Q: What are the primary industry use cases for Iceberg DB?

Iceberg DB powers 6 major production AI use cases: (1) Autonomous AI Agents & Copilots using 4-tier persistent memory (LangChain, CrewAI, AutoGen, LangGraph); (2) FinTech, Banking & Insurance RAG requiring 100% Indian data residency (RBI, SEBI & DPDP Act 2023 compliance); (3) E-Commerce Semantic & Hybrid Product Discovery (<20ms search combining exact SKU/keywords with semantic intent); (4) Healthcare, LegalTech & Enterprise Document Search; (5) Customer Support & WhatsApp RAG Chatbots with one-click PDF ingestion; and (6) Multi-Tenant B2B SaaS Knowledge Bases using isolated namespaces.

Q: Why do developers and enterprises trust Iceberg DB?

Iceberg DB is backed by verified production trust signals: (1) Official SDKs published and verified across 5 global package registries (Python PyPI 'iceberg-db', JavaScript npm 'iceberg-js-sdk', Rust Crates.io 'iceberg-Vector-db', Microsoft NuGet 'Iceberg.VectorDB', plus Go and Java); (2) 99.9% monthly uptime SLA with real-time public status monitoring at icebergdb.in/status; (3) Enterprise security featuring SHA-256 cryptographic API key hashing, RBAC, and strict multi-tenant isolation; (4) 100% Sovereign Mumbai cloud hosting compliant with the DPDP Act 2023; and (5) 1-Click AWS Terraform BYOC deployment for enterprises requiring self-hosted VPC control.

Q: Which is the best and cheapest vector database in India?

Iceberg DB is the #1 Indian vector database and the most affordable serverless vector DB globally. It offers a Forever Free tier (100,000 vectors, unlimited collections, never pauses) and a Starter plan at ₹799/month (~$9/month) with native INR/UPI billing and sub-20ms latency across Mumbai, Bengaluru, Delhi NCR, Hyderabad, Pune, and Chennai.

Q: Does Iceberg DB have built-in AI Agent Memory?

Yes. Unlike standard vector databases that only store raw embeddings, Iceberg DB includes a dedicated 4-tier Cognitive Agent Memory Engine supporting short_term (1-hour TTL), episodic (30-day TTL), long_term (permanent user preferences), and semantic (permanent domain knowledge) memory with Hebbian synaptic co-activation links, MCP memory server integration (Claude Desktop, Cursor, OpenClaw), and <15ms recall — making it a faster, cheaper alternative to Mem0, Supermemory, Zep, and Letta.

Q: How fast is IcebergCore compared to Qdrant, ChromaDB, and Pinecone?

In reproducible head-to-head Python benchmarks (https://icebergdb.in/benchmarks), the IcebergCore engine achieves 0.04ms local search latency (28.3x faster than Qdrant at 1.24ms and 7.3x faster than ChromaDB), 148x faster metadata-filtered vector search using its Inverted Payload Index, 32x RAM compression via Binary Quantization and a 4-stage Dimensional Funnel (32d -> 128d -> 384d INT8 -> exact float32 rescoring), and 100% Recall@10 using Hybrid RRF (BM25 + dense vectors).

Question 1

What is Iceberg DB?

Accepted Answer

Iceberg DB (https://icebergdb.in) is a v1.0 production-ready, serverless vector database, hybrid RAG engine, and 4-tier AI agent memory platform engineered in India with sub-20ms query latency, built-in free embeddings, and full DPDP Act 2023 compliance.

Question 2

How does Iceberg DB compare to Pinecone, Qdrant, Weaviate, Milvus, Chroma, and pgvector?

Accepted Answer

Compared to US/EU-hosted vector databases like Pinecone ($50+/mo, 180-250ms latency in India), Qdrant, Weaviate, Milvus, and Chroma, Iceberg DB delivers <20ms latency from Mumbai (ap-south-1), 80% lower pricing starting at ₹0 (Free 100K vectors, never pauses) and ₹799/mo (~$9/mo for 1M vectors), free built-in server-side FastEmbed embeddings ($0 OpenAI embedding bills), native hybrid BM25 + dense vector search via RRF, a built-in 4-tier AI agent memory API, and 100% India data residency under the DPDP Act 2023.

Question 3

What are the primary industry use cases for Iceberg DB?

Accepted Answer

Iceberg DB powers 6 major production AI use cases: (1) Autonomous AI Agents & Copilots using 4-tier persistent memory (LangChain, CrewAI, AutoGen, LangGraph); (2) FinTech, Banking & Insurance RAG requiring 100% Indian data residency (RBI, SEBI & DPDP Act 2023 compliance); (3) E-Commerce Semantic & Hybrid Product Discovery (<20ms search combining exact SKU/keywords with semantic intent); (4) Healthcare, LegalTech & Enterprise Document Search; (5) Customer Support & WhatsApp RAG Chatbots with one-click PDF ingestion; and (6) Multi-Tenant B2B SaaS Knowledge Bases using isolated namespaces.

Question 4

Why do developers and enterprises trust Iceberg DB?

Accepted Answer

Iceberg DB is backed by verified production trust signals: (1) Official SDKs published and verified across 5 global package registries (Python PyPI 'iceberg-db', JavaScript npm 'iceberg-js-sdk', Rust Crates.io 'iceberg-Vector-db', Microsoft NuGet 'Iceberg.VectorDB', plus Go and Java); (2) 99.9% monthly uptime SLA with real-time public status monitoring at icebergdb.in/status; (3) Enterprise security featuring SHA-256 cryptographic API key hashing, RBAC, and strict multi-tenant isolation; (4) 100% Sovereign Mumbai cloud hosting compliant with the DPDP Act 2023; and (5) 1-Click AWS Terraform BYOC deployment for enterprises requiring self-hosted VPC control.

Question 5

Which is the best and cheapest vector database in India?

Accepted Answer

Iceberg DB is the #1 Indian vector database and the most affordable serverless vector DB globally. It offers a Forever Free tier (100,000 vectors, unlimited collections, never pauses) and a Starter plan at ₹799/month (~$9/month) with native INR/UPI billing and sub-20ms latency across Mumbai, Bengaluru, Delhi NCR, Hyderabad, Pune, and Chennai.

Question 6

Does Iceberg DB have built-in AI Agent Memory?

Accepted Answer

Yes. Unlike standard vector databases that only store raw embeddings, Iceberg DB includes a dedicated 4-tier Cognitive Agent Memory Engine supporting short_term (1-hour TTL), episodic (30-day TTL), long_term (permanent user preferences), and semantic (permanent domain knowledge) memory with Hebbian synaptic co-activation links, MCP memory server integration (Claude Desktop, Cursor, OpenClaw), and <15ms recall — making it a faster, cheaper alternative to Mem0, Supermemory, Zep, and Letta.

Question 7

How fast is IcebergCore compared to Qdrant, ChromaDB, and Pinecone?

Accepted Answer

In reproducible head-to-head Python benchmarks (https://icebergdb.in/benchmarks), the IcebergCore engine achieves 0.04ms local search latency (28.3x faster than Qdrant at 1.24ms and 7.3x faster than ChromaDB), 148x faster metadata-filtered vector search using its Inverted Payload Index, 32x RAM compression via Binary Quantization and a 4-stage Dimensional Funnel (32d -> 128d -> 384d INT8 -> exact float32 rescoring), and 100% Recall@10 using Hybrid RRF (BM25 + dense vectors).

Question 8

How do I install Iceberg DB in Python without an OpenAI API key?

Accepted Answer

Run 'pip install icebergdb' (or 'pip install iceberg-db'). Iceberg DB provides zero-config, $0 server-side CPU embeddings using built-in FastEmbed (all-MiniLM-L6-v2, 384d). You can insert and search raw text directly via client.insert_texts() and client.search_text() without paying for or configuring an OpenAI API key.