creation time does not match the actual data distribution — a problem that manifests silently.

sql

-- Build IVFFlat with enough lists for your dataset size
-- Rule of thumb: lists = sqrt(row_count) up to ~1M rows
--                lists = row_count / 1000 for larger datasets
CREATE INDEX embeddings_ivfflat_idx
    ON embeddings
    USING ivfflat (embedding vector_cosine_ops)
    WITH (lists = 200);   -- appropriate for ~40K-400K rows

-- At query time: tune probes to balance recall vs speed
-- Starting point: probes = sqrt(lists)
SET ivfflat.probes = 15;   -- ~7.5% of lists searched; ~90-92% recall

-- Higher recall:
SET ivfflat.probes = 40;   -- ~20% of lists searched; ~95-97% recall

-- Verify current probe setting
SHOW ivfflat.probes;

Warning

Never build an IVFFlat index on a table with fewer rows than 3x the lists value. The k-means clustering algorithm needs enough representative data to build meaningful cluster centroids. If you create an IVFFlat index with lists=200 on a table with 100 rows, the resulting clusters will be degenerate and recall will be poor regardless of the probes setting — and you will not see an error. The index will exist, queries will use it, and it will return incorrect results. Load representative data first, then build the index.

Checking Whether Your Index Is Actually Being Used

sql

-- Confirm the query planner is using the vector index (not doing a seqscan)
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT)
SELECT id, title, embedding <=> '[0.1,0.2,...]'::vector AS dist
FROM documents
WHERE user_id = '550e8400-e29b-41d4-a716-446655440000'
ORDER BY dist
LIMIT 10;

-- In the output, look for:
--   "Index Scan using documents_embedding_hnsw_idx"  <-- good
--   "Seq Scan on documents"                           <-- bad, investigate

-- If you see a Seq Scan on a table with an HNSW index, try:
SET enable_seqscan = off;
-- Re-run EXPLAIN to see the index plan and its estimated cost
-- If the index plan is dramatically more expensive, your table statistics
-- may be stale -- run ANALYZE documents; and retry

Detecting Slow Similarity Searches

Slow similarity searches in production fall into four categories: queries that are not using the index, queries that are using the index but hitting disk instead of memory, queries where ef_search or probes is set too high for the latency target, and queries that are slow because of post-index filters on large intermediate result sets. Each has a distinct signature in pg_stat_statements and EXPLAIN ANALYZE output.

Identify the Top Offending Vector Queries

sql

-- Full diagnostic query for slow similarity searches
-- Includes rows-per-call ratio to detect queries returning too many results
SELECT
    LEFT(query, 150)                                    AS query_preview,
    calls,
    ROUND(mean_exec_time::numeric, 2)                   AS mean_ms,
    ROUND(max_exec_time::numeric, 2)                    AS max_ms,
    ROUND(stddev_exec_time::numeric, 2)                 AS stddev_ms,
    ROUND(total_exec_time::numeric / 1000.0 / 60, 3)   AS total_minutes,
    rows,
    ROUND(rows::numeric / NULLIF(calls, 0), 1)          AS rows_per_call,
    shared_blks_hit,
    shared_blks_read,
    ROUND(
        shared_blks_hit::numeric /
        NULLIF(shared_blks_hit + shared_blks_read, 0) * 100,
        1
    )                                                   AS buffer_hit_pct
FROM pg_stat_statements
WHERE query LIKE '%<=>%'
  AND calls > 10
ORDER BY mean_exec_time DESC
LIMIT 25;

The buffer_hit_pct column from pg_stat_statements is particularly diagnostic: a vector query with a buffer_hit_pct below 85% is reading significant HNSW graph pages from disk on each call. This is the memory pressure signature — the HNSW index is too large for the available buffer cache. The solution is either to increase shared_buffers and total RAM, reduce the index size by decreasing m, reduce ef_search to traverse fewer nodes, or partition the table to keep individual HNSW indexes smaller.

Log Slow Vector Queries Directly

sql

-- Enable auto_explain for queries above your latency threshold
-- Add to postgresql.conf (reload required, no restart):
--   shared_preload_libraries = 'pg_stat_statements,auto_explain'
--   auto_explain.log_min_duration = 100    -- log queries over 100ms
--   auto_explain.log_analyze = on
--   auto_explain.log_buffers = on
--   auto_explain.log_verbose = off         -- keep log volume manageable
--   auto_explain.sample_rate = 0.1         -- sample 10% of queries

-- After enabling, slow vector queries will appear in postgresql log with
-- full EXPLAIN ANALYZE output, including buffer hit/miss detail.
-- Filter for vector queries in the log:
--   grep '<=>' /var/log/postgresql/postgresql-*.log | grep -i "seq scan"

Detect Index Bloat on High-Churn Vector Tables

If embeddings are frequently updated or deleted and reinserted, HNSW index bloat can degrade query performance. The index retains dead node entries until a VACUUM processes them, but HNSW does not support selective page reclamation the way btree does — a full REINDEX is the only path to reclaiming bloated space.

sql

-- Estimate index bloat for vector indexes using pg_stat_user_indexes
SELECT
    schemaname,
    relname                                         AS table_name,
    indexrelname                                    AS index_name,
    pg_size_pretty(pg_relation_size(indexrelid))    AS index_size,
    idx_scan,
    idx_tup_read,
    idx_tup_fetch,
    ROUND(
        idx_tup_read::numeric / NULLIF(idx_scan, 0),
        1
    )                                               AS avg_tuples_per_scan
FROM pg_stat_user_indexes
WHERE indexrelname LIKE '%hnsw%'
   OR indexrelname LIKE '%ivfflat%'
ORDER BY pg_relation_size(indexrelid) DESC;

Alerting and Dashboards for Vector Workloads

Effective alerting for pgvector workloads requires three tiers of signal: immediate alerts that fire on active degradation, warning-level alerts that give you time to act before an incident, and informational metrics that surface in dashboards for trend analysis. The thresholds below are starting points calibrated against typical AI application query patterns — adjust them based on your SLA and observed baseline.

Critical Alerts (Page Immediately)

sql

-- Alert 1: Sequential scan detected on a vector table in the last 5 minutes
-- This almost always means active performance degradation for users
SELECT relname, seq_scan, last_seq_scan
FROM pg_stat_user_tables
WHERE relname IN ('documents', 'embeddings', 'document_chunks')
  AND last_seq_scan > NOW() - INTERVAL '5 minutes'
  AND seq_scan > 0;

-- Alert 2: Mean similarity search latency above SLA threshold
-- Fire when mean_exec_time for cosine queries exceeds your p50 SLA (e.g., 200ms)
SELECT
    LEFT(query, 80)     AS query,
    calls,
    ROUND(mean_exec_time::numeric, 2) AS mean_ms
FROM pg_stat_statements
WHERE query LIKE '%<=>%'
  AND mean_exec_time > 200    -- adjust to your SLA in milliseconds
  AND calls > 50;             -- avoid noise from rare queries

Warning Alerts (Respond Within Hours)

sql

-- Alert 3: Buffer hit rate below 90% for vector tables
-- Indicates index is not fitting in memory; latency degradation is coming
SELECT
    s.relname,
    ROUND(
        s.heap_blks_hit::numeric /
        NULLIF(s.heap_blks_hit + s.heap_blks_read, 0) * 100,
        1
    ) AS buffer_hit_pct
FROM pg_stat_user_tables s
WHERE s.relname IN ('documents', 'embeddings', 'document_chunks')
  AND (s.heap_blks_hit + s.heap_blks_read) > 1000
  AND (s.heap_blks_hit::numeric /
       NULLIF(s.heap_blks_hit + s.heap_blks_read, 0)) < 0.90;

-- Alert 4: Vector index missing (table exists but no HNSW/IVFFlat index)
SELECT
    c.relname                                       AS table_name,
    COUNT(i.indexrelid)                             AS vector_index_count
FROM pg_class c
JOIN pg_attribute a ON a.attrelid = c.oid
JOIN pg_type t ON t.oid = a.atttypid
LEFT JOIN pg_index ix ON ix.indrelid = c.oid
LEFT JOIN pg_class i ON i.oid = ix.indexrelid
    AND i.relam IN (
        SELECT oid FROM pg_am WHERE amname IN ('hnsw', 'ivfflat')
    )
WHERE t.typname = 'vector'
  AND c.relkind = 'r'
GROUP BY c.relname
HAVING COUNT(i.indexrelid) = 0;

Dashboard Metrics to Expose

These queries are appropriate for polling every 60 seconds from a monitoring agent (Prometheus postgres_exporter custom queries, Datadog autodiscovery, or a cron-driven metrics pusher) and graphing over time:

sql

-- Metric: similarity search call rate (queries/second over last minute)
SELECT
    ROUND(SUM(calls)::numeric / 60, 2)  AS vector_qps
FROM pg_stat_statements
WHERE query LIKE '%<=>%'
  AND query NOT LIKE '%pg_stat_statements%';

-- Metric: p50 / p95 approximation using mean and stddev as proxy
-- True percentiles require pg_stat_statements track_io_timing = on
-- and additional tooling (pgBadger, Datadog APM, etc.)
SELECT
    ROUND(AVG(mean_exec_time)::numeric, 2)          AS avg_mean_ms,
    ROUND(MAX(mean_exec_time)::numeric, 2)           AS worst_mean_ms,
    ROUND(AVG(stddev_exec_time)::numeric, 2)         AS avg_stddev_ms,
    SUM(calls)                                       AS total_calls
FROM pg_stat_statements
WHERE query LIKE '%<=>%';

Tip

For true percentile tracking of vector query latency in production, instrument at the application layer rather than relying solely on pg_stat_statements. Wrap your vector search calls in application-level timing that records p50, p95, and p99 latency per query type, and emit those as metrics to your observability platform. PostgreSQL aggregates execution time across all calls for a given query fingerprint, which prevents direct percentile computation from the statistics table alone.

Key Takeaways

Key Takeaways

Vector workloads have distinct failure modes from standard relational workloads. Instrument them specifically: pg_stat_statements filtered on '<=>' for cosine queries, pg_stat_user_tables for sequential scan detection, and buffer hit rate per vector table for memory pressure.
HNSW delivers recall above 95% with lower query latency but requires 2–4x the raw vector data in memory to serve queries at full speed. If the index does not fit in memory, p99 latency becomes unpredictable and high variance appears in pg_stat_statements stddev.
IVFFlat uses significantly less memory and allows recall tuning at query time via SET ivfflat.probes, but the index must be built after representative data is loaded. Indexes built on sparse or empty tables silently degrade recall without error.
Use SET hnsw.ef_search = 100 for quality-sensitive paths (RAG retrieval, document search) and SET hnsw.ef_search = 20 for latency-sensitive paths (autocomplete, real-time recommendations). Set this per session or per workload path, not as a global default.
Alert immediately on sequential scans on vector tables and on mean similarity search latency exceeding your SLA. Alert with time to act on buffer hit rates below 90% and on vector tables that are missing their ANN indexes.
High stddev_exec_time relative to mean_exec_time in pg_stat_statements for vector queries is the canonical signature of memory pressure on an HNSW index — the graph is partially being read from disk. Increase shared_buffers, add RAM, reduce m, or partition the table.

Working with JusDB on AI Database Operations

Operating pgvector in production requires combining PostgreSQL internals knowledge — buffer management, query planner behavior, VACUUM scheduling — with AI infrastructure patterns that most database teams encounter for the first time when they ship their first embedding pipeline. JusDB's database engineers have worked with teams across SaaS, fintech, and enterprise AI products to instrument, tune, and scale pgvector deployments: setting up the right monitoring baseline before launch, diagnosing recall regressions after data distribution shifts, and right-sizing HNSW index parameters for specific embedding model dimensionalities and query concurrency profiles.

If you are adding vector search to an existing PostgreSQL instance, planning a migration from a dedicated vector database, or troubleshooting latency in a vector workload that is already live, our team can review your schema, index configuration, and monitoring setup and give you a concrete remediation plan.

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