VPS Providers: I Ran 2 in Production. Only One Cost $6.

Search “VPS providers” and you get listicles ranking fifteen hosts. Nobody ran fifteen hosts. They ran zero and copied a pricing page.

I ran two, in production, with real money moving through them: DigitalOcean and QuantVPS. Railway is in this post too, but on published price and workload shape only, because I have not run it and I am not going to write a sentence that implies I did. That distinction is the whole point of the post.

Which VPS providers did I actually run?

Two, across the life of a Polymarket trading bot and the monitoring that still surrounds it.

Provider	Did I run it?	Monthly cost	The catch
DigitalOcean	Yes. Four months of the live bot, plus monitors, dashboards and cron jobs to this day	$6/mo flat	Fewer regions than AWS. You manage your own server.
QuantVPS	Yes. Runs the live bot now, Amsterdam instance	Not quoted here (see below)	Premium pricing for one specific property: latency.
Railway	No. Price and shape only	$5 + usage	Usage-based pricing sounds cheap until your bot runs 24/7. A busy month can cost $20-40.

Two machines and one honest exclusion. Below the table I also name every provider I have never touched, because a comparison that hides its scope is not a comparison.

What does $6/month actually buy on DigitalOcean?

The entry Droplet gives you 1 vCPU, 1GB RAM, 25GB SSD, and 1TB transfer.

Here is what my trading bot did with it. An asyncio event loop holding multiple WebSocket connections and placing real-time orders used about 200MB of that 1GB. The CPU barely touched 5% between trading signals. I went from zero to a running bot in 28 minutes, and over the next four months it placed 23 real trades at a 69.6% win rate. The infrastructure never failed me. The server was not the bottleneck. It never was.

That is the number that should reframe this whole category for you. The workload most people are shopping a VPS for is a single process that sits mostly idle waiting for an event, then does a small burst of work. It does not need auto-scaling, because it is one process. It does not need a load balancer, because it is not serving HTTP traffic. It does not need a managed runtime, because you want control rather than guardrails.

The catch is real and I will name it: DigitalOcean has fewer regions than AWS, and you manage your own server. There is no dyno restarting for you. I skipped the SSH lockdown step on my first Droplet and found 3,000 brute-force login attempts in the auth log a week later. Nothing was compromised, because key-based auth held, but that is the class of chore you are taking on. It is also exactly the chore Railway charges you to avoid.

If you want the full walkthrough rather than the comparison, I wrote the step-by-step at deploy a Python agent on DigitalOcean.

Is Railway cheaper than DigitalOcean?

Railway’s headline is $5 plus metered usage, which reads as a dollar cheaper. For an always-on process it is not.

Metered pricing bills you for the hours your process is awake. A trading bot, a Discord bot, a scraper on a schedule, a queue worker: these are awake every hour of every day by definition. Usage-based pricing sounds cheap until your bot runs 24/7. A busy month can cost $20-40. That is three to seven times the flat Droplet, for the same single process.

What Railway sells against that is developer experience. You push to git and it deploys. There is no apt update, no systemd unit, no firewall rule you forgot, and its persistent server processes avoid the cold starts that make serverless awkward for stateful Node apps. That is a real argument, and for a bursty workload I would take it seriously.

I want to be precise about what this section is: a pricing and shape comparison, not a field report. I have not run a production workload on Railway. Everything above comes from published pricing and from the structural fact that metered billing and a 24/7 duty cycle are a bad fit for each other. If you want someone’s scars from operating Railway at scale, I am not that person.

When does a specialized VPS like QuantVPS make sense?

Only when you have measured that latency is costing you money. That threshold is higher than the marketing implies, and my own numbers are the reason I can say so.

My DigitalOcean Droplet was in Amsterdam, and it already delivered 5-12ms round-trip to Polymarket’s CLOB in London. That is not slow. It carried the bot for four months. The $6 box was never the thing standing between me and a good fill.

What changed was the strategy, not the server. When the approach shifted to one that needed 3-5ms consistency rather than a 5-12ms range, I moved the live bot to a QuantVPS instance in Amsterdam. The difference in fill quality against testing from my Mac in San Francisco was measurable. That is the entire justification: my own fill data, on my own orders, not a benchmark from a review site.

I am deliberately not quoting a QuantVPS price here. I do not have a current figure I can stand behind, plans change, and a number I cannot verify is worse than no number. I will also flag the obvious gap in my own account: I have no operational complaint to report about QuantVPS, which is partly a good sign and partly just a shorter track record than I have with the Droplet. Treat the absence of a downside as thin evidence, not a clean bill of health.

Almost everyone reading this belongs on the left column. I did, for four months.

What I actually run today

Both, split by job.

QuantVPS hosts the live bot: Amsterdam instance, sub-10ms to the venues this strategy needs. The $6 DigitalOcean Droplet runs everything that is not latency-sensitive, which is monitors, dashboards, and cron jobs. That split is the real recommendation buried in this whole comparison. You are probably not choosing one VPS provider. You are choosing which of your processes deserve to pay for latency, and the answer is usually “one of them, eventually.”

My honest advice: start on the Droplet, and pay for a specialized VPS only when your fill data proves latency is costing you money. I used both before either paid me a cent. The full build is at how I built a Polymarket trading bot, and the engineering discipline around it is at what shipping a production trading bot taught me.

Which VPS providers did I not test?

This is the section the listicles do not write.

I have not run Railway, Hetzner, Linode, Vultr, Contabo, OVH, Fly.io, Render, or AWS Lightsail in production. Railway gets a pricing comparison above because its metered model is the specific trap that catches always-on workloads, and that argument stands on published pricing rather than on my experience. The rest get nothing, because I have no evidence to rank them with, and inventing an opinion about a machine I never SSH’d into is exactly the thing that makes provider comparisons useless.

If your requirement is “cheapest possible always-on Linux box in Europe,” go read someone who actually ran Hetzner. If your requirement is “a flat-rate box I can have a Python agent running on this afternoon,” that is a question I can answer from receipts.

How do you pick, in one paragraph?

Count your duty cycle first. If the process runs around the clock, take the flat rate and accept that you manage the server. If it idles most of the month, metered billing plus a git-push workflow is worth the premium. If you are in a domain where microseconds convert directly into money, buy the specialized box, but buy it after your own data says so, not before. And whatever you pick, start smaller than you think you need. My bot used 200MB of a gigabyte and 5% of a core, and I would have happily overpaid for eight times that if I had shopped by anxiety instead of by measurement.