What does an on-premise integration runtime cost your server? We measured ours

Description and use

Измерения资源 использования локальногоruntime Neblex Worker показали: idleпотребляет ~25 МБ; 14 из 15 everydayflows平均 29–94 МБ; 1МRowза

Problem

IT and security teams need to know what an on-premise integration runtime will cost their servers before installing it, specifically memory and CPU usage across different flow types and batch sizes.

Outcome

Idle runtime uses ~25 MB memory; 14 of 15 everyday flows use 29–94 MB; 1M row cloud load peaks at ~450 MB; heavy matching peaks at ~947 MB resident; CPU stays under 31% for everyday flows and 102% for heavy matching; destination latency dominates runtime time, not compute.

Workflow

• Deploy Neblex Worker as background service or container (4 CPU cores, 4 GB RAM) alongside PostgreSQL, Valkey, and Kafka instances
• Run 15 everyday flows covering events, files, CRM syncs, and database loads on synthetic/test data
• Run 1 million row load from local PostgreSQL to cloud PostgreSQL with data-quality checks and masking
• Run fuzzy duplicate matching over ~970,000 records grouped by zip code
• Run plain copy from PostgreSQL to Valkey at 1,000 to 5 million rows to isolate batch size effects
• Sample runtime memory and CPU every half second using standard process tools
• Record start/finish times from runtime logs and average metrics from Neblex console

Evidence

Idle, it used about 25 MB of memory. Fourteen of the 15 flows averaged 29 to 94 MB, and a 1 million row load into a cloud database peaked at about 450 MB.

Source

The runtime is a single executable, about 74 MB on macOS and 92 MB on Linux x64. It needs no separate language runtime, such as Java, .NET or Node.js, and it starts no child processes.

Source

Deployments: we ran the runtime two ways, as a background service and in a container capped at 4 CPU cores and 4 GB of RAM. The test machine also hosted the PostgreSQL, Valkey (an open-source, Redis-compatible cache) and Kafka instances it read from and wrote to, so the runtime competed for memory more than it would on a dedicated server.

Source

First run, 970,000 records: 1,510 seconds. CPU averaged about 102% of one core, peaking at 217%, and resident memory peaked at 534 MB.

Source

Inserting into the cloud database one row per statement took about 0.35 seconds per round trip, under 3 rows per second. Grouping thousands of rows into each statement brought the full 1 million rows down to under 7 minutes.

Source

Raising the flow's concurrency setting to 4 and then 64 didn't change that rate. That fits what Valkey's documentation on pipelining describes: when a client sends one command at a time, round-trip time is the limit.

Source