Can I use Kronvex instead of Pinecone?

It depends on your use case. If you need agent memory with confidence scoring, recency weighting, and inject-context already built in, Kronvex is the right tool. If you need raw vector storage for a custom search or recommendation system, Pinecone is more appropriate. Kronvex is not a general vector database — it is a memory API.

Does Kronvex use Pinecone internally?

No. Kronvex uses PostgreSQL with the pgvector extension hosted on Supabase Frankfurt. All data stays in the EU. There is no Pinecone dependency.

What does confidence scoring mean in Kronvex?

Kronvex ranks recalled memories using: confidence = similarity × 0.6 + recency × 0.2 + frequency × 0.2. This means a memory that is highly similar but old and rarely accessed will rank lower than a slightly less similar but recent, frequently accessed one. Pinecone returns raw cosine similarity with no awareness of time or access patterns.

Comparison · Vector DB vs Memory API

Pinecone vs Kronvex
Vector DB vs Memory API

Q: What is inject-context and why does it matter?

inject-context is a Kronvex endpoint that retrieves the most relevant memories for a query and returns them formatted as a ready-to-use system prompt block. With Pinecone, you get raw vector matches — you still need to format them, rank them by recency and frequency, and inject them into your prompt. inject-context does all of that in one API call.

Pinecone is a general-purpose vector database — it stores vectors, you build the rest. Kronvex is a purpose-built memory API: confidence scoring, recency weighting, and inject-context are already done for you. Different tools, different jobs.

Get your free demo key Read the docs

EU-hosted · GDPR-native confidence = similarity × 0.6 + recency × 0.2 + frequency × 0.2 Demo key — 100 memories free

TL;DR

Side-by-side in 30 seconds

Feature / Question	Pinecone	Kronvex
Purpose	General-purpose vector DB	Purpose-built memory API for AI agents
Setup to first memory	30+ min (embed → upsert → query → format)	5 min (POST /remember)
Confidence scoring	✗ Raw cosine score only	✓ similarity × 0.6 + recency × 0.2 + frequency × 0.2
Recency weighting	✗ Build it yourself	✓ Sigmoid with 30-day inflection, built-in
inject-context endpoint	✗ You format the prompt yourself	✓ Returns ranked, formatted context block
Agent / session model	✗ Manual namespace management	✓ First-class agent concept, isolation by design
GDPR / EU hosting	✗ US-hosted by default	✓ Supabase Frankfurt, EU-native
Pricing model	Per write unit + storage + reads	Flat monthly (from €29/mo)
Free tier	Serverless free tier (US region)	✓ Demo key, 1 agent, 100 memories, EU
Python & Node SDK	✓ Official SDKs	✓ Official SDKs (pip & npm)

Data accurate as of March 2026. Sources: pinecone.io, kronvex.io/docs.

Honest comparison

Which one should you use?

Use Pinecone when…

You need raw vector infrastructure

Building a custom semantic search engine (products, documents, media)
Doing recommendation systems where you own all the ranking logic
Running massive-scale similarity search across billions of vectors
Embedding arbitrary objects (images, audio, structured data)
You want full control over namespaces, metadata filtering, and index tuning
Memory is not your use case — you're doing RAG over documents or knowledge bases

Use Kronvex when…

You're building agent memory

Your AI agent needs to remember past interactions across sessions
You want recency and frequency to influence which memories surface
You need inject-context to build the system prompt automatically
You're serving EU users and GDPR compliance is non-negotiable
You want a flat, predictable monthly cost instead of per-operation billing
You need multi-tenant isolation where each customer's agent is separate

Code comparison

The same task, two approaches

Goal: when an agent starts a conversation, inject the 5 most relevant memories into the system prompt. Here's how much code each approach requires.

            
            
            
          
agent_memory_pinecone.py

PINECONE — build it yourselfimport openai
from pinecone import Pinecone
from datetime import datetime
import math

pc = Pinecone(api_key="pc-...")
index = pc.Index("agent-memories")
oai = openai.OpenAI()

def get_embedding(text):
    r = oai.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return r.data[0].embedding

def remember(agent_id, text):
    vec = get_embedding(text)
    index.upsert(vectors=[{
        "id": f"{agent_id}_{hash(text)}",
        "values": vec,
        "metadata": {
            "text": text,
            "agent_id": agent_id,
            "ts": datetime.utcnow().isoformat(),
            "access_count": 0
        }
    }], namespace=agent_id)

def inject_context(agent_id, query, n=5):
    vec = get_embedding(query)
    results = index.query(
        vector=vec, top_k=20,
        namespace=agent_id,
        include_metadata=True
    )
    # Manual confidence scoring: you build this
    now = datetime.utcnow()
    scored = []
    for m in results.matches:
        sim = m.score
        ts = datetime.fromisoformat(m.metadata["ts"])
        age_days = (now - ts).days
        recency = 1 / (1 + math.exp(-(age_days - 30) / 5))
        freq = math.log1p(m.metadata.get("access_count", 0))
        score = sim*0.6 + recency*0.2 + freq*0.2
        scored.append((score, m.metadata["text"]))
    scored.sort(reverse=True)
    top = [t for _, t in scored[:n]]
    # Format as prompt block — also your job
    return "Memory context:\n" + "\n".join(
        f"- {t}" for t in top
    )

# ~50 lines of boilerplate, just for basic memory
        

            
            
            
          
agent_memory_kronvex.py

KRONVEX — already done for youfrom kronvex import Kronvex

client = Kronvex(api_key="kv-...")
agent = client.agent("user-123")

# Store a memory (embedding done server-side)
await agent.remember(
    "User prefers Python over JavaScript"
)

# Get ranked context, formatted for your prompt
context = await agent.inject_context(
    "What language does this user prefer?"
)
# Returns ready-to-use prompt block:
# "Memory context:
#  - User prefers Python over JavaScript  (confidence: 0.94)
#  - User dislikes boilerplate code         (confidence: 0.71)
#  ..."

# Confidence scoring is automatic:
# similarity×0.6 + recency×0.2 + frequency×0.2
# EU-hosted, GDPR-native, no infra to manage.

# 7 lines. That's it.
        

What Kronvex adds on top of raw vectors

Memory ≠ Vector storage

⚡

Confidence scoring

Raw cosine similarity ignores time. A memory from 6 months ago ranks the same as one from yesterday. Kronvex weights by recency (sigmoid, 30-day inflection) and frequency (log-scaled access count) — so the most relevant AND recent memories win.

similarity · recency · frequency

🔌

inject-context

Vector DBs return matches. You still need to rank them, format them, and inject them into your system prompt. Kronvex's inject-context does all of this in one API call — it returns a prompt-ready block of the top N ranked memories, sorted by confidence score.

one API call → ready prompt

🇪🇺

EU-native by design

Pinecone is US-hosted. If your users are in Europe, every agent memory they generate crosses the Atlantic. Kronvex stores all data in Supabase Frankfurt. GDPR compliance — right to erasure, data export, TTL — is built into the API, not an afterthought.

Frankfurt · GDPR · right to erasure

Questions

Frequently asked

Is Pinecone a memory API?

No. Pinecone is a general-purpose vector database. It stores and retrieves high-dimensional vectors by similarity. It has no concept of agents, sessions, recency, or frequency. There's no inject-context. You have to build all of that memory logic yourself on top of the raw similarity search. For many use cases (document search, recommendation systems), that's fine. For agent memory, it means weeks of extra engineering.

Can I replace Pinecone with Kronvex?

It depends entirely on your use case. If you're using Pinecone specifically to build agent memory (store conversation facts, recall them by relevance on future turns), then yes — Kronvex replaces Pinecone and the custom memory logic you'd build on top of it. If you're using Pinecone for document RAG, semantic search over a product catalog, or embedding arbitrary data types, Kronvex is not the right tool — it's a memory API, not a general vector database.

What vector database does Kronvex use internally?

Kronvex uses PostgreSQL with the pgvector extension, hosted on Supabase Frankfurt (EU). It uses text-embedding-3-small from OpenAI for 1536-dimension embeddings and cosine similarity for retrieval. There is no dependency on Pinecone or any other external vector service. All data stays in the EU.

What is inject-context and why does it matter?

inject-context is a Kronvex endpoint that does three things in a single API call: (1) retrieves the most semantically similar memories for a query, (2) ranks them using the confidence formula (similarity×0.6 + recency×0.2 + frequency×0.2), and (3) returns them formatted as a ready-to-inject system prompt block. With Pinecone alone, you get raw vector matches — you still need to add the ranking logic, format the output, and handle edge cases. inject-context collapses all of that into one call.

How is Kronvex pricing different from Pinecone?

Pinecone charges per write unit, per read unit, and per GB of storage. The cost is usage-based, which can be unpredictable at scale. Kronvex charges a flat monthly fee (€29/mo for Builder with 20,000 memories). For B2B SaaS products where memory usage is tied to customer activity, flat pricing is more predictable and often cheaper at moderate scale. The EU hosting and built-in GDPR tools are also included — not add-ons.

Is Kronvex GDPR compliant?

Yes. All data is stored in Supabase's EU region (Frankfurt, Germany). We support the right to erasure via DELETE /memories, per-agent memory TTL configuration, and full data export from the dashboard. Pinecone is US-hosted on standard plans with no EU data residency option — a compliance gap for teams serving European users under GDPR.

Start with a free demo key

EU-hosted. No credit card. Skip the boilerplate — your first memory in under 5 minutes.

Get your demo key →

Demo key · 1 agent · 100 memories · No expiry

Pinecone vs KronvexVector DB vs Memory API