{
  "schemaVersion": "2026-05-14.tool-review.v1",
  "slug": "pinecone",
  "name": "Pinecone",
  "category": "vector-db",
  "verdict": {
    "label": "Use with caution",
    "tone": "use-with-caution",
    "summary": "Use Pinecone when managed vector search matters; compare carefully for small projects and agent memory."
  },
  "scores": {
    "dx": 80,
    "ax": 74,
    "production": 83,
    "pricing": 58,
    "performance": 84
  },
  "pricingTier": "usage-based",
  "agentReadiness": "partial",
  "timeToFirstSuccessMinutes": 35,
  "recommendedFor": [
    "retrieval-rag"
  ],
  "avoidWhen": [
    "tiny prototypes that can use local or Postgres vector search",
    "teams needing strict cost predictability",
    "simple keyword search"
  ],
  "evidence": {
    "built": "Validated retrieval decision flows for AI application architecture.",
    "testedScenario": "Indexing and querying embedded content for agent-readable retrieval.",
    "methodology": [
      "Compared setup friction",
      "Checked filtering model",
      "Reviewed cost levers",
      "Mapped agent retrieval needs"
    ]
  },
  "evidenceProfile": {
    "level": "strong",
    "artifacts": [
      {
        "kind": "human-review",
        "label": "Human review page",
        "href": "/blueprints/reviews/pinecone",
        "public": true
      },
      {
        "kind": "agent-json",
        "label": "Agent JSON verdict",
        "href": "/blueprints/reviews/pinecone.json",
        "public": true
      },
      {
        "kind": "compare-view",
        "label": "Compare with alternatives",
        "href": "/blueprints/reviews/compare?tools=pinecone,langsmith,cursor",
        "public": true
      }
    ],
    "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."
    ],
    "confidenceSignals": [
      "Tested scenario: Indexing and querying embedded content for agent-readable retrieval.",
      "4 methodology checks",
      "Last verified: 2026-05-14",
      "2 agent safe-use notes"
    ],
    "agentEvidenceSummary": "Pinecone was tested in scenario \"Indexing and querying embedded content for agent-readable retrieval.\" and last verified on 2026-05-14. Use the human review, agent JSON verdict, and compare view before trusting the recommendation."
  },
  "freshness": {
    "lastTestedAt": "2026-04-22",
    "lastVerifiedAt": "2026-05-14",
    "staleAfterDays": 90,
    "scoreDiffLog": [
      "2026-05-14: pricing score marked as watch item"
    ],
    "changelogPulse": "Vector DB tradeoffs change with managed Postgres and agent-memory patterns."
  },
  "agent": {
    "skillText": "Use Pinecone when the task needs managed vector retrieval for a production RAG or agent-memory system. Avoid it for tiny prototypes, simple keyword search, or when Postgres vector search is already enough.",
    "manifestSnippet": {
      "name": "pinecone",
      "useWhen": [
        "retrieval-rag",
        "managed vector search",
        "agent memory"
      ],
      "avoidWhen": [
        "tiny prototype",
        "simple keyword search",
        "strict fixed budget"
      ],
      "requiredContext": [
        "embedding model",
        "metadata filters",
        "query volume",
        "latency target"
      ],
      "confidence": "medium"
    },
    "safeUseNotes": [
      "Run retrieval evals before blaming the database",
      "Model query/storage costs with realistic volumes"
    ]
  }
}