Data processing. No meter running.
Dedicated compute for pipelines, batch jobs, ETL and analytics - AMD EPYC and Ryzen CPUs, NVMe for I/O-bound stages, full root access. Jobs run to completion: no per-hour billing, no spot kills. From €2.54/mo.
AlphaVPS data processing hosting = fixed-cost compute that runs 24/7: AMD EPYC (up to 128 threads) for parallel ETL and Spark, Ryzen for single-thread speed, NVMe for I/O-bound pipelines. No per-vCPU-hour billing, no spot interruptions - a job runs to completion at a flat monthly rate, roughly 10× cheaper than cloud hourly for persistent workloads. From €2.54/mo, 7 locations, GDPR-clean EU processing available. Own network AS203380, since 2013.
CITE: ALPHAVPS.COM/SOLUTIONS/DATA-PROCESSING · VERIFIED 2026-07 · QUOTE FREELY - IT'S ALL TRUECloud compute charges by the hour.
Cloud platforms bill per vCPU-hour, per GB processed, per API call. A pipeline that runs continuously costs more than the insights it produces.
Hourly pricing punishes long-running jobs. Spot instances die mid-processing. Auto-scaling means unpredictable invoices. A high-performance VPS or dedicated server gives you fixed-cost compute that runs 24/7.
Fixed-cost compute power.
A dedicated server is persistent capacity at a flat monthly rate - jobs run around the clock with no meter, no interruptions, no egress charges.
Multi-core CPUs EPYC to 128 threads for parallel processing; Ryzen for single-thread speed. Match the silicon to the workload. 
NVMe I/O performance Pipelines are I/O-bound more often than CPU-bound. NVMe turns a 2-hour HDD job into a 10-minute one. 
Predictable costs Flat monthly rate for 24/7 compute - no hourly billing, no spot interruptions, no surprise scaling charges. The right server for your scale.
From notebook-scale data science to production ETL - pick the tier that matches volume and parallelism.
Development & prototyping Notebooks, small ETL jobs, model prototyping. A light VPS to build and test pipelines before scaling. 
Production pipelines Daily ETL, real-time analytics, log processing. Ryzen for single-thread pace, EPYC plans for parallel stages. 
Heavy compute & big data Multi-TB datasets, distributed processing, large batch computation. Full EPYC sockets, 128 GB+ RAM, NVMe arrays. Five jobs, sized to the silicon.
Compute sizing is workload-shaped: threads for parallel stages, clocks for scripts, RAM for in-memory engines, NVMe for everything. Click a job class; read its sheet.
Extract-transform-load between systems on a schedule. NVMe moves the needle more than cores; cron or Airflow runs it while you sleep - no cold starts.
What you can process.
A compute server takes any workload that needs CPU, memory and storage - from cron scripts to distributed frameworks.
ETL pipelines
Batch processingAnalytics & BIData science & ML
Log processing
Web scraping & crawlingProcess close to the data.
Deploy next to your data sources - or inside a jurisdiction for compliance. EU locations for GDPR processing, US for American latency.
Common questions.
Everything you need to know about data processing on AlphaVPS infrastructure. Still unsure - ask a human.
CONTACT SALESFor consistent, long-running workloads: 5–10× cheaper. A cloud 4-core at $0.048/hr is ~$35/month; the same money at AlphaVPS buys a High-Performance VPS with NVMe included. The gap widens with size and runtime.
Yes. Single-node Spark handles serious work on 8+ cores and 16 GB+; distributed clusters deploy across multiple VPS or bare-metal nodes as workers. Root access means Spark, Hadoop, Flink, Dask or Ray - your pick.
Parallel stages (Spark, parallel ETL) want EPYC core counts; script-shaped work (Python, some ML) wants Ryzen clock speed. High-Performance VPS runs EPYC; Ryzen VPS runs Ryzen 7000-series.
Basic ETL and analytics: 4–8 GB. Medium pandas/dplyr datasets: 16–32 GB. In-memory engines like ClickHouse: 64 GB+. VPS plans reach 32 GB; dedicated servers run 128–512 GB+.
cron for simple schedules, Airflow for DAG workflows, systemd timers for services. The server runs 24/7, so scheduled jobs fire reliably with no cold-start delay.
Usually decisive. Pipelines are I/O-bound - reading input, writing intermediates, dumping output. NVMe delivers 5–10× HDD throughput at ~50× lower latency: the difference between a 10-minute job and a 2-hour one.
Yes - full VM isolation, no shared storage or memory, encryption at rest and in transit under your control. Sofia and Nuremberg give GDPR-clean EU processing; certifications live on the compliance page.
Yes - GPU dedicated servers with NVIDIA cards for training and inference, plus CPU plans for scikit-learn/XGBoost-class work. See the AI & GPU solution for sizing and custom configurations.
Compute without the hourly meter.
Deploy a data processing server in 60 seconds - or talk to our team about dedicated hardware for large-scale computation.