score --dx --ax --prod --pricing --perf
Scorecard
dx 80
ax 74
production 83
pricing 58
performance 84
How to read these scores
86+ is excellent, 74-85 is solid, and anything below 74 needs active scrutiny before a team or agent depends on it.
cat ./evidence/pinecone.md
What Neurl built with it
Validated retrieval decision flows for AI application architecture.
Scenario
Indexing and querying embedded content for agent-readable retrieval.
Method
- Compared setup friction
- Checked filtering model
- Reviewed cost levers
- Mapped agent retrieval needs
Public artifacts
Limitations
- Scores reflect Neurl hands-on evidence and should be re-verified before procurement or high-risk production adoption.
- Pricing, limits, model defaults, and product policies can change quickly; use freshness dates and vendor docs before final rollout.
when-to-use pinecone
Use it when
- Retrieval / RAG
- managed vector search
- RAG backends
- retrieval infrastructure
avoid-if pinecone
Not a fit when
- tiny prototypes that can use local or Postgres vector search
- teams needing strict cost predictability
- simple keyword search
pricing --teardown
Pricing teardown
Managed convenience has real value, but pricing transparency and small-project economics need scrutiny.
- Check storage/query assumptions
- Compare against pgvector or lighter managed options
prod --readiness
Production notes
Production-ready for managed vector workloads with the right cost and data model fit.
- Retrieval quality depends more on chunking/evals than database choice alone
ls ./use-cases/pinecone
Best use cases
RAG backend
Good when retrieval is a core production feature.
Agent memory
Useful if metadata filtering and predictable retrieval behavior are well tested.