Qdrant

Qdrant is a vector database built for storing, indexing, and searching high-dimensional embeddings. It's aimed at teams building semantic search, recommendation systems, and retrieval-augmented generation (RAG) pipelines — developers who need a dedicated vector search layer rather than bolting similarity search onto a general-purpose database.

Pricing

Qdrant offers five tiers: a $0/month Free tier, Standard (usage-based pricing) and Premium (minimum spend required) above that, and Hybrid Cloud / Private Cloud tiers priced on request. Qdrant's pricing page doesn't disclose specific dollar figures for Standard or Premium — only the Free tier has a confirmed price.

API Availability

Qdrant provides an API as a core part of its offering. Teams can integrate vector search directly into applications programmatically — inserting, updating, querying, and managing collections of embeddings without depending on a graphical interface. For a vector database meant to live inside a larger application stack, this kind of API access is table stakes, and it's what makes Qdrant workable for automated pipelines, backend services, and machine learning workflow integration.

Security and Compliance

Qdrant's SOC 2 certification status is not publicly disclosed. Organizations with strict vendor security review requirements — especially those in regulated industries or working through formal procurement checklists — should treat this as an open question and confirm directly with Qdrant during evaluation rather than assume an answer either way.

Feature Set

Qdrant's feature list covers both entry-level and enterprise-oriented capabilities:

• Single Node Cluster (0.5 vCPU / 1GB RAM / 4GB Disk) — the baseline compute and storage allocation available at the free tier, suited to small workloads.
• Cloud Inference With Selected Models — run inference using a defined set of supported models directly through Qdrant's cloud offering, cutting down the need to host inference separately.
• Dedicated Resources & Flexible Scaling — infrastructure that scales up or down and is allocated exclusively, moving past the shared constraints of the free single-node tier.
• Highly Available Setups & Multi-AZ Deployment — clusters distributed across multiple availability zones to cut downtime risk, relevant for production systems with uptime requirements.
• Backup & Disaster Recovery — mechanisms for protecting data against loss and restoring service after failures.
• GPU Indexing & Shard Splitting — GPU-accelerated indexing for performance-sensitive workloads, paired with shard splitting to distribute data across cluster nodes as collections grow.
• Enterprise SSO Authentication — single sign-on support aligned with enterprise identity management practices.
• Private VPC Links — private networking for Qdrant deployments within a virtual private cloud, avoiding exposure over the public internet.

Summary

Qdrant's structure follows a clear path: start free with a resource-limited single node, then grow into infrastructure built for larger deployments — dedicated resources, multi-AZ high availability, backup and disaster recovery, GPU-accelerated indexing, SSO, and private networking. That range covers both individual developers testing vector search concepts on the free tier and organizations running production-grade retrieval systems that need scaling, availability guarantees, and enterprise authentication controls. The one notable gap is the undisclosed SOC 2 status, which matters for teams whose procurement process demands a documented compliance posture before adoption.