Servers tool
Server rack power calculator
Estimate power, cooling, circuit, and operations impact before hardware arrives.
What to collect
| Server draw | Expected idle, average, and peak watts for CPU, GPU, memory, drives, and fans. |
|---|---|
| Circuit capacity | Available voltage, amperage, redundancy, and derating rules. |
| Cooling path | Room airflow, rack layout, heat exhaust, noise, and remote access. |
How to use it
| 1 | Plan around sustained and peak draw, not only PSU rating. |
|---|---|
| 2 | Confirm circuit, rack depth, airflow, and service access before ordering. |
| 3 | Add remote management and spare strategy to acceptance criteria. |
How to read the result
| Ready | Power and cooling confirmed | Proceed to support, spares, and acceptance testing. |
|---|---|---|
| Constrained | Resize or colocate | A smaller node or colo rack may beat office improvisation. |
| Not ready | Facility unknown | Use cloud or a workstation while operations catches up. |
Useful vs risky
| Healthy | Facility owner signs off before the purchase order. |
|---|---|
| Risky | The server is bought before power, cooling, noise, and remote hands are priced. |
Buyer tools
Quick checks before a shortlist
AI model cost calculator
Use this before choosing a default model. The useful answer is not the cheapest token price; it is the cheapest solved task with acceptable latency and failure rate.
AI Modelsllm context window plannerLLM context window planner
Long context helps only when the model still follows instructions near the end of the prompt. This planner forces a fit check before a bigger context tier becomes the easy answer.
AI Modelsprompt routing savings estimatorPrompt routing savings estimator
Routing is useful when easy prompts are common and failure is observable. It is wasteful when every task is rare, expert, or hard to classify.
AI Modelsinference latency budget plannerInference latency budget planner
A fast model can still feel slow if retrieval, tool calls, retries, and post-processing are not budgeted. This planner keeps the whole user path visible.
AI Modelsmodel eval sample size plannerModel eval sample size planner
Small evals can still be useful if they are realistic and repeated. This tool makes the sample deliberate: enough cases to catch regression, not so many that no one maintains it.
AI Toolsrag chunk size plannerRAG chunk size planner
Chunking is not a magic number. The right size depends on the shape of the source and whether the model needs local detail, full sections, or cross-document synthesis.
AI Toolsembedding storage cost estimatorEmbedding storage cost estimator
Embedding cost is rarely just the first import. Refresh cycles, duplicate content, metadata, backups, and permission filters decide whether the system stays manageable.
AI Toolsapi rate limit plannerAPI rate limit planner
Rate limits are product constraints. This planner helps choose batching, backoff, queueing, and multi-model fallback before launch traffic teaches the lesson.