1. What “capacity” actually means for a validator stack
A healthy validator stack has enough headroom to tolerate normal volatility: mempool bursts, blob-heavy blocks, short networking issues, occasional resyncs. In practice this means thinking about resources in four dimensions:
On top of this you may have:
- MEV-Boost or other sidecars (extra network and CPU),
- local block building with blobs, which can be disk- and IO-heavy,
- monitoring and backups that add their own load.
2. Where operators usually get surprised
Even experienced teams get caught off-guard by resource issues. Common patterns:
- Disk growth underestimated – beacon or execution node stores months of history and logs on a disk that was sized “for a test run”.
- Bandwidth saturation – running too many validators, or enabling builders and extra peers, on a connection that was never designed for it.
- Aggressive consolidation – moving many validators onto fewer machines without re-analysing CPU and memory usage under worst-case scenarios.
In all these cases, the problem was not lack of monitoring – it was the absence of a clear capacity model and a place where it is tracked.
2.1 Capacity profiles instead of one-off guesses
In Validator Tools we encourage thinking in terms of simple, named profiles rather than individual hosts. For example:
Once profiles are defined and visible in the GUI, capacity decisions can be made against those profiles, not against ad-hoc hostnames.
This sequence shows how to use Validator Tools to move from “we think it will be fine” to “we have a documented resource plan per profile and node”.
- Install the desktop application. Download and install the latest version Validator Tools GUI for your operating system (Windows, macOS or Linux). Run it on an operator workstation with network access to your validator, beacon and execution nodes.
- Register nodes and basic hardware facts. In the infrastructure view, add each node (or VM) that participates in your validator stack and record: CPU cores, RAM, disk size/type (SSD/HDD/NVMe), and network context (home, DC, cloud region).
- Attach validators and services to nodes. For each node, let the app discover attached services: beacon, execution, validator clients, MEV-Boost, builders, monitoring agents. Associate validator ranges (e.g. indices or counts) with those services.
- Define and assign capacity profiles. Create a few capacity profiles (solo / standard / high-throughput) and define rough expected bounds: typical validator counts, recommended disk headroom, and bandwidth expectations. Assign each node to one of these profiles in the GUI.
- Ingest basic utilisation metrics. Connect Validator Tools to existing metrics sources (e.g. Prometheus endpoints or client APIs) to record average and peak usage for CPU, RAM, disk consumption over time and bandwidth if available.
- Identify nodes close to their thresholds. Use the capacity view to highlight: nodes whose disk usage routinely exceeds a set percentage, nodes whose CPU or RAM peaks leave little headroom, and nodes carrying more validators than the profile suggests.
- Plan resizing or redistribution actions. For nodes near limits, create actions in the app: move a subset of validators to another node, upgrade disk or RAM, or introduce an additional node with the same profile. Attach these actions to maintenance windows.
- Review after changes and update profiles. After executing resizing or migrations, review the new utilisation patterns and adjust profile expectations if needed. Over time, this gives you realistic, organisation-specific capacity numbers.
4. How Validator Tools helps you stay ahead of resource issues
4.1 A single view of “where the load lives”
Instead of spreading information across cloud consoles, monitoring dashboards and notebooks, Validator Tools gives you one place where you can see:
- which nodes host which CL/EL/VC instances and sidecars,
- how many validators those nodes serve,
- which capacity profile each node is supposed to follow.
This makes “where do we put the next N validators?” a concrete question, not a guess.
4.2 Connecting utilisation to validator risk
Pure infrastructure metrics are useful, but they do not tell you which validators are at risk if a node starts to struggle. In Validator Tools, resource warnings can be expressed in validator terms:
- “this node is at 85% disk and serves 128 validators for entity A”,
- “this CL node is frequently CPU-saturated during peak hours, affecting all validators using it”,
- “bandwidth saturation on this link would delay proposals for these specific indices”.
This helps prioritise capacity work in a way that matches business and governance expectations.
4.3 Making upgrades predictable
Capacity planning is also about timing. Upgrades and resizes are safer when:
- they are scheduled during low expected load,
- you know exactly which validators and services are affected,
- you have clear “before” and “after” utilisation snapshots in the tool.
The maintenance and action tracking in Validator Tools can be used to tie capacity changes to specific exit windows, testnets rehearsals and post-change reviews.
5. Practical dos and don’ts for capacity planning
- Track validator counts and resource usage per node, not just per cluster.
- Keep a simple set of capacity profiles and update them as you learn.
- Leave intentional headroom for spikes, resyncs and future protocol changes.
- Revisit capacity assumptions after enabling MEV, builders or blobs.
- Guess how many validators “should fit” on new hardware without data.
- Ignore disk growth and log retention until a volume fills up in production.
- Consolidate validators onto fewer nodes without re-checking peak usage.
- Rely solely on cloud auto-scaling; validator workloads are not generic web apps.