LanceDB Has a Free API — Here's How to Build Serverless AI Search with Zero Infrastructure

Why LanceDB?

LanceDB is a serverless vector database that runs embedded — no server process needed. Built on Lance columnar format, it's fast for both vector search and analytics.

Free and open source. LanceDB Cloud has a free tier.

Getting Started

pip install lancedb

import lancedb
import numpy as np

db = lancedb.connect("./my_lancedb")

# Create table with data
data = [
    {"text": "Machine learning transforms industries", "category": "AI", "vector": np.random.rand(128).tolist()},
    {"text": "React 19 introduces new features", "category": "Frontend", "vector": np.random.rand(128).tolist()},
    {"text": "Kubernetes simplifies deployment", "category": "DevOps", "vector": np.random.rand(128).tolist()}
]

table = db.create_table("articles", data)

# Vector search
results = table.search(np.random.rand(128).tolist()).limit(3).to_pandas()
print(results[["text", "category", "_distance"]])

# Filtered search
results = table.search(query_vec).where("category = 'AI'").limit(5).to_pandas()

# Full-text search (hybrid!)
table.create_fts_index("text")
results = table.search("machine learning", query_type="fts").limit(5).to_pandas()

TypeScript

import * as lancedb from "lancedb";

const db = await lancedb.connect("./my_lancedb");
const table = await db.createTable("docs", [
  { text: "Hello world", vector: [0.1, 0.2], category: "greeting" }
]);

const results = await table.search([0.1, 0.2]).limit(5).execute();
results.forEach(r => console.log(r.text));

Why LanceDB?

Feature	LanceDB	Chroma	Pinecone
Embedded	Yes	Yes	No
Format	Lance (columnar)	Custom	Proprietary
Analytics	Yes	No	No
Multimodal	Yes	Limited	No
License	Apache 2.0	Apache 2.0	Proprietary

Use Cases

1. RAG applications — embed docs, retrieve context for LLMs
2. Multimodal search — images + text in one table
3. ML pipelines — store embeddings alongside raw data
4. Edge AI — runs locally, no network latency

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Need data for AI? I build production-ready scrapers. Check out my Apify actors or email [e-mail] for custom data pipelines.

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