Can I use Kronvex instead of Weaviate?

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, Weaviate is more appropriate. Kronvex is not a general vector database — it is a memory API.

Does Kronvex use Weaviate internally?

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

Comparison · Vector DB vs Memory API

Weaviate 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 Weaviate, 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.

Q: How is Kronvex pricing different from Weaviate?

Weaviate Cloud charges based on resources and usage, which can be unpredictable. Self-hosting Weaviate means bearing your own infrastructure costs. Kronvex charges a flat monthly fee from €29/mo (Builder: 5 agents, 20,000 memories). For B2B SaaS products, flat pricing is more predictable and often cheaper at moderate scale.

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

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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	Weaviate	Kronvex
Purpose	General-purpose vector database	Purpose-built memory API for AI agents
Setup to first memory	Hours (schema design, collections, config, embed)	5 min (POST /remember)
Confidence scoring	✗ Raw vector distance 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 multi-tenancy configuration	✓ First-class agent concept, isolation by design
GDPR / EU hosting	~ EU region on Weaviate Cloud; self-hosted = your compliance	✓ Supabase Frankfurt, EU-native by default
Pricing model	Usage-based (Weaviate Cloud) or self-hosted infra costs	Flat from €29/mo (Builder)
Free tier	Sandbox clusters (limited, expire)	✓ Demo key, 1 agent, 100 memories, EU, no expiry
Python & Node SDK	✓ Official SDKs	✓ Official SDKs (pip & npm)

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

Honest comparison

Which one should you use?

Use Weaviate when…

You need raw vector infrastructure

You're building a custom semantic search engine (products, documents, knowledge bases)
You want multi-modal vectors (text + images + structured data)
You need fine-grained schema control and metadata filtering
You're building recommendation systems where you own all ranking logic
Your team has the infra bandwidth to self-host and maintain a vector DB
Memory is not your core use case — you're doing document RAG

Use Kronvex when…

You're building agent memory

Your AI agent must 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 serve EU users and GDPR compliance is non-negotiable
You want a flat monthly cost rather than usage-based billing
You need multi-tenant isolation where each customer's agent is separate

Code comparison

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.

weaviate_memory.py

          With Weaviate (DIY)
          import weaviate
import openai
from datetime import datetime

# Step 1: Connect and define schema
client = weaviate.connect_to_weaviate_cloud(
    cluster_url="...",
    auth_credentials=weaviate.Auth.api_key("...")
)
# Must define collection schema first
client.collections.create(
    "Memory",
    properties=[
        weaviate.Property(name="content", ...),
        weaviate.Property(name="agent_id", ...),
        weaviate.Property(name="created_at", ...),
        weaviate.Property(name="access_count", ...)
    ]
)

# Step 2: Embed the query manually
oai = openai.OpenAI()
def embed(text):
    r = oai.embeddings.create(
        model="text-embedding-3-small",
        input=text
    )
    return r.data[0].embedding

# Step 3: Query with filters
query_vec = embed("What does the user like?")
collection = client.collections.get("Memory")
results = collection.query.near_vector(
    near_vector=query_vec,
    filters=weaviate.Filter.by_property("agent_id").equal("user-123"),
    limit=10
)

# Step 4: Manual recency + frequency scoring
def confidence(obj, sim):
    age_days = (datetime.now() - obj.properties['created_at']).days
    recency = 1 / (1 + 0.023 * age_days)
    freq = min(obj.properties['access_count'], 10) / 10
    return sim * 0.6 + recency * 0.2 + freq * 0.2

scored = sorted([
    (obj, confidence(obj, obj.metadata.certainty))
    for obj in results.objects
], key=lambda x: x[1], reverse=True)[:5]

# Step 5: Format as prompt block manually
context = "Memory context:\n" + "\n".join(
    f"- {o.properties['content']} (confidence: {s:.2f})"
    for o, s in scored
)
# ~45 lines just to inject context
        

kronvex_memory.py

          With Kronvex (managed)
          import requests

HEADERS = {"X-API-Key": "kv-your-key"}
BASE = "https://api.kronvex.io/api/v1"
AGENT = "user-123"

# Get ranked context, formatted for your prompt
context = requests.get(
    f"{BASE}/agents/{AGENT}/inject-context",
    headers=HEADERS
).json()["context"]

# Returns ready-to-use prompt block:
# "Memory context:
#  - User prefers Python  (confidence: 0.94)
#  - User dislikes boilerplate  (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 vector distance 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 object 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 — returning a prompt-ready block of the top N ranked memories, sorted by confidence score.

one API call → ready prompt

🇪🇺

EU-native by design

Self-hosted Weaviate means you manage GDPR compliance yourself. Weaviate Cloud has EU regions but at an extra cost. Kronvex stores all data in Supabase Frankfurt by default. GDPR compliance — right to erasure, data export, TTL — is built into the API.

Frankfurt · GDPR · right to erasure

Questions

Frequently asked

Is Weaviate a memory API?

No. Weaviate is a general-purpose vector database. It stores objects with vector representations and supports semantic search. It has no concept of agents, sessions, recency weighting, or inject-context. You have to build all that memory logic yourself — schema design, embedding, scoring, prompt formatting — on top of the raw vector search. For agent memory specifically, that's weeks of extra engineering.

Can I replace Weaviate with Kronvex?

It depends on your use case. If you're using Weaviate to build agent memory (storing conversation facts, recalling them by relevance on future turns), Kronvex replaces Weaviate and the custom memory layer you'd build on top. If you're using Weaviate for document search, multi-modal retrieval, or a custom recommendation system, 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 Weaviate 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 Weaviate alone, you get raw object 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 Weaviate?

Weaviate Cloud charges based on resource usage, which can scale unpredictably. Self-hosting Weaviate means bearing your own infrastructure and engineering costs. Kronvex charges a flat monthly fee (€29/mo for Builder with 5 agents and 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. EU hosting and GDPR tools are 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. While Weaviate Cloud offers EU region options, self-hosted Weaviate deployments require you to manage your own GDPR compliance — a significant overhead for most teams.

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Weaviate vs KronvexVector DB vs Memory API