AI Decision Tools
10 interactive tools — answer a few questions, get a concrete recommendation.
Agent vs RAG: Which Architecture Do You Need?
RAG (retrieval-augmented generation) and AI agents solve different problems. RAG retrieves facts; agents take actions. This tool diagnoses which architecture — or combination — your application needs.
Use RAG when your problem is 'the model doesn't know the right information.' Use…
How Much Context Window Do You Actually Need?
Bigger context windows cost exponentially more and don't always perform better. This tool recommends the right context window size for your use case — and whether you need a memory strategy instead.
Most applications need 32K–128K tokens. Use RAG with a smaller context window ra…
How Should You Evaluate Your LLM Application?
Without evaluation, you're flying blind — you can't tell if a model change improved or degraded quality. This tool recommends the right evaluation approach based on your task type, available ground truth, and team size.
For most teams: start with LLM-as-judge for fast iteration, build a golden datas…
OpenAI vs Anthropic: Which API Is Right for Your Use Case?
OpenAI and Anthropic are the two dominant frontier AI API providers, but they have different strengths. This tool compares them across task type, safety requirements, context window needs, and pricing to give you a clear recommendation.
Claude (Anthropic) is the better default for long-context tasks, coding agents, …
Should You Use Prompt Caching? (Anthropic + OpenAI)
Prompt caching lets you reuse expensive prompt prefixes across requests, cutting costs by 50–90%. This tool tells you whether caching applies to your use case and how to implement it effectively.
Use prompt caching if you have a system prompt over 1,024 tokens that's sent on …
RAG vs Fine-Tuning: Which Should You Use?
RAG and fine-tuning solve different problems. This interactive tool walks through your specific requirements — data freshness, task type, labeled data availability — to give you a clear recommendation.
Use RAG when your data changes frequently or you need citations and source groun…
Self-Host vs LLM API: Should You Run Your Own Model?
Self-hosting an LLM saves money at scale but comes with significant infrastructure burden. This tool evaluates your token volume, privacy needs, and team capacity to tell you whether to self-host or use a managed API.
Use a managed API (OpenAI, Anthropic, Google) unless you're processing over 1 bi…
Streaming vs Batch LLM Processing: Which to Choose?
Streaming and batch LLM processing have very different cost, latency, and UX profiles. This tool helps you decide based on your workload type, latency tolerance, and volume requirements.
Use streaming for user-facing interfaces where perceived responsiveness matters.…
Which Vector Database Should You Use?
Pinecone, pgvector, Qdrant, Weaviate, Chroma — the vector database landscape is crowded. This tool finds the right one based on your scale, infrastructure preferences, and query patterns.
For most new RAG projects: start with pgvector (free, inside your existing Postg…
Which LLM Should I Use? (2026 Decision Tool)
With 25+ LLM providers and hundreds of models, choosing the right one is genuinely hard. This tool routes you to the best model for your use case, budget, and latency requirements in 2026.
For most production use cases in 2026: Claude Sonnet 4 for quality-critical task…