DEV Community: Pavel

PostgreSQL Benchmark: AWS RDS vs Hostim vs Self-Hosted on Hetzner (2026)

Pavel — Tue, 07 Jul 2026 11:04:02 +0000

Short answer first: at the same size (2 vCPU / 4 GB, PostgreSQL 16), Hostim had the fastest writes, about 2.5× the write throughput of AWS RDS db.t4g.medium and 2.1× a default self-hosted Postgres on Hetzner. Hetzner had the fastest reads, on raw per-core CPU speed. RDS was slowest or near-slowest on both, and its listed price is the smallest part of the real bill.

This post shows every number, the exact commands to reproduce them, and (because it changes the conclusion) what high availability actually costs on each platform.

Benchmarks run July 2026 on PostgreSQL 16, in a central-Europe region. Prices change often, so check each provider's live pricing page before you commit. The shape of the result changes far less than the exact numbers.

What we compared

Three ways to run a small production Postgres, all at 2 vCPU / 4 GB RAM, PostgreSQL 16, in a central-Europe region:

Offering	Instance	Listed price / month	Replicated (failover)?
Hostim managed Postgres	`drp-50` (2 vCPU / 4 GB)	€50	Yes, by default
AWS RDS	`db.t4g.medium` (2 vCPU / 4 GB)	~$48 (instance only)	No (single-AZ)
Self-hosted	Hetzner CPX22 (2 vCPU AMD / 4 GB, shared)	€19.49	No (single node)

The "replicated" column matters a lot for both price and performance. It gets its own section below.

Methodology

The goal was a like-for-like test, not a flattering one. The rules:

Same DB size: 2 vCPU / 4 GB on every target.
Same Postgres major version: 16.
A separate load generator per target, one network hop from the database, in the same region, never on the database box itself. Each client had 4 vCPU so the client was never the bottleneck (checked with mpstat).
Same workload: pgbench, scale factor 50 (about 750 MB, which fits in RAM so the test measures CPU and the commit path, not cold disk reads), 300-second runs.

Client setup:

sudo apt-get install -y postgresql-contrib nmap sysstat   # pgbench + nping
pgbench --version

The workload, run identically against each database:

# initialise (~750 MB)
pgbench -i -s 50

# write-heavy, TPC-B-like, 4 clients, 5 minutes
pgbench -c 4 -j 4 -T 300

# read-only, 8 clients, 5 minutes
pgbench -c 8 -j 4 -T 300 -S

# single connection, write, 1 minute (isolates commit/fsync latency)
pgbench -c 1 -T 60

Network latency was measured with a TCP ping to port 5432 (nping --tcp -p 5432) and a SELECT 1 round-trip, because ICMP is not open on every platform.

Configuration parity, stated plainly: the self-hosted Hetzner node ran stock Postgres defaults (shared_buffers 128 MB, no tuning). That is the realistic "install Postgres and go" baseline. RDS ran on its default parameter group, which AWS tunes for the instance size. Hostim runs its own managed tuning. In other words, the two managed options are tuned out of the box and the self-hosted default is not, and that difference is part of what you are comparing.

Results

All runs completed with zero failed transactions.

Write throughput (4 clients, TPC-B)

Target	TPS	Avg latency
Hostim `drp-50`	2,708	1.48 ms
Hetzner (default)	1,303	3.07 ms
AWS RDS `t4g.medium`	1,080	3.71 ms

Hostim did about 2.5× the write throughput of RDS and 2.1× the default Hetzner node.

Single-connection write latency (1 client)

This isolates the commit path. Every transaction waits for a WAL flush (synchronous_commit = on is the default), so it mostly measures storage fsync latency.

Target	TPS	Avg latency
Hostim `drp-50`	871	1.15 ms
AWS RDS `t4g.medium`	416	2.41 ms
Hetzner (default)	276	3.63 ms

Both self-hosted and RDS pay for network-attached block storage on the commit path. This is the single biggest driver of the write-throughput gap.

Read throughput (8 clients, SELECT-only)

Target	TPS	Avg latency
Hetzner (default)	20,068	0.40 ms
Hostim `drp-50`	14,333	0.56 ms
AWS RDS `t4g.medium`	13,261	0.60 ms

Reads scale with CPU, and the Hetzner box took this one on raw per-core AMD speed with no orchestration layer in the path. Hostim and RDS were close, with RDS last. Note that db.t4g.medium is a burstable instance: over a sustained 300-second run it can drop to its CPU-credit baseline, which is what the ~$48 tier is designed to provide. A workload that needs sustained CPU would move to an m-class RDS instance, which costs roughly twice as much.

Network latency

Target	TCP RTT (avg)	`SELECT 1`
Hostim `drp-50`	0.48 ms	~0.29 ms
Hetzner (default)	0.71 ms	~0.39 ms
AWS RDS `t4g.medium`	1.50 ms	~0.58 ms

The price you actually pay

This is where the listed numbers stop being useful.

AWS RDS: $48 is not the bill

The ~$48 is the instance only, single-AZ, on-demand. On top of that you pay, metered separately:

Storage: gp3 is billed per GB-month.
IOPS and throughput above the gp3 baseline: billed if you provision more.
Backups: retained backup storage beyond your volume size is billed.
Data transfer: egress and cross-AZ traffic are billed per GB.

One storage dropdown makes this concrete. Pick Provisioned IOPS SSD in the create-database wizard and RDS defaults to 3000 provisioned IOPS, billed per IOPS-month at about $300/month (io1 ≈ $0.10/IOPS, io2 ≈ $0.125/IOPS) on top of the instance and the GB storage. The gp3 default includes the same 3000 IOPS and 125 MB/s free up to 400 GB, so there it costs nothing. Same database, ~$300/month apart, from one dropdown.

A realistic single-AZ db.t4g.medium with a modest volume and normal traffic lands well above the sticker once storage, IOPS, backups, and egress are counted, and the exact figure changes month to month. Treat $48 as a floor, not a price. (We wrote about this metered-bill pattern in Cloud Rent in Action and Usage-Based Pricing.)

High availability changes the comparison

The results above are not apples-to-apples on resilience, and this is the important part.

Hostim drp-50 is replicated by default. The €50 includes a standby and automatic failover. Nothing to configure.
AWS RDS needs Multi-AZ for a standby with failover. Multi-AZ runs a second instance and roughly doubles the instance and storage cost. It also makes writes slower, because commits wait on the synchronous standby, so a fair, HA-to-HA comparison would put RDS writes below the single-AZ numbers measured above.
Self-hosted Hetzner has no failover at all on one node. To match Hostim you add a second VPS (another €20) and then build and operate the replication and failover yourself (streaming replication plus a tool like Patroni or repmgr, plus a routing layer), and you own every incident.

Normalised to "replicated Postgres with automatic failover":

Offering	Roughly what HA costs / month	Extra work
Hostim `drp-50`	€50, included	None
AWS RDS Multi-AZ	~2× instance + storage, plus egress and backups	None, but slower writes
Hetzner, 2 nodes	~€40 hardware	You build and run failover yourself

So the €50 Hostim number already includes the thing that doubles the AWS price and turns the Hetzner option into an operations project.

Caveats

db.t4g.medium is burstable ARM; sustained runs can hit the CPU-credit baseline. That is the behaviour of the price-matched tier, not a misconfiguration.
The self-hosted node used stock Postgres defaults on purpose. Tuning shared_buffers and friends would raise its numbers, but doing that is your job when you self-host.
RDS was single-AZ for the raw benchmark; Multi-AZ is slower and dearer, as noted above.
The dataset fit in RAM (scale 50), which isolates compute. A dataset larger than RAM would put more weight on storage and widen the write gaps, not close them.
Architectures differ (RDS is ARM Graviton, the others x86). This is what each provider sells at this price, so it is reported as-is.

Takeaways

For write-heavy workloads at this size, Hostim was fastest by a clear margin, mostly due to storage fsync latency on the commit path.
For read-heavy workloads, a self-hosted box had the raw per-core CPU edge, if you are willing to tune it and operate it.
AWS RDS at the price-matched tier was slowest or near-slowest on both, and its real cost (storage, IOPS, egress, and Multi-AZ for failover) is a multiple of the listed instance price.
If you want replicated Postgres without running it yourself, the flat, HA-included price is the number to compare against, not the single-node sticker.

Every number above is reproducible with the commands in the methodology section. If you are still deciding between managed and self-hosted in the first place, see Self-Host Postgres or Use Supabase? and Which Database Should You Self-Host?.

A managed, replicated Postgres at a flat price

If the parts you dislike are the climbing AWS bill and the operations work of running your own failover, that is exactly what Hostim is built to avoid. Managed Postgres is replicated by default, at a flat, predictable price: drp-50 is €50/month with the standby included. There is also a free database tier to test the fit, and new projects get a free 5-day trial with no card.

👉 Spin up a managed Postgres on Hostim, free tier, no card

Last updated: 2026-07-07. Benchmarks run 2026-07-07 on PostgreSQL 16, eu-central region.

Render vs Railway vs Fly.io: Pricing Compared (2026)

Pavel — Tue, 23 Jun 2026 05:03:13 +0000

Short answer first: the three platforms charge in three different shapes. Render adds a workspace fee for teams on top of fixed instance prices. Railway has no free tier and meters everything per second on top of a small plan fee. Fly.io is pure pay-as-you-go per second, with no base plan fee at all. The right pick depends less on the headline price and more on which shape fits how you work.

Prices are accurate as of June 2026. All three change pricing often — check the live page before you commit. The shapes change far less than the numbers.

The three shapes in one line each

Render — pick an instance size at a fixed monthly price, plus a flat workspace fee for teams. Predictable, with a real free tier that sleeps.
Railway — no free tier. A small monthly plan fee, then per-second metering of CPU, RAM, volumes, and egress on top.
Fly.io — no free tier, no seat fee. You pay per second for each running machine and for what it uses. The most "raw cloud" of the three.

Is there a free tier?

Platform	Free tier?	The catch
Render	Yes	Free web services spin down after ~15 min idle, then cold-start. Fine for demos, not production.
Railway	No	One-time $5 trial credit, no card. Then you are on a paid plan.
Fly.io	No	New accounts have no free allowance. You pay from the first running machine.

How usage metering works

Render bills fixed instance prices — choose a size (Starter ~$7/mo for 0.5 CPU/512 MB, Standard ~$25/mo for 1 CPU/2 GB) and pay it whether the app is busy or idle.

Railway and Fly.io both bill per second of actual use:

Railway meters memory, CPU, volumes, and egress on top of your plan fee. An always-on 1 GB service runs in the low tens of dollars/month past the included credit.
Fly.io bills each machine by the second. The smallest shared-cpu-1x / 256 MB is ~$2.02/mo if left on; more RAM is ~$5/GB/mo; egress in NA/EU is $0.02/GB. Stopped machines cost only their disk.

Per-second billing is cheaper for spiky or scale-to-zero workloads and harder to predict, because the bill moves with traffic.

Side-by-side

Factor	Render	Railway	Fly.io
Free tier	Yes (sleeps ~15 min)	No ($5 trial credit)	No (new accounts)
Billing model	Fixed instance price	Per-second usage + plan	Per-second usage
Workspace / team fee	$25/mo flat (Pro)	$20/mo flat (Pro)	None
Entry cost (solo)	Free or ~$7/mo	~$5–15/mo	~$2–7/mo
Best for spiky traffic	Weak (fixed size)	Good	Good
Cost predictability	High	Medium	Low–medium

When each wins

Render — predictable, fixed bills you can budget; a real free tier for demos (if you accept cold starts); simple over tunable.
Railway — clean DX with per-second billing that scales down when idle; great for spiky traffic; small enough team that the plan fee stays small.
Fly.io — lowest raw compute cost, no seat fees, multi-region or scale-to-zero, and you are fine with more configuration.

The pattern behind all three

All three start cheap and grow with usage, plan tier, or both. That is the normal SaaS shape — convenience now, a bill that climbs as you succeed. Worth paying if you use what you pay for.

If the part you dislike is the climbing bill, that is what we are building Hostim to avoid: deploy a container or git repo, pay a flat predictable price for the resources you pick. New projects get a free 5-day trial (no card), and managed Postgres/MySQL/Redis have a free tier of their own.

We're building Hostim.dev to make this simpler — happy to answer any questions.

Self-Host Postgres or Use Supabase? Here's How to Decide

Pavel — Mon, 08 Jun 2026 15:36:52 +0000

Short answer first: use Supabase if you want Postgres plus auth, realtime, storage, and a dashboard as one managed bundle. Self-host Postgres – or use a managed Postgres – if you mostly need a database and your app already handles its own auth and logic.

The choice is not really "Postgres vs Supabase". It's whether you need the extra layers Supabase puts on top of Postgres.

Supabase is not a database

Supabase runs on PostgreSQL, but it's a stack of services around it:

Postgres – the actual database
Auth – user signup, login, JWT tokens
Realtime – live updates over websockets
Storage – an S3-style file store
Edge Functions – serverless functions
Studio – dashboard + auto-generated REST/GraphQL API

So "self-host Postgres or use Supabase" compares a plain database to a full backend. The honest question: do you need those extra layers, or just the database underneath them?

A quick test:

You use Supabase Auth, Storage, and Realtime → Supabase earns its place.
You use one of them → it's replaceable.
You use none and treat it as "Postgres with a nice dashboard" → you want plain Postgres.

Side-by-side comparison

Factor	Supabase (managed)	Self-hosted Supabase	Plain Postgres (managed or self-hosted)
Database engine	PostgreSQL	PostgreSQL	PostgreSQL
Built-in auth	Yes	Yes	No (bring your own)
Realtime / websockets	Yes	Yes	No
File storage	Yes	Yes	No
Dashboard + auto API	Yes	Yes	No (use any SQL client)
Backups	Managed (limits by plan)	You manage	Managed or you manage
Cost shape	Metered, grows with usage	Server cost + your time	Database only
Self-host effort	None	High (many containers)	Low–medium
Lock-in	Medium–high	Medium	Very low

The lock-in point decides it for many teams. Your data is standard Postgres in every option (pg_dump portable). The lock-in is everything else: Auth tokens, Storage paths, Supabase-specific RLS policies, Edge Function code. The more Supabase-specific features you adopt, the harder the exit.

When each option wins

Pick managed Supabase when:

You're starting a new app and want auth, storage, and realtime working today.
You'll genuinely use at least two of those features.
You prefer to pay for convenience and not run infrastructure.

Pick self-hosted Supabase when:

You want the Supabase feature set but need full data ownership or on-prem.
You're ready to run and update a multi-container stack.
Compliance or cost at scale justifies the extra ops work.

Pick plain Postgres (managed or self-hosted) when:

You mainly need a reliable database, and your app already handles auth and logic.
You want minimal lock-in and a simple, predictable cost.
You value boring, standard Postgres you can move anywhere.

The honest summary

Supabase is a strong choice when you use its full stack. The moment you only want the database underneath it, you're paying in cost and lock-in for layers you don't run.

Need a backend → Supabase.
Need a backend you fully own → self-hosted Supabase.
Need a database → plain Postgres, managed or self-hosted.

I write about this stuff while building Hostim, a platform with managed Postgres built in. The full version of this post – with Docker Compose config and backup commands – is on the Hostim blog.

Let's Encrypt Wildcard Certs in Kubernetes: cert-manager + DNS-01 (and When We Skipped It)

Pavel — Tue, 05 May 2026 16:52:23 +0000

If you run Kubernetes and want a wildcard TLS cert from Let's Encrypt — say *.example.com — you need a DNS-01 challenge. HTTP-01 cannot prove control over a wildcard. That single fact rules out the easy path most tutorials show.

This post is what we actually run at Hostim.dev for our shared *.region.hostim.dev wildcard. We use cert-manager for per-app certs and a plain certbot Ansible playbook for the wildcard. Two different tools for two different jobs. We will explain why, then show the code for both.

Why two tools for one cluster?

You can do everything with cert-manager. It supports DNS-01 with a long list of providers. So why are we running a second tool?

Three reasons:

Our DNS provider (Namecheap) does not have a stable cert-manager webhook. There are community webhooks, but they break on upgrades. Maintaining one for a single cert is more work than running certbot once a quarter.
The wildcard cert covers our shared ingress, not user apps. It rotates rarely, lives in one namespace, and is read by every ingress as a TLS secret. cert-manager is built for the opposite case: many short-lived certs per Ingress.
A failed cert-manager renewal at 3 a.m. is hard to debug. A failed Ansible run on our laptop is a stack trace we can read.

For per-app domains (my-app.user.tld with cert-manager + HTTP-01), the controller-driven model wins. For the one shared wildcard, the manual model wins. Use the right tool.

Path A: cert-manager + HTTP-01 (per-app domains)

This is the standard path. Most apps want a cert for one or two hostnames. HTTP-01 is the simplest challenge: cert-manager spins up a temporary pod, the ACME server hits http://app.example.com/.well-known/acme-challenge/..., the pod responds, the cert is issued.

1. Install cert-manager

kubectl apply -f https://github.com/cert-manager/cert-manager/releases/download/v1.16.1/cert-manager.yaml

Wait for the three pods (cert-manager, cert-manager-webhook, cert-manager-cainjector) to be ready.

2. Create a ClusterIssuer

apiVersion: cert-manager.io/v1
kind: ClusterIssuer
metadata:
  name: letsencrypt-prod
spec:
  acme:
    server: https://acme-v02.api.letsencrypt.org/directory
    email: you@example.com
    privateKeySecretRef:
      name: letsencrypt-prod-account
    solvers:
      - http01:
          ingress:
            class: nginx

Apply it. cert-manager will register an ACME account on first use.

3. Annotate your Ingress

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: my-app
  annotations:
    cert-manager.io/cluster-issuer: letsencrypt-prod
spec:
  tls:
    - hosts: ["app.example.com"]
      secretName: app-example-com-tls
  rules:
    - host: app.example.com
      http:
        paths:
          - path: /
            pathType: Prefix
            backend:
              service:
                name: my-app
                port:
                  number: 80

That is it. cert-manager sees the annotation, requests the cert, solves the HTTP-01 challenge, writes the cert into the app-example-com-tls secret. Renewal is automatic.

This works for any number of distinct hostnames. We do this exact thing for every user app on hostim.dev.

Path B: certbot + DNS-01 (the wildcard)

For *.region.hostim.dev, HTTP-01 cannot work — the ACME server cannot resolve every possible subdomain. We need DNS-01: prove control over the parent domain by adding a TXT record.

You can do this with cert-manager and a DNS-01 webhook for your provider. We chose not to. Here is the Ansible playbook we run instead.

The flow

Ansible writes two scripts: an auth hook (creates the TXT record) and a cleanup hook (deletes it).
certbot --manual --preferred-challenges dns runs the auth hook, waits for DNS to propagate, lets ACME verify, then runs the cleanup hook.
The resulting fullchain.pem and privkey.pem get loaded into a Kubernetes Secret of type kubernetes.io/tls.
Every ingress in the shared namespace references that secret.

The playbook (trimmed)

- name: Issue and upload wildcard TLS certificate
  hosts: localhost
  vars:
    sld: "example"
    tld: "com"
    region: "eu-center"
    wildcard_domain: "*.{{ region }}.{{ sld }}.{{ tld }}"
    local_tmp: "/tmp/wildcard-{{ region }}"
    k8s_namespace: "ingress-nginx"
    k8s_secret_name: "wildcard-{{ region }}-tls"

  tasks:
    - name: Create certbot auth hook (creates the TXT record)
      copy:
        dest: "/tmp/certbot-auth-{{ region }}.sh"
        mode: "0755"
        content: |
          #!/bin/bash
          set -e
          namecheap-cli setone \
            --sld {{ sld }} --tld {{ tld }} \
            --type TXT --name "_acme-challenge.{{ region }}" \
            --address "${CERTBOT_VALIDATION}" --ttl 60
          # Wait for DNS to propagate
          for i in {1..30}; do
            val=$(dig TXT _acme-challenge.{{ region }}.{{ sld }}.{{ tld }} @1.1.1.1 +short | tr -d '"')
            [[ "$val" == "${CERTBOT_VALIDATION}" ]] && break
            sleep 10
          done
          sleep 30  # belt and suspenders

    - name: Issue wildcard certificate
      command: >
        certbot certonly --manual --preferred-challenges dns
        --manual-auth-hook /tmp/certbot-auth-{{ region }}.sh
        --manual-cleanup-hook /tmp/certbot-cleanup-{{ region }}.sh
        --agree-tos -m you@example.com
        --server https://acme-v02.api.letsencrypt.org/directory
        -d "{{ wildcard_domain }}"
        --work-dir {{ local_tmp }} --config-dir {{ local_tmp }}
        --logs-dir {{ local_tmp }} --non-interactive

    - name: Create or update TLS Secret
      kubernetes.core.k8s:
        state: present
        namespace: "{{ k8s_namespace }}"
        definition:
          apiVersion: v1
          kind: Secret
          metadata:
            name: "{{ k8s_secret_name }}"
          type: kubernetes.io/tls
          data:
            tls.crt: "{{ lookup('file', local_tmp + '/live/.../fullchain.pem') | b64encode }}"
            tls.key: "{{ lookup('file', local_tmp + '/live/.../privkey.pem') | b64encode }}"

Reference the secret in your Ingress

spec:
  tls:
    - hosts: ["*.region.example.com"]
      secretName: wildcard-region-tls

When does it run?

We run the playbook every 60 days. Let's Encrypt certs are valid for 90 days, so 60 leaves a 30-day buffer. A simple cron on a bastion host is enough — we do not even need to automate this. The cost of a manual run twice a quarter is lower than the cost of debugging a webhook.

"Unable to locate package 'appengine'" — a real gotcha we hit

If you copy this playbook and your certbot is from your distro's package manager, you may hit:

ImportError: cannot import name 'appengine' from 'urllib3.contrib'

This is a Python env collision. System certbot (often 1.21) wants old urllib3; you have a newer one in ~/.local/lib/python3.10/site-packages. The newer version dropped appengine.

Quick fix — add PYTHONNOUSERSITE: "1" to the certbot task's environment:

- name: Issue wildcard certificate
  environment:
    PYTHONNOUSERSITE: "1"
  command: >
    certbot certonly --manual ...

Long-term fix — install certbot via snap or pipx so it has its own Python env.

Should you do it this way?

Probably not. If your DNS provider has a stable cert-manager webhook (Cloudflare, Route53, DigitalOcean, Google Cloud DNS), use cert-manager for both per-app and wildcard certs. It is simpler and renews automatically.

The hybrid model only makes sense when:

Your DNS provider has no first-party or stable cert-manager support
You have one wildcard, not many
You would rather audit a 30-line shell script than a webhook deployment

For us those three are all true. For most teams, only the first might be — and even then, switching DNS provider is often easier than maintaining a webhook.

TL;DR

Per-app domains → cert-manager + HTTP-01 + ClusterIssuer. One annotation per Ingress, automatic renewals.
Wildcards → DNS-01 is mandatory. Use cert-manager with your DNS provider's webhook if it exists. Otherwise, a 60-day Ansible run with certbot --manual and a TLS Secret.
Two tools is fine. Don't force one model onto two different problems.

Want to skip TLS entirely?

Hostim.dev does this for you. Bring a Docker image or a git repo, get a cert and a domain.

Should Small Teams Even Bother with Kubernetes?

Pavel — Sun, 26 Apr 2026 10:43:15 +0000

Most small teams hit the same question at some point: should we move to Kubernetes? The honest answer for the majority of them is no, but that answer alone is not very helpful. So here is the longer version, with real prices and a clear line where the answer flips to yes.

What Kubernetes gives you

Underneath the marketing, Kubernetes is four practical things:

Declarative scheduling – you describe the desired state and a controller keeps the cluster in that state
Self-healing – crashed pods restart, dead nodes are drained, replicas come back automatically
Bin-packing – many workloads share the same nodes with CPU and memory limits
A standard API – Deployments, Services, Ingress, Jobs, Secrets, all the same on any cluster

These are real benefits. The catch is that you pay for them in money, time, or both.

What it actually costs

A realistic small-team setup looks like this: 3 services (API, worker, frontend), one Postgres, one Redis, around 50GB of storage. Here is what the same workload costs in three common shapes, all in eu-central-1 / Frankfurt.

Managed Kubernetes (AWS EKS)

EKS control plane, 0.10 USD per hour: ~67 €/mo
3× t3.medium nodes (2 vCPU / 4GB): ~92 €/mo
RDS Postgres db.t3.small, Single-AZ: ~27 €/mo
ElastiCache Redis cache.t3.micro: ~13 €/mo
ALB base plus LCU: ~15 €/mo
50GB EBS gp3 plus ~200GB egress: ~14 €/mo

Total: around 228 €/mo, before backups, observability, or any of your time.

GKE used to give you the first cluster for free. That is gone now: control plane is 0.10 USD per hour, and you get a 74.40 USD monthly billing-account credit that offsets one zonal cluster. Regional clusters pay full price.

Single Hetzner box + Docker Compose

AX42 dedicated (8-core Ryzen 7 PRO, 64GB DDR5, NVMe): from 57 €/mo (April 2026 pricing)
Postgres, Redis, app – all containers on the same machine, isolated by Compose
nginx and Let's Encrypt for HTTPS
Storage Box BX11 (1TB) for backups: ~4 €/mo

Total: around 61 €/mo. That box has enough headroom to run several more projects beside the main one.

Self-hosted Kubernetes (k3s on Hetzner Cloud)

3× CCX13 (2 dedicated vCPU / 8GB / 80GB SSD): ~48 €/mo
You run the control plane, etcd, ingress controller, cert-manager, backups and monitoring yourself

Compute is around 48 €/mo, but the real cost is the hours you put into the cluster every week.

The ops cost nobody prices in

Hosting is the cheap part. Kubernetes adds work that simply does not exist with Compose:

Cluster upgrades. A new minor lands every four months. If you skip a few, the upgrade path becomes painful.
Ingress and cert-manager. Works fine until cert-manager hits a CRD migration or your ingress controller deprecates an annotation you depend on.
CNI debugging. A misbehaving Calico or Cilium pod can take half a day to track down.
RBAC and ServiceAccounts. Required even for trivial things like letting one pod read one secret.
PVCs and storage classes. A reboot at the wrong moment can leave a volume stuck in Terminating and you reading the controller logs.
etcd. Quiet most of the time, then your cluster is suddenly read-only at 2am and you are restoring from a snapshot.

Realistic estimate: 2 to 5 hours a week of cluster maintenance for a self-hosted setup. Managed clusters cost less time but more money, as the table above shows. For a 3-person team, 2-5 hours a week is 5-12% of one engineer's time spent on infrastructure that does not ship features.

When Kubernetes is the right call

There are real cases where the cost is justified:

You run more than 20 services that need consistent deploys, secrets and networking
Multi-region or multi-tenant with hard isolation per customer
Compliance work (SOC 2, HIPAA) where audited RBAC and NetworkPolicies save weeks of paperwork
Your team already knows Kubernetes well and Compose would slow them down
Bursty workloads that genuinely benefit from horizontal autoscaling on shared nodes
You are building a platform where the Kubernetes API itself is the product (operators, CRDs)

If two or more of those apply, Kubernetes earns its keep. If none do, you are paying for capabilities you will not use.

When it is not

Most small teams have a workload that looks like this:

1 to 5 services
One Postgres, maybe a Redis
A single region
Fewer than 5 deploys a day

This fits comfortably on one Hetzner box with Docker Compose, or on a PaaS. No Kubernetes needed, much less money spent, and far less time on ops.

The "we will need it eventually" argument is mostly survivorship bias. Most projects never reach the scale where Kubernetes is actually the cheapest option, and migrating later is easier than people claim. A docker-compose.yml maps almost line-for-line to Deployments and Services when the day comes.

Quick decision table

Situation	Use
Solo dev, 1-3 services	Docker Compose on a VPS
Small team, up to ~10 services, one region	PaaS or Compose + Ansible
Multi-tenant SaaS with isolation needs	Kubernetes (managed)
Compliance-heavy, audited infrastructure	Kubernetes (managed)
Building a platform or operator	Kubernetes
"Everyone else uses it"	Not a real reason

The middle ground

A PaaS exists exactly for this gap. You get the useful parts of Kubernetes – self-healing, declarative deploys, automatic HTTPS, isolated namespaces – without running the cluster yourself.

Hostim.dev runs Kubernetes underneath, on bare metal in Germany, so you do not have to. You paste a docker-compose.yml and get a deployed app with HTTPS, Postgres, Redis, volumes, metrics and logs.

The same stack priced on Hostim:

3× shared App (2 vCPU / 2GB): 13.50 €
Postgres (10GB): 10 €
Redis (2.5GB): 5 €
50GB volume: 10 €

Total: 38.50 €/mo, with HTTPS, metrics, logs and backups included.

If you actually need Kubernetes, run Kubernetes. If you are reaching for it because it is the default answer, a PaaS or a single Hetzner box will probably serve you better, for less money and less weekend work.

👉 Try Hostim.dev

Which Database Should You Self-Host? SQLite vs MySQL vs PostgreSQL vs Redis

Pavel — Wed, 08 Apr 2026 16:38:44 +0000

When you're deploying your own app, the database choice matters more than most people think. It affects performance, ops complexity, backups, and how much memory your server needs.

There are four options you'll run into most often: SQLite, MySQL, PostgreSQL, and Redis. They're not all the same kind of database – and that's the point. Here's when each one makes sense.

SQLite – the zero-ops embedded database

Best for: small apps, prototypes, CLIs, single-user tools, edge deployments
Strengths: no server process, single file, zero config, instant setup
Weaknesses: no concurrent writes, no replication, hard to scale past one instance

SQLite is not a server – it's a library that reads and writes a single file. That makes it perfect for apps where simplicity matters more than scale. If your app has one process writing to the database and modest traffic, SQLite will outperform anything else because there's no network round-trip. The moment you need concurrent writes or multiple app replicas, you've outgrown it.

MySQL – the reliable workhorse

Best for: web apps, CMS platforms, CRUD-heavy workloads, WordPress/Laravel stacks
Strengths: fast reads, mature replication, huge ecosystem, low memory footprint
Weaknesses: weaker JSON support, less strict by default, fewer advanced types

MySQL powers a massive chunk of the internet. It's battle-tested, well-documented, and runs well even on small VPS instances. If you're running a standard web app with mostly reads and simple queries, MySQL will serve you well without hogging resources. Just be aware that its default configs are more lenient than PostgreSQL – silent truncations and implicit type casts can bite you.

PostgreSQL – the feature-rich powerhouse

Best for: complex queries, data integrity, JSON workloads, GIS, analytics
Strengths: advanced types (JSONB, arrays, hstore), strong standards compliance, extensions ecosystem
Weaknesses: higher memory usage, more tuning needed, steeper learning curve for ops

PostgreSQL is the database you pick when correctness and flexibility matter. It handles complex joins, window functions, CTEs, and full-text search natively. The extension ecosystem (PostGIS, pg_cron, pgvector) makes it a Swiss army knife. The trade-off: it's hungrier on resources and rewards careful tuning of shared_buffers, work_mem, and connection pooling.

Redis – the in-memory speed layer

Best for: caching, sessions, rate limiting, queues, pub/sub, leaderboards
Strengths: sub-millisecond reads, rich data structures (lists, sets, sorted sets, streams), built-in TTL
Weaknesses: data must fit in RAM, persistence is optional and lossy, not a primary data store

Redis isn't a replacement for a relational database – it's a complement. Use it for things that need to be fast and can tolerate occasional data loss: session tokens, cache layers, job queues. Redis Streams can even replace simple message brokers. Just don't store your source of truth here – if the server restarts between RDB snapshots, recent writes are gone.

Quick comparison

Feature	SQLite	MySQL	PostgreSQL	Redis
Type	Embedded	Relational server	Relational server	In-memory store
Ease of setup	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Concurrent writes	❌ Single-writer	✅ Good	✅ Excellent	✅ Very fast
Complex queries	Basic	Good	Excellent	N/A (key-value)
Memory usage	Minimal	Low–moderate	Moderate–high	High (all data in RAM)
Replication	None built-in	Mature	Mature	Built-in
Best self-host size	Single instance	Small–large	Medium–large	Any (as cache layer)
Persistence	Always (file)	Always (disk)	Always (disk)	Optional (RDB/AOF)

So which one should you choose?

Building a prototype or CLI tool? → SQLite
Running a standard web app? → MySQL
Need complex queries, JSONB, or extensions? → PostgreSQL
Need a fast cache, session store, or queue? → Redis

Most real-world apps end up using two: a relational database (MySQL or PostgreSQL) for your data, and Redis for caching and sessions. That's not overkill – it's the right tool for each job.

Self-hosting these databases

Running databases on a VPS means managing backups, updates, and disk space yourself. It's doable, but it's one more thing to maintain.

On Hostim.dev, MySQL, PostgreSQL, and Redis are built in – provisioned alongside your app with metrics and no extra config. Paste a docker-compose.yml and your database is ready.

Bastion Host & GitHub Actions on Hostim.dev

Pavel — Thu, 08 Jan 2026 19:00:09 +0000

I haven't posted updates for a while, but several core features landed on Hostim.dev recently.

Instead of shipping from a fixed roadmap, I'm following support-driven (customer-driven) development: features move to the top of the queue once users actively need them.

Over the past month, this resulted in three practical additions around Docker CI/CD, GitHub Actions deployment, and secure bastion host access.

GitHub Actions deploy for Docker apps

Hostim.dev now supports GitHub Actions deployments out of the box.

You can trigger a deploy directly from GitHub Actions using a simple API call. This works well for common Docker CI/CD setups:

Build and deploy on merge to main
Restart an app after pushing a new Docker image
Manual deploys via workflow_dispatch

There's no OAuth and no hidden logic. Your workflow controls everything — branches, conditions, environments. Hostim only executes the requested action.

This is especially useful if you already run CI/CD with Docker and just want a clean deployment target.

Bastion host for secure shell access to containers

Each project now includes a built-in SSH bastion host.

If you're unfamiliar: a bastion host is a hardened entry point used to access private infrastructure without exposing services to the public internet.

On Hostim.dev, the bastion host allows you to open a shell into running apps:

shell my-app

This answers common questions like:

What is a bastion host used for?
How do I securely SSH into containers?
How can I debug a production Docker app without public access?

Typical use cases:

Debugging production issues
Running database migrations
Inspecting environment variables
Accessing internal services safely

The bastion host is private, key-based, and isolated per project.

Custom commands for Docker apps

Apps can now override the container command.

This enables common Docker patterns such as:

One image, multiple roles (web + worker)
Background jobs using the same Docker image
CI/CD pipelines that reuse images across environments

This pairs naturally with Docker CI/CD pipelines, where images are built once and reused consistently.

Why this approach

Many platforms ship features based on assumptions.

Instead, these changes came directly from:

"How do I deploy with GitHub Actions?"
"How do I get shell access without exposing ports?"
"How do I run workers with the same image?"

Support questions shape the roadmap.

What's next

More items are planned, but user feedback decides the order.

If something feels missing, it's probably already on the list.

👉 https://hostim.dev

A Better Umami Dashboard with Grafana

Pavel — Thu, 20 Nov 2025 17:24:38 +0000

Umami is great. Lightweight, privacy-friendly, no cookies, no tracking drama.
We use it ourselves on Hostim.dev, and we ship a one-click Umami template for anyone who wants simple, privacy-focused analytics.

But once you start relying on analytics to make actual decisions, you hit the limits pretty quickly.

Why the default Umami dashboard wasn't enough

Umami intentionally keeps things minimal, but some gaps become obvious:

No clear view of when visitors peak during the day
Hard to isolate bots from real traffic
No moving averages or trend smoothing
No grouped referrers (e.g. "search", "LLM", "other")
Limited visibility into relationships between sessions and custom events

None of this is criticism — Umami is intentionally simple.
But sometimes you want more resolution.

So I took the quickest path:

Deploy Grafana → connect it to Umami's PostgreSQL → build a custom dashboard.

This took maybe ten minutes and unlocked:

Daily heatmap showing real traffic peaks
7-day moving averages for referrers
Qualified sessions (≥2 pageviews) to filter out most bots
Selectable custom events
Raw stats for the selected period

Suddenly Umami became "actionable" instead of just "nice".

How to connect Grafana to Umami's PostgreSQL

Inside Grafana:

Configuration → Data sources → Add data source → PostgreSQL

Fill in the credentials from your Umami database and save.

You can now import our dashboard:

👉 Grafana.com Dashboard

Try it yourself

If you prefer to self-host on your own VPS, here is a complete Docker Compose stack for Umami, PostgreSQL, and Grafana.

Full Docker Compose stack

services:
  postgres:
    image: postgres:15
    restart: always
    environment:
      POSTGRES_USER: umami
      POSTGRES_PASSWORD: umami_pass
      POSTGRES_DB: umami
    volumes:
      - postgres_data:/var/lib/postgresql/data

  umami:
    image: ghcr.io/umami-software/umami:postgres-latest
    restart: always
    depends_on:
      - postgres
    environment:
      DATABASE_URL: postgres://umami:umami_pass@postgres:5432/umami
      DATABASE_TYPE: postgresql
      APP_SECRET: "replace_this_with_a_random_secret"
    ports:
      - "127.0.0.1:3000:3000"

  grafana:
    image: grafana/grafana:latest
    restart: always
    depends_on:
      - postgres
    environment: GF_SERVER_DOMAIN=grafana.example.com
      GF_SERVER_ROOT_URL=https://grafana.example.com
    ports:
      - "127.0.0.1:3001:3000"
    volumes:
      - grafana_data:/var/lib/grafana

volumes:
  postgres_data:
  grafana_data:

Start everything:

docker compose up -d

Then:

Umami → http://localhost:3000
Grafana → http://localhost:3001

In Grafana, configure a PostgreSQL data source:

Host: postgres
Port: 5432
User: umami
Password: umami_pass
Database: umami

Import the dashboard using the ID from Grafana.com.

Don't want to run a server?

If you don't want to manage Docker, OS maintenance, or networking, you can deploy the same stack on Hostim.dev by simply pasting the Compose file above when creating a new project.

Choose Paste Docker Compose
Use the YAML from this section

Hostim.dev will handle HTTPS, internal networking, logs, metrics, and persistence for all three services.

👉 Try Hostim.dev — deploy the full stack without touching SSH

Already running Umami on Hostim.dev?

If you deployed Umami using the one-click template, you don't need a new project. Just:

Create a separate Grafana App in the same project
Use the grafana/grafana:latest image
Add the existing Umami PostgreSQL as a Grafana data source
Import the dashboard JSON

Both apps run on the same private project network, so they can communicate without exposing ports or adjusting firewall rules.

MetalLB on Hetzner Dedicated with vSwitch

Pavel — Tue, 28 Oct 2025 17:00:41 +0000

When running Kubernetes on Hetzner Dedicated, there is no cloud load balancer. But you can provide public LoadBalancer IPs by attaching a routed IP range to a vSwitch and letting MetalLB announce addresses over L2.

Our setup:

Calico (VXLAN + WireGuard)
kube-proxy IPVS with strictARP
ingress-nginx for ingress traffic

1. Assign a public subnet to your vSwitch

Example routed block Hetzner provides:

Subnet:     123.45.67.32/29
Gateway:    123.45.67.33
Usable:     123.45.67.34–38
Broadcast:  123.45.67.39

Attach your dedicated servers to the vSwitch (VLAN ID e.g. 4000).

2. Configure vSwitch VLAN on each node

Each node gets a /32 from the subnet – Hetzner routes the whole /29 to your server.

Important note on routing table IDs

This guide uses routing table 200 as an example.

If you are running Cilium, avoid table 200: Cilium currently flushes all routes in table 200 on startup, which breaks vSwitch routing.

For Cilium-based installations, any other unused routing table ID works (for example 201, 300, or 1001).

Reference: https://github.com/cilium/cilium/issues/38531
Create /etc/netplan/10-vlan-4000.yaml:

network:
  version: 2
  renderer: networkd
  vlans:
    vlan4000:
      id: 4000
      link: eno1
      mtu: 1400
      addresses:
        - 123.45.67.38/32 # node-specific
      routes:
        - to: 0.0.0.0/0
          via: 123.45.67.33
          on-link: true
          table: 200
        - to: 123.45.67.32/29
          scope: link
          table: 200
      routing-policy:
        - from: 123.45.67.32/29
          table: 200
          priority: 10
        - to: 123.45.67.32/29
          table: 200
          priority: 10
        - from: 123.45.67.32/29
          to: 10.233.0.0/18
          table: 254
          priority: 0
        - from: 123.45.67.32/29
          to: 10.233.64.0/18
          table: 254
          priority: 0

Apply:

netplan apply

3. Required sysctl settings

Create /etc/sysctl.d/999-metallb.conf:

net.ipv4.conf.all.arp_ignore=1
net.ipv4.conf.all.arp_announce=2
net.ipv4.conf.all.rp_filter=0
net.ipv4.conf.default.rp_filter=0

Why:

Setting	Purpose
`arp_ignore=1`	Only reply to ARP queries for an IP on the correct interface – prevents conflicting replies from Calico/VXLAN.
`arp_announce=2`	Send ARP only from the interface that owns the VIP, required when MetalLB moves VIPs between nodes.
`rp_filter=0`	Disable strict reverse-path filtering – otherwise nodes drop return traffic sourced from VIPs or remote pods.

4. kube-proxy + Calico adjustments

Enable strictARP in kube-proxy (IPVS mode):

apiVersion: kubeproxy.config.k8s.io/v1alpha1
kind: KubeProxyConfiguration
ipvs:
  strictARP: true

MTU must account for VXLAN + vSwitch + WireGuard overhead:

Calico MTU: 1280 (consistent across nodes)

5. Deploy MetalLB

apiVersion: metallb.io/v1beta1
kind: IPAddressPool
metadata:
  name: vswitch
  namespace: metallb-system
spec:
  addresses:
    - 123.45.67.34-123.45.67.36 # free VIPs
---
apiVersion: metallb.io/v1beta1
kind: L2Advertisement
metadata:
  name: l2
  namespace: metallb-system
spec:
  ipAddressPools: ["vswitch"]
  interfaces: ["vlan4000"]

Restart MetalLB speakers to pick up interface binding.

6. Ingress service configuration

For ingress-nginx:

spec:
  externalTrafficPolicy: Local

Pro:

Preserves client IP
Prevents traffic hairpin across nodes

Tradeoff:

Only one node handles a given connection (acceptable for ingress)

7. Verification

Confirm that your ingress-nginx Service received a public VIP:

kubectl get svc -n ingress-nginx ingress-nginx-controller

Expected example:

NAME                       TYPE           CLUSTER-IP      EXTERNAL-IP    PORT(S)                      AGE
ingress-nginx-controller   LoadBalancer   10.233.53.156   123.45.67.35   80:30440/TCP,443:30477/TCP   17d

Inspect the Service events to see which node currently advertises the VIP:

kubectl describe svc -n ingress-nginx ingress-nginx-controller

Look for:

Events:
  Normal  nodeAssigned  ...  metallb-speaker  announcing from node "control-plane-1" with protocol "layer2"

Then verify reachability from outside:

curl -I http://123.45.67.35

Failover test

Identify the active announcer from the above events
Shut that node down abruptly:

sudo poweroff

Re-run:

curl -I http://123.45.67.35

Expected: traffic continues within ~1–2 seconds as another node picks up the VIP.

➡️ Note: VIPs do not appear in ip addr on nodes; they are held in IPVS and advertised via ARP. That is normal.

Acknowledgment

Thanks to Oleksandr Vorona (DevOps at Dysnix) for reporting a Cilium routing table conflict and helping improve this guide.

https://dysnix.com

Wrapping up

This gives:

Public LoadBalancer IPs
Fast failover (~1-2s)
Clean separation: pod networking via VXLAN/WireGuard, external via vSwitch

Alternative approaches:

Hetzner Cloud Load Balancer (simpler, works with Dedicated too)
Cilium with L2 announcements

We run hosting infrastructure, so controlling ingress networking ourselves matters (mostly to prove the point really). Hetzner still runs the vSwitch underneath, but it's more independent than relying on the cloud LB.

And if you'd rather not handle any of this yourself – Hostim.dev is now live. You can deploy your Docker or Compose apps with built-in databases, volumes, HTTPS, and logs – all in one place, ready in minutes.

How to Self-Host n8n with Docker Compose

Pavel — Sat, 04 Oct 2025 12:23:08 +0000

n8n is a popular open-source automation tool – like Zapier, but self-hosted.
Here's how to run it on your own VPS using Docker Compose, and expose it securely over HTTPS using Caddy as the ingress proxy.

If you want a broader primer, check out How to Self-Host a Docker Compose App. But you don't need to read it first – this guide is self-contained.

1. Install Docker on your VPS

Update the system and install Docker + Compose plugin:

apt update && apt upgrade -y
apt-get install ca-certificates curl -y
install -m 0755 -d /etc/apt/keyrings
curl -fsSL https://download.docker.com/linux/ubuntu/gpg -o /etc/apt/keyrings/docker.asc
chmod a+r /etc/apt/keyrings/docker.asc

echo \
  "deb [arch=$(dpkg --print-architecture) signed-by=/etc/apt/keyrings/docker.asc] https://download.docker.com/linux/ubuntu \
  $(. /etc/os-release && echo "${UBUNTU_CODENAME:-$VERSION_CODENAME}") stable" \
  | tee /etc/apt/sources.list.d/docker.list > /dev/null

apt-get update
apt-get install docker-ce docker-ce-cli containerd.io docker-compose-plugin -y

2. Write a Docker Compose file

Create a new directory for n8n and add docker-compose.yml:

services:
  n8n:
    image: n8nio/n8n:latest
    restart: always
    ports:
      - "127.0.0.1:5678:5678"
    environment:
      - N8N_HOST=n8n.example.com
      - N8N_PORT=5678
      - N8N_PROTOCOL=https
    volumes:
      - n8n_data:/home/node/.n8n

volumes:
  n8n_data:

💡 Note: This guide assumes you already have a domain (like n8n.example.com) pointing to your VPS's IP address. If not, set that up with your DNS provider before continuing.

Notice we bind to 127.0.0.1:5678 – so it's only accessible locally. Caddy will handle public access.

Start it:

docker compose up -d

3. Make it survive reboots

Create a systemd service:

# /etc/systemd/system/n8n.service
[Unit]
Description=n8n workflow automation (Docker Compose)
After=network.target

[Service]
Type=oneshot
WorkingDirectory=/root/n8n
ExecStart=/usr/bin/docker compose up -d
ExecStop=/usr/bin/docker compose down
RemainAfterExit=yes

[Install]
WantedBy=multi-user.target

Enable it:

systemctl enable n8n
systemctl start n8n

Now n8n restarts automatically after reboots.

4. Install and configure Caddy

Caddy is a modern reverse proxy with automatic HTTPS. Perfect for small setups.

If you're curious about how Caddy compares to Nginx, HAProxy, or Traefik, see The Reverse Proxy Showdown. For n8n, Caddy is the simplest choice.

Make sure your domain (e.g. n8n.example.com) already resolves to your VPS before you configure Caddy. Otherwise, Let's Encrypt won't be able to issue a certificate.

apt install -y debian-keyring debian-archive-keyring apt-transport-https
curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | tee /etc/apt/trusted.gpg.d/caddy-stable.asc
curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | tee /etc/apt/sources.list.d/caddy-stable.list
apt update
apt install caddy -y

Edit the Caddyfile:

nano /etc/caddy/Caddyfile

Add:

n8n.example.com {
    reverse_proxy localhost:5678
}

Reload Caddy:

systemctl reload caddy

That's it – Caddy requests and renews Let's Encrypt certificates automatically.

5. Secure access

Use strong credentials when creating first user.
Keep the docker-compose.yml volume so your workflows persist.
Optionally, restrict access to your IP range using Caddy if it's just for personal use.

Wrapping Up

You now have a self-hosted n8n instance:

Running in Docker Compose
Restarting automatically after reboots
Exposed via Caddy with HTTPS

If you want to avoid managing servers altogether, platforms like Hostim.dev let you paste a Compose file and get HTTPS, metrics, and persistence without touching SSH.

But if you prefer DIY – this setup will take you far.

The Reverse Proxy Showdown: Nginx vs HAProxy vs Caddy vs Traefik

Pavel — Tue, 16 Sep 2025 13:26:48 +0000

Reverse proxies are the unsung heroes of modern infrastructure. They terminate TLS, route traffic, balance loads, and keep your apps reachable. But which one should you choose?
There are four popular options worth comparing head-to-head: Nginx, HAProxy, Caddy, and Traefik. Each comes with its own strengths, trade-offs, and ideal use cases.

Nginx – the classic all-rounder

Best for: general-purpose web serving, static content, simple reverse proxy setups
Strengths: battle-tested, massive ecosystem, tons of tutorials, easy Certbot integration
Weaknesses: verbose configs, not as dynamic as newer tools

Nginx is often the default choice. It's powerful, stable, and widely documented. If you're setting up a straightforward proxy or serving static files alongside your app, Nginx will feel familiar and reliable. Just be prepared to manage slightly more configuration boilerplate.

👉 Full Nginx reverse proxy guide →

HAProxy – the performance beast

Best for: high-traffic sites, low-latency routing, advanced load balancing
Strengths: blazing fast, robust observability, flexible ACL system
Weaknesses: steeper learning curve, TLS setup can be fiddly

HAProxy is famous for performance. It's a favorite in environments where uptime and throughput matter most. Think enterprise setups or any case where you need fine-grained control over routing logic and health checks. It's less beginner-friendly, but extremely powerful once mastered.

👉 Full HAProxy reverse proxy guide →

Caddy – the modern “batteries included” choice

Best for: minimal config, automatic HTTPS, developer-friendly defaults
Strengths: one-line proxy configs, TLS handled automatically, sane defaults
Weaknesses: smaller ecosystem, fewer advanced knobs for complex routing

Caddy made waves by taking the pain out of HTTPS. With a simple Caddyfile, you get automatic TLS, redirects, and reverse proxying. It's ideal for small projects or developers who want secure, working defaults without fiddling with extra tooling.

👉 Full Caddy reverse proxy guide →

Traefik – the container-native router

Best for: Docker and Kubernetes workloads, dynamic environments
Strengths: integrates with container labels, dynamic service discovery, built-in metrics
Weaknesses: YAML configs can get verbose, less popular outside containerized setups

Traefik was built with cloud-native apps in mind. Instead of editing config files, you annotate containers with labels and Traefik routes traffic automatically. It shines in environments where services come and go frequently, making it a natural fit for orchestrators like Kubernetes.

👉 Full Traefik reverse proxy guide →

Quick comparison

Looking for full setup walkthroughs? Check out our guides for Nginx, HAProxy, Caddy, and Traefik.

Feature	Nginx	HAProxy	Caddy	Traefik
Ease of setup	⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐
Performance	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Auto HTTPS	Needs Certbot	Manual + hooks	Built-in	Built-in
Container native	No	No	Somewhat	Yes
Ecosystem/docs	Huge	Mature ops-focused	Growing dev-focused	Strong in Docker/K8s space

So which one should you choose?

Just learning or running a blog? → Nginx
Handling big traffic or need reliability? → HAProxy
Want HTTPS with zero config? → Caddy
Running Docker/Kubernetes? → Traefik

Which reverse proxy do you use in your projects and why?

Netlify's New Credit Pricing: When Cloud Rent Comes Due

Pavel — Mon, 08 Sep 2025 08:33:10 +0000

📌 Last week, I wrote about Cloud Rent in Action – how layers of middlemen drive up the cost of running a simple SaaS stack. Netlify's new pricing update feels like the same story, playing out live.

What Changed at Netlify

Netlify just rolled out a credit-based pricing model.

New accounts are now required to buy credits.
Every deploy, function, or gigabyte of bandwidth consumes those credits.
When the credits run out, your projects pause until you top up.
Legacy users can stay on old plans for now, but the future is clear: credits are the new normal.

On paper, this looks like a simplification. In reality, it's the next stage of cloud rent.

Why Netlify Had to Change

For years, companies like Netlify grew fast thanks to venture capital money. Investors subsidized growth: cheap plans, generous free tiers, and aggressive marketing. The mission was simple – capture the market at any cost.

That was the market expansion phase.

VCs were happy to foot the bill as long as user numbers climbed.

Now we're in the market exploration (or sustainability) phase. Investors want returns. And that means:

Free tiers shrink
Simple flat plans get replaced with credit systems
Costs shift from VC wallets to developer wallets

It's not that Netlify suddenly became greedy – it's that the VC playbook always ends this way. Rent has to be collected. And developers end up paying it.

The Problem With Credit Pricing

Credits sound neat – one bucket, one metric. But for most developers, they create more problems than they solve:

Mental overhead – you're forced to budget not just money, but deploys and requests.
Unpredictable bills – a sudden spike in traffic can drain credits overnight.
Complexity creep – hosting a static site shouldn't require a calculator.

This is exactly the dynamic I wrote about in my Cloud Rent post: when platforms optimize for investor returns instead of developer trust, pricing drifts away from simplicity and fairness.

Why Hostim.dev Is Different

I'm building Hostim.dev with a completely different philosophy:

Bootstrapped, not VC-funded. No investors. No pressure to flip pricing later. No "growth at all costs" phase.
Lean team. Right now it's just me – the founder – building and running the platform. That means lower overhead and no bloated payroll to pass on to you.
Fair pricing from the beginning. Plans are simple, predictable, and surge-safe. No credits, no hidden meters, no surprise bills.
Built for developers, not investors. The focus is on usability and transparency. You don't need to rewire your workflow to fit a platform's billing quirks.

Cloud Rent vs. Developer Trust

So if last week's post was the theory, this week is the proof:

Cloud rent always comes due. Netlify's credits are just the latest example.

At Hostim.dev, I'm building the opposite:

Flat, predictable plans
No surprise charges
Databases, volumes, and apps as first-class citizens
A platform you can trust, built for developers, not for VCs

💬 Curious to hear your thoughts:

Do you prefer credit-based pricing models (like Netlify's) or flat, predictable plans?

👉 You can check out Hostim.dev if you're interested – your first project is free for 5 days, no credit card required.