Servers tool
GPU cloud vs colocation calculator
Estimate when colocation makes sense after power, bandwidth, remote hands, spares, and downtime are included.
What to collect
| Utilization | Expected GPU hours, concurrency, and workload predictability. |
|---|---|
| Facility cost | Rack space, power draw, bandwidth, remote hands, spares, and insurance. |
| Ops maturity | Who handles firmware, failed drives, cables, thermal events, and recovery. |
How to use it
| 1 | Use cloud until workload shape and utilization are proven. |
|---|---|
| 2 | Compare colo only after power and remote management are priced. |
| 3 | Keep a cloud fallback for overflow, failures, and model experiments. |
How to read the result
| Cloud | Variable workload | Best for experiments, bursts, and uncertain model choices. |
|---|---|---|
| Colocation | Stable high utilization | Works when operations ownership is real. |
| Owned office rack | Rarely first choice | Power, noise, cooling, and access usually make it harder. |
Useful vs risky
| Healthy | Colo savings survive a realistic downtime and remote-hands budget. |
|---|---|
| Risky | The plan ignores facility constraints and support response time. |
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.