Runtime tuning (Node.js / Python / Go)

Runtime tuning is a project for your stack: for Node.js — clustering/workers (by cores), pool and event-loop setup, memory/GC flags; for Python — the run model (gunicorn/uvicorn, number of workers/threads, async where appropriate), pools; for Go — GOMAXPROCS, GC settings, profiling; common — DB/service connection pools, timeouts, graceful shutdown, metrics. Honestly about limits, this is key: tuning unlocks CPU/RAM and removes config bottlenecks, but does NOT create resources and does NOT rewrite the architecture — on a weak server or under high load the ceiling is in the hardware (beyond that scaling, separate). Honestly about code: a significant part of performance problems is in the code itself (blocking operations, leaks, inefficient algorithms, no async); tuning the config does not cure them — code changes are needed (we can do them separately), we honestly separate what is solved by config and what needs code. Honestly about GC and memory: garbage-collection settings are trade-offs (pauses vs throughput vs memory), we pick for the profile, without 'magic' values. Honestly about risk: wrong parameters/clustering can cause instability; we test under load, keep rollback. Honestly about access: access to the server/code/deploy is needed. Honestly about business: stability and response improve, we do NOT guarantee sales growth by itself. An important boundary: this is runtime tuning, not code refactoring (separate), not web server tuning (722) and not DB optimization (717-719). Picture this: instead of 'the app uses one core and leaks memory' — efficient resource use and stable operation under load. The base price starts from 18,000 ₽ per project; it depends on the stack and complexity.

• The app does not use all cores, stuck on one process.
• Memory leaks and long GC pauses under load.
• There are no connection pools and graceful shutdown.
• Under load there is instability and low throughput.

• Clustering/workers by cores (Node/Python/Go)
• Memory, GC and concurrency setup for the profile
• DB/service connection pools, timeouts
• Graceful shutdown and basic metrics
• Profiling bottlenecks
• Separation: what tuning solves vs what needs code changes
• Testing under load and rollback
• Handover and review with you

• Efficient CPU/RAM use
• Higher throughput and stability
• Controlled memory and GC for the profile
• A resilience base (sales growth — not guaranteed)

• We profile the runtime and load; collect server/code access
• We set up workers/memory/GC/pools carefully
• We test under load, separate code tasks, keep rollback, review with you

• Will runtime tuning speed up any application?

  It unlocks CPU/RAM and removes config bottlenecks, but does not create resources or rewrite the architecture. On a weak server/high load the ceiling is in the hardware (beyond that scaling). A significant part of problems is in the code, and config tuning does not cure them.

• What if it is the code?

  We honestly separate what is solved by runtime tuning and what needs code changes (blocking operations, leaks, algorithms, async). Code refactoring is separate work; we advise priorities rather than pretend everything is cured by config.

• Will GC settings give a guaranteed boost?

  Garbage-collection settings are trade-offs (pauses vs throughput vs memory); there are no 'magic' values, we pick for your profile and test. We do not guarantee sales growth by itself — it is stability and response.