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PostgreSQL·22 min read·September 2, 2026

How to Make PostgreSQL Cheaper Without Sacrificing Performance

PostgreSQL does not have to become unsustainably expensive as your workload scales. Discover practical, production-oriented architectural strategies—covering connection pooling, bloat management, indexing discipline, memory allocation, cold data tiering, and analytical execution—while preserving low latency and high throughput.

KOLMOS Engineering Team
KOLMOS Engineering Team
Database Systems & Cloud Architecture · KOLMOS Systems
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How to Make PostgreSQL Cheaper Without Sacrificing Performance
How to Make PostgreSQL Cheaper Without Sacrificing Performance
TL;DR — How to Make PostgreSQL Cheaper:
- Right-size compute before upgrading: Avoid sizing instances purely for peak connection counts. Deploy an external connection pooler to prevent CPU starvation caused by context switching.
- Tame table and index bloat: Tune autovacuum to trigger proactively on high-churn tables, and use online tools like pg_repack to return unallocated disk space to the OS.
- Tune memory conservatively: Use roughly 25% of system RAM as a baseline starting point for shared_buffers, and set work_mem dynamically inside complex reporting sessions rather than inflating it globally.
- Cut billable I/O at the root: Eliminate sequential scans on large tables by introducing partial, composite, or covering (INCLUDE) indexes, and drop unused indexes that tax write throughput.
- Tier and partition cold data: Partition high-volume time-series or event data to enable partition pruning, and archive stale records (>90 days) to low-cost object storage.
- Avoid unnecessary read replicas: Fix underlying unindexed queries before adding replicas that duplicate compute, storage, and cross-AZ networking costs.
- Isolate heavy analytical queries: When mixed HTAP or analytical workloads choke your primary transactional instance, introduce a specialized execution layer equipped with columnar compression and vectorized execution like KOLMOS.
PostgreSQL does not have to become unsustainably expensive as your workload scales.
When a database slows down, the most common—and costly—engineering reaction is to immediately upgrade compute instances, attach larger memory tiers, provision higher IOPS, or deploy read replicas. But scaling hardware before diagnosing resource consumption usually papers over underlying inefficiencies rather than resolving them.
PostgreSQL costs stem from a coupled mix of compute allocations, persistent storage provisioning, I/O consumption, cross-AZ replication bandwidth, and inefficient query execution plans.
This guide outlines practical, production-oriented architectural strategies to reduce your cloud database spend—covering connection pooling, bloat management, indexing discipline, memory allocation, cold data tiering, and analytical execution—while preserving low latency and high throughput.

1. Where Does the PostgreSQL Bill Actually Come From?

Before changing any configuration settings, it helps to break down where the money goes. Managed cloud PostgreSQL services (such as AWS RDS/Aurora, Google Cloud SQL, and Azure Database for PostgreSQL) bill across several coupled resource dimensions:
Billing VectorPrimary DriversCost Impact
**Compute (vCPU)**Backend connection overhead, query planning, CPU context switches, unindexed filtering.High monthly baseline; instances often remain oversized to absorb rare spikes.
**Memory (RAM)**Buffer caching, connection memory overhead, sorting/hashing scratch space (`work_mem`).Drives up instance sizing tiers when working sets grow large.
**Storage Provisioning**Allocated disk volume (GB/TB), high-water marks, table and index bloat.Persistent monthly infrastructure cost that rarely shrinks automatically.
**Storage I/O Operations**Cache misses, sequential disk scans, continuous WAL flushes.Variable, unpredictable operational cost (particularly on per-request I/O models).
**Replication Fleet**Read standby instances, failover nodes, cross-AZ replication bandwidth.Multiplies compute and storage costs linearly with every replica added.
Understanding this breakdown helps prevent one common mistake: optimizing one resource only to drive up another. For instance, allocating excessive RAM globally can trigger Out-of-Memory (OOM) events, while cutting storage allocation without managing bloat can lead to complete disk saturation.
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2. Right-Sizing Compute: Eliminating Connection Churn

PostgreSQL uses a dedicated process-per-connection architecture. Whenever a client establishes a connection, the postmaster forks a new backend process (postgres: user db client). Each process consumes memory for session states and local operations, while also adding overhead to the Linux kernel scheduler.

The Problem with Direct High-Concurrency Connections

When application fleets spin up hundreds of worker threads—each maintaining its own connection pool—thousands of backend processes compete for CPU cycles. At elevated connection counts, a significant fraction of CPU capacity is spent handling operating system context switches, process contention, and spinlocks rather than processing SQL statements.
text 
Without Connection Pooling:
[App Fleet: 2,500 Direct Connections]
                 │
                 ▼
[PostgreSQL Backend: 2,500 Processes] ---> Elevated Context Switching & Lock Contention
(High CPU Utilization, High Latency, Oversized Instance Required)

With External Connection Pooling:
[App Fleet: 2,500 Client Connections]
                 │
                 ▼
   [PgBouncer / PgCat Pooler]
                 │ (Controlled Multiplexing: ~64 Server Connections)
                 ▼
[PostgreSQL Backend: 64 Processes]    ---> Efficient CPU Scheduling, High Throughput
(Low CPU Overhead, Stable Latency, Smaller Instance Footprint)

Deploying External Connection Pooling

Instead of jumping to a larger compute instance to handle high connection counts, place an external connection pooler like PgBouncer or pgcat in front of PostgreSQL, running in transaction pooling mode.
In this setup, thousands of client connections are multiplexed into a lean pool of server connections (typically sized near $2 \times \text{vCPU count} + \text{effective spindle count}$).
A standard pgbouncer.ini configuration for transaction pooling:
ini 
[databases]
production_db = host=127.0.0.1 port=5432 dbname=production_db pool_size=64 reserve_pool=10

[pgbouncer]
listen_addr = 0.0.0.0
listen_port = 6432
auth_type = scram-sha-256
auth_file = /etc/pgbouncer/userlist.txt
pool_mode = transaction
max_client_conn = 10000
default_pool_size = 64
min_pool_size = 10
reserve_pool_size = 10
reserve_pool_timeout = 5.0
max_db_connections = 100
Operational Trade-off: Transaction pooling breaks session-level state such as named prepared statements (unless protocol-level workarounds are enabled), session-level temporary tables, and LISTEN/NOTIFY. For microservices requiring session features, reserve a separate, smaller connection pool operating in session mode.

3. Storage Optimization: Controlling Table and Index Bloat

PostgreSQL's Multi-Version Concurrency Control (MVCC) handles concurrent access by treating updates as a delete-and-insert operation. An UPDATE writes a brand new tuple version to disk while marking the old version dead.
If maintenance routines do not clean up dead rows fast enough, they accumulate as "bloat," inflating physical disk usage and diluting cache density.

Auditing Bloat and Dead Tuples

Use pg_stat_user_tables to identify tables with high dead tuple counts:
sql 
SELECT
    schemaname,
    relname,
    pg_size_pretty(pg_total_relation_size(relid)) AS total_size,
    pg_size_pretty(pg_relation_size(relid)) AS table_size,
    pg_size_pretty(pg_total_relation_size(relid) - pg_relation_size(relid)) AS index_size,
    n_dead_tup,
    n_live_tup,
    ROUND((n_dead_tup::float / NULLIF(n_live_tup + n_dead_tup, 0))::numeric * 100, 2) AS dead_tuple_ratio,
    last_autovacuum
FROM pg_stat_user_tables
WHERE n_live_tup + n_dead_tup > 100000
ORDER BY pg_total_relation_size(relid) DESC
LIMIT 15;

Tuning Autovacuum for High-Churn Workloads

PostgreSQL defaults for autovacuum are deliberately conservative to prevent maintenance tasks from overwhelming low-spec systems. On large tables, the default scale factor (autovacuum_vacuum_scale_factor = 0.20) means autovacuum will not run until 20% of the table's rows have changed. On a table with 50 million rows, that requires 10 million row modifications before cleanup triggers.
For write-heavy workloads, tune autovacuum to trigger more frequently and process batches faster:
ini 
# Global postgresql.conf maintenance tuning
autovacuum_max_workers = 4
autovacuum_vacuum_cost_limit = 2000     # Default 200 often throttles autovacuum too heavily
autovacuum_vacuum_cost_delay = 2        # Minimizes sleep delays during vacuum cycles
Fine-grained overrides for active, high-churn tables:
sql 
ALTER TABLE transaction_records SET (
    autovacuum_vacuum_scale_factor = 0.02,   -- Trigger vacuum at 2% row churn
    autovacuum_vacuum_threshold = 10000,
    autovacuum_vacuum_cost_limit = 5000
);

Reclaiming Space Online with pg_repack

Standard VACUUM marks dead tuple space as reusable for future inserts, but it rarely returns allocated storage blocks back to the underlying operating system. VACUUM FULL does reclaim physical disk space, but it requires an exclusive lock (ACCESS EXCLUSIVE) that blocks all reads and writes.
To reclaim physical disk storage without downtime, use the open-source extension pg_repack:
bash 
# Rebuilds the relation and its indexes online without read/write locks
pg_repack -h db-primary.internal -U postgres -d production_db -t transaction_records

4. Memory Architecture: Sizing Cache and Scratch Allocations

Memory optimization aims to keep active working data in memory, reduce disk I/O, and prevent sorting tasks from spilling to temporary disk files.
text 
Total Machine RAM
├── shared_buffers             ---> Database shared cache (typically ~25% as a starting point)
├── OS Page Cache              ---> Kernel cache handling read-aheads and write-backs
├── work_mem (per operation)   ---> Allocations for Sorts, Hashes, and Aggregations
└── maintenance_work_mem       ---> Operations like VACUUM, CREATE INDEX, ALTER TABLE

Sizing shared_buffers

A common recommendation is to set shared_buffers to roughly 25% of total system RAM as a starting point.
Because PostgreSQL relies on standard POSIX system calls, data loaded from disk typically passes through the operating system’s page cache before entering shared_buffers. Setting shared_buffers to an excessively high percentage (such as 70–80%) can lead to double buffering and starve the Linux kernel of space needed for sequential read-aheads and write buffering.
Note on Environment: The optimal shared_buffers configuration depends on your working-set size, OS configuration, and whether the system runs on dedicated bare metal, standard cloud VMs, or containerized environments. Treat 25% as an initial operational baseline, validate cache metrics using pg_buffercache, and adjust based on measured results.

Avoiding Disk Spills with Targeted work_mem

The work_mem parameter dictates how much memory can be consumed by an internal sort operation or hash table before PostgreSQL writes data to temporary disk files.
Inspecting whether queries are spilling to disk can be done directly via log settings:
ini 
log_temp_files = 1024 # Log temporary files larger than 1 MB
Setting work_mem too high across all connections creates a major risk of Out-of-Memory (OOM) crashes, because a single complex query with multiple joins and sort stages can allocate work_mem multiple times across parallel workers.
Instead of setting a dangerously high global work_mem, set it selectively within the session executing the heavy query:
sql 
BEGIN;
-- Set higher allocation strictly for the current transaction scope
SET LOCAL work_mem = '128MB';

SELECT customer_id, count(*), sum(order_total)
FROM historical_orders
WHERE created_at >= '2026-01-01'
GROUP BY customer_id
ORDER BY sum(order_total) DESC;

COMMIT;

5. Slashing I/O Usage Through Indexing Discipline

I/O operations drive a significant portion of the variable cost in cloud database environments. Lowering I/O requirements directly stabilizes your budget and improves query execution speed.

Diagnosing Inefficient Execution with EXPLAIN (ANALYZE, BUFFERS)

The most reliable way to spot unnecessary I/O is to inspect execution plans using BUFFERS:
sql 
EXPLAIN (ANALYZE, BUFFERS)
SELECT id, user_id, order_total 
FROM orders 
WHERE status = 'pending' 
ORDER BY created_at DESC 
LIMIT 50;
Look closely at the resulting execution output:
text 
->  Sort  (cost=125430.12..125430.25 rows=50 width=24) (actual time=142.12..142.15 rows=50 loops=1)
      Sort Key: created_at DESC
      Sort Method: top-N heapsort  Memory: 29kB
      Buffers: shared hit=412 read=18452
      ->  Seq Scan on orders  (cost=0.00..124112.00 rows=250000 width=24) (actual time=0.04..118.45 rows=248900 loops=1)
            Filter: (status = 'pending'::text)
            Rows Removed by Filter: 4751100
            Buffers: shared hit=412 read=18452
Here, shared read=18452 indicates that over 18,000 blocks (~144 MB) were read directly from disk storage because the query executed a full sequential scan across millions of filtered rows.

Deploying Partial and Covering Indexes

To resolve queries like the one above without indexing the entire table, deploy targeted indexing patterns:
1. Partial Indexes If only a small fraction of orders are in a pending state, build an index covering only that subset:
sql 
-- Indexes only the active rows, drastically reducing index size and write overhead
CREATE INDEX idx_orders_active_pending 
ON orders (created_at DESC) 
WHERE status = 'pending';
2. Covering Indexes (INCLUDE) To satisfy a query entirely within the index and avoid heap block lookups, use covering indexes:
sql 
-- Resolves lookups purely inside the index pages (Index-Only Scan)
CREATE INDEX idx_user_account_lookup 
ON users (email) 
INCLUDE (id, account_tier, is_active);

Dropping Unused Indexes

Unused indexes waste storage space and force PostgreSQL to update auxiliary structures on every insert, update, and delete.
Identify indexes with zero usage:
sql 
SELECT
    schemaname,
    relname AS table_name,
    indexrelname AS index_name,
    pg_size_pretty(pg_relation_size(i.indexrelid)) AS index_size,
    idx_scan AS number_of_scans
FROM pg_stat_user_indexes ui
JOIN pg_index i ON ui.indexrelid = i.indexrelid
WHERE NOT indisunique
  AND idx_scan = 0
  AND pg_relation_size(i.indexrelid) > 10485760 -- Focus on indexes > 10MB
ORDER BY pg_relation_size(i.indexrelid) DESC;

6. Cold Data Strategy: Archiving and Declarative Partitioning

Letting historical, append-only logs sit indefinitely inside your primary transactional tables inflates backup sizes, slows maintenance operations, and pollutes memory caches.
text 
Access Frequency Profile:
[ Recent Data: Hot ]      --> High access frequency (Requires fast transactional storage & RAM)
[ Historical Data: Cold ]  --> Rare analytical queries (Better suited for compressed or external tiers)

Declarative Range Partitioning

Partitioning splits a large logical table into discrete physical tables. This lets the PostgreSQL query planner use Partition Pruning to skip older partitions entirely when query filters contain date boundaries:
sql 
-- Partition parent table
CREATE TABLE telemetry_events (
    event_id UUID NOT NULL,
    device_id BIGINT NOT NULL,
    payload JSONB,
    recorded_at TIMESTAMPTZ NOT NULL
) PARTITION BY RANGE (recorded_at);

-- Discrete monthly partitions
CREATE TABLE telemetry_events_2026_08 PARTITION OF telemetry_events
    FOR VALUES FROM ('2026-08-01 00:00:00+00') TO ('2026-09-01 00:00:00+00');

CREATE TABLE telemetry_events_2026_09 PARTITION OF telemetry_events
    FOR VALUES FROM ('2026-09-01 00:00:00+00') TO ('2026-10-01 00:00:00+00');
Verify that partition pruning is actively operating:
sql 
EXPLAIN (ANALYZE, BUFFERS)
SELECT count(*) FROM telemetry_events
WHERE recorded_at >= '2026-09-01' AND recorded_at < '2026-09-15';
-- The plan will confirm that telemetry_events_2026_08 is excluded from execution entirely.

Offloading Immutable Records to Object Storage

For compliance or long-term auditing data older than 90–180 days, keeping records inside block storage is rarely cost-effective.
Consider exporting cold partitions into compressed formats (such as Parquet) and transferring them to cloud object storage (e.g., AWS S3, Cloudflare R2, or Azure Blob). This lowers storage costs, reduces primary snapshot footprints, and keeps active database backups fast.

7. Rethinking Read Replicas

Deploying read replicas is a common strategy for handling elevated read latency. However, each replica duplicates compute instance sizing, multiplies storage volume requirements, and incurs continuous cross-AZ network transfer charges.
text 
Standard Approach:
Primary Instance ($$) + Replica 1 ($$) + Replica 2 ($$)
Linear cost scaling for both compute and storage.
Before adding replicas to address load:
  • 1.Audit Slow Queries First: Use pg_stat_statements to pinpoint the queries consuming the most aggregate runtime:
    • sql 
       SELECT 
       query,
       calls,
       round(total_exec_time::numeric, 2) AS total_time_ms,
       round(mean_exec_time::numeric, 2) AS avg_time_ms,
       rows
   FROM pg_stat_statements
   ORDER BY total_exec_time DESC 
   LIMIT 10;
    Often, optimizing two or three high-frequency queries with covering indexes frees up enough CPU capacity to make additional replicas unnecessary.
  • 2.Implement Application-Level Caching: For static, slow-changing, or read-heavy endpoints, place an in-memory cache (like Redis) or an edge CDN in front of your database to absorb repetitive read patterns.
  • 3.Guard Against Replication Lag: Heavy write operations on the primary can lead to replication lag on standbys, causing read-after-write inconsistencies in application workflows.

    • 8. Query Optimization: High-Impact Code Patterns

    Application-level code frequently introduces query patterns that inflate database resource usage.

    Eradicating N+1 Query Patterns

    Object-Relational Mapping (ORM) frameworks often trigger N+1 query loops, producing hundreds of round-trip network requests to populate child collections:
    sql 
    -- Inefficient: 1 initial query + N follow-up lookups
SELECT id FROM accounts WHERE is_active = true;
-- Repeated across hundreds of entities:
SELECT * FROM users WHERE account_id = ?;
    Replace these with aggregated queries that execute in a single round trip:
    sql 
    -- Optimized single execution using JSON aggregation
SELECT 
    a.id AS account_id,
    a.company_name,
    COALESCE(json_agg(u.*) FILTER (WHERE u.id IS NOT NULL), '[]') AS users
FROM accounts a
LEFT JOIN users u ON u.account_id = a.id
WHERE a.is_active = true
GROUP BY a.id, a.company_name;

    Mindful Use of JSONB

    Storing unstructured documents inside JSONB columns offers flexibility, but it introduces architectural overhead:
  • Updates to any nested property require rewriting the entire JSON document on disk, generating significant write bloat and WAL volume.
  • Analytical filtering across nested JSON attributes requires runtime document parsing, which burns CPU cycles unless dedicated expression indexes are created.
  • For columns that drive regular filtering, joins, or aggregations, extract those properties into explicitly typed relational columns.

    • 9. Where a Performance Engine Fits

    Standard row-oriented PostgreSQL excels at transactional (OLTP) workloads: inserting individual rows, updating account balances, and fetching point records by primary key.
    However, as organizations grow, databases are often asked to handle mixed workloads (HTAP): aggregating metrics across millions of rows, processing analytical queries, running cohort calculations, and querying large append-only event tables.
    In a traditional row-oriented layout, analytical queries require scanning entire 8 KB pages off disk, pulling every column into memory even when only two are needed. Over time, these analytical scans compete with transactional operations for CPU time and buffer pool space, prompting teams to make expensive hardware upgrades or add dedicated replicas.
    text 
    Workload Contention in Row-Oriented Tables:
[ OLTP Transactions ] ──┐
                         ├──> [ Shared Memory / Buffer Pool ] <── (Disk Thrashing & Eviction)
[ Analytical Queries ]  ──┘
    When configuration tuning and indexing reach their architectural limits on mixed workloads, teams typically face two options:
  • 1.The Distributed Split: Extract, transform, and load (ETL) transactional data into an external data warehouse (such as Snowflake, ClickHouse, or BigQuery). This adds operational complexity, pipeline maintenance, and egress costs.
  • 2.Specialized Execution Layers: Introduce an engine built for analytical workloads that provides columnar compression and vectorized execution while maintaining compatibility with your existing PostgreSQL ecosystem.
    • This is where a specialized performance engine fits naturally into the architecture.

    10. Augmenting PostgreSQL Workloads with KOLMOS

    Instead of maintaining brittle ETL pipelines or over-provisioning primary database instances, KOLMOS provides an execution layer designed specifically for data-intensive analytical and aggregation workloads.
    text 
                      Application Layer
                          │
                          ▼
            PostgreSQL-Compatible Interface
                          │
                          ▼
                       KOLMOS
                          │
          ┌───────────────┴───────────────┐
          ▼                               ▼
  Columnar Storage               Vectorized Execution
(High-density compression)      (SIMD-accelerated batching)
          │                               │
          └───────────────┬───────────────┘
                          ▼
            Faster Analytical Execution
          Lower Infrastructure Consumption

    Architectural Advantages

  • Columnar Storage Efficiency: Standard PostgreSQL reads data row-by-row. KOLMOS organizes data column-by-column, allowing analytical queries to read only the columns relevant to the query. Grouping identical data types together also enables modern compression algorithms, which can substantially reduce storage requirements and disk I/O demands.
  • Vectorized Query Execution: Row-oriented query processing evaluates expressions one row at a time. KOLMOS leverages SIMD (Single Instruction, Multiple Data) processing to evaluate filters and aggregations over arrays of values simultaneously, cutting the compute resources needed for intensive aggregations.
  • Reduced Architectural Complexity: Moving data into a completely separate external data warehouse creates synchronization latency, schema drift, and added infrastructure maintenance. Using an execution engine that integrates with PostgreSQL patterns lets teams accelerate heavy queries while preserving familiar tooling, drivers, and query interfaces.
  • Isolating Analytical Overhead: By running intensive reporting and scan-heavy aggregations through a specialized execution layer, you prevent analytical queries from evicting hot transactional pages from your primary cache. This allows your transactional instance to run reliably on a leaner, right-sized compute tier.

    • Architectural Checklist: A Sensible Path Forward

    Tackling database costs is an iterative process. Focus on low-risk configuration and indexing wins before considering structural changes:

    Phase 1: Immediate Hygiene

  • [ ] Audit Connection Pools: Deploy PgBouncer or pgcat in transaction pooling mode to cap active server backends.
  • [ ] Clean Inefficient Indexes: Identify and drop unused indexes with zero scans using pg_stat_user_indexes.
  • [ ] Identify Expensive Queries: Review pg_stat_statements to find the top queries by total_exec_time, and use EXPLAIN (ANALYZE, BUFFERS) to diagnose sequential scans.

    • Phase 2: Configuration & Maintenance Tuning

  • [ ] Reclaim Wasted Space: Evaluate table bloat and use pg_repack to reclaim unallocated disk space online.
  • [ ] Tune Autovacuum: Lower autovacuum_vacuum_scale_factor on high-churn tables to prevent bloat from building up.
  • [ ] Review Memory Boundaries: Set shared_buffers to an initial baseline of ~25% of RAM, and apply higher work_mem selectively in reporting sessions rather than globally.

    • Phase 3: Structural Scaling

  • [ ] Partition Growing Datasets: Implement declarative range partitioning on tables that exceed tens of millions of rows.
  • [ ] Tier Inactive Data: Archive cold historical data to object storage rather than keeping it on high-performance primary disk.
  • [ ] Adopt Purpose-Built Engines for Analytics: When analytical aggregations and reporting workloads start competing with core transactional throughput, evaluate performance engines like KOLMOS to leverage columnar storage and vectorized execution—keeping resource consumption low without breaking PostgreSQL compatibility.

    • Frequently Asked Questions (FAQs)

    What is the fastest way to reduce PostgreSQL cloud costs without downtime?

    The fastest zero-downtime interventions are connection pooling and indexing cleanup. Placing PgBouncer in transaction pooling mode immediately caps database processes and drops CPU load caused by context switching. Identifying and dropping large unused indexes via pg_stat_user_indexes instantly reduces disk consumption and write I/O overhead without disrupting active user traffic.

    Why is VACUUM not freeing up disk space on my cloud volume?

    Standard VACUUM marks dead tuples as free space within existing data pages so they can be reused for future INSERT and UPDATE statements, but it does not return physical disk space to the underlying filesystem. While VACUUM FULL does reclaim physical space, it locks the table exclusively. To reclaim disk blocks without read/write downtime, run the pg_repack utility.

    When should I use read replicas versus connection pooling?

    Use connection pooling when your database CPU is saturated due to a high volume of open connections, thread context switching, or short bursty queries. Use read replicas when your queries are already indexed and optimized, but pure read throughput exceeds the physical I/O and memory capacity of a single primary instance. Replicas should not be deployed to mask slow, unindexed queries.

    Is setting shared_buffers to 50% or more of RAM a good idea?

    Generally, no. PostgreSQL relies on the operating system’s kernel page cache for filesystem read-ahead and buffered writes. Over-allocating shared_buffers (e.g., beyond 40%) risks double-buffering and starves the kernel cache, often degrading sequential scan performance. A baseline of ~25% of system RAM is a standard initial configuration, which can be fine-tuned based on cache hit monitoring.

    How does columnar storage lower cloud PostgreSQL costs?

    Standard PostgreSQL row storage requires the engine to load all columns of a row into memory during a scan, even if only one column is referenced. Columnar storage organizes data on disk by attribute, reading only the specific columns needed by the query. Because identical data types sit contiguously, columnar engines achieve significantly higher compression ratios, cutting storage footprints and reducing billable disk I/O requests.

    Where does an execution layer like KOLMOS fit compared to an external data warehouse?

    An external data warehouse (e.g., Snowflake, BigQuery) requires extracting data via complex ETL/ELT pipelines, maintaining dual schemas, and managing external egress and licensing costs. An execution engine like KOLMOS acts as an integrated performance layer within the PostgreSQL ecosystem. It executes analytical aggregations, vector math, and scan-heavy queries using columnar and SIMD acceleration while retaining PostgreSQL compatibility, avoiding the operational complexity of maintaining separate data silos.

    Connect with the KOLMOS Team

    Have questions about optimizing your PostgreSQL infrastructure or evaluating self-compressing storage engines?
  • LinkedIn Account: https://www.linkedin.com/company/kolmos
  • Official Website: https://kolmos.dev/
  • Systems Architecture: https://kolmos.dev/architecture
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