AI Models tool
LLM context window planner
Plan the minimum context window that can solve the job without paying for huge prompts by default.
What to collect
| Longest source | Largest document, code diff, transcript, or ticket bundle a user will submit. |
|---|---|
| Required evidence | Whether the answer must cite sources, inspect all files, or only summarize. |
| Latency tolerance | How long users will wait before the workflow feels broken. |
How to use it
| 1 | Measure the longest real input and expected answer size. |
|---|---|
| 2 | Decide which context can be retrieved instead of pasted. |
| 3 | Test long-context accuracy at the far end of the prompt. |
How to read the result
| Daily work | Short context plus retrieval | Cheaper and faster for most product interactions. |
|---|---|---|
| Research work | Long context | Useful when the model must inspect a full transcript, contract, or repo map. |
| Sensitive work | Human review lane | Long context can still miss instructions, so high-risk output needs review. |
Useful vs risky
| Healthy | Long context is reserved for workflows that prove recall and citation quality. |
|---|---|
| Risky | Huge context is bought to avoid document design, chunking, or retrieval work. |
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.