Qdrant Vector Database

Why Qdrant on MTD Cloud

Most AI prototypes start fast, but they often get stuck when you need repeatable operations: reliable storage, filtering, multi-tenant separation, predictable performance, and a clean deployment path across dev/test/prod. With Qdrant on MTD Cloud, you get a standardized vector storage foundation that’s easy to adopt across teams—without vendor lock-in.

Vector search only becomes valuable in production when you can combine semantic similarity with real-world constraints: tenant boundaries, document sources, access levels, time ranges, product categories, and more. Qdrant is built around vectors plus payload metadata, which makes it a strong fit for production RAG where filtering is non-negotiable.

MTD Cloud adds the missing platform layer: Kubernetes-native operations, consistent deployment patterns, and a predictable way to scale from a single use case to a shared AI foundation.

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What’s included

• Kubernetes-ready deployment pattern

  Deploy Qdrant as a first-class component within your clusters using standard platform workflows.

• Production storage foundations

  Persistent storage and deployment baselines designed for real workloads (not “toy” demos).

• RAG-friendly data model

  Store vectors with payload metadata to support filtered retrieval and traceability.

• Multi-team adoption

  Clean onboarding model that fits existing namespaces, RBAC patterns, and team workflows.

• Integration-ready usage

  Simple API patterns for creating collections, upserting points, and searching top-K results.

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Best-fit use cases

Qdrant support is ideal for:

• RAG assistants grounded in internal policies, runbooks, and technical docs

• Semantic search across knowledge bases, tickets, wikis, and documentation portals

• Multi-tenant AI platforms where each team/client needs data separation

• Recommendations & similarity use cases (content, products, documents, templates)

• AI feature add-ons inside existing apps (search, classification, summarization with citations)

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Quick start

Install Qdrant (illustrative) and verify it’s running:

# Install (example with Helm chart pattern) helm repo add qdrant https://qdrant.github.io/qdrant-helm helm repo update helm upgrade -i qdrant qdrant/qdrant \ --namespace ai-platform --create-namespace kubectl -n ai-platform get pods# Create a collection (define embedding size + distance metric): curl -X PUT "http://qdrant.ai-platform.svc.cluster.local:6333/collections/docs" \ -H "Content-Type: application/json" \ -d '{"vectors": {"size": 1536, "distance": "Cosine"}}'# Create a collection (define embedding size + distance metric): curl -X PUT "http://qdrant.ai-platform.svc.cluster.local:6333/collections/docs" \ -H "Content-Type: application/json" \ -d '{"vectors": {"size": 1536, "distance": "Cosine"}}'# Upsert vectors with metadata payload (source/team/ACL tags, etc.): curl -X PUT "http://qdrant.ai-platform.svc.cluster.local:6333/collections/docs/points?wait=true" \ -H "Content-Type: application/json" \ -d '{ "points": [ { "id": 1, "vector": [0.1, 0.2, 0.3, 0.4], "payload": { "source": "policy", "team": "security", "access": "internal" } } ] }'# Run a filtered similarity search (top-K with metadata constraints): curl -X POST "http://qdrant.ai-platform.svc.cluster.local:6333/collections/docs/points/search" \ -H "Content-Type: application/json" \ -d '{ "vector": [0.1, 0.2, 0.3, 0.4], "limit": 5, "filter": { "must": [ { "key": "team", "match": { "value": "security" } }, { "key": "access", "match": { "value": "internal" } } ] } }'

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Conclusion

With Qdrant on MTD Cloud, teams can move from “RAG demos” to production-grade AI features using an open-source, Kubernetes-native vector database that supports the realities of enterprise data: filtering, governance, and multi-team adoption.

Instead of each team building their own vector storage stack, you now have a shared, standardised foundation for semantic search and retrieval, accelerating delivery while keeping operations and data control consistent across environments.