Elastic vs Qdrant
Relationship
Elastic Agent Builder and Qdrant do comparable work on vector search; Qdrant's scale not recorded; ships API service and data service rather than the same layer.
Assembled from the recorded fields for this pair, not hand-checked. The comparison below is read from each company’s own profile.
3 of 9 capabilities — Shares knowledge retrieval, model inference and vector search.
Ludbee capability tags · from the product recordsDifferent layer — Qdrant ships API service and data service, not the same layer.
Ludbee product recordsAligned comparison
Capability overlap
Shared · 3
Not verified for Qdrant · 6
Recorded for Elastic. Qdrant’s product records say nothing either way — a missing record is not a missing capability.
Qdrant has no capability Elastic lacks, among the 3 recorded here.
Products, side by side
Algorithmic pairing — assembled from recorded fields, not hand-checked
Elastic
No shared stack layer with the other side.
Qdrant
No shared stack layer with the other side.
No counterpart
Elastic sells these in a stack layer with no product recorded for Qdrant yet — nothing on the other side to compare them against.
Application
An AI security-operations layer that correlates alerts from a customer's existing security tools, prioritizes threats and guides response workflows without replacing their SIEM.
GenAI- and ML-driven capability inside Elastic Observability that automatically detects, diagnoses and helps resolve operational issues, providing recommended actions for SREs.
Monitoring capability inside Elastic Observability for generative-AI and agentic applications: performance, cost control, guardrail tracking and reliability for GenAI workloads.
Agentic security-operations platform unifying SIEM, XDR and native automation, with autonomous agents handling detection-to-response workflows and purpose-built AI skills for threat hunting, alert analysis and detection engineering; supports multiple LLMs including on-premises models.
Agent platform
A builder for custom AI agents that answer questions and take actions over data indexed in Elasticsearch, using configurable tools, skills and prompts.
An automation engine that runs both scripted steps and AI agents which reason through investigations and execute response actions against data in Elasticsearch.
Platform
A conversational assistant embedded in Kibana that answers natural-language questions against a customer's own indexed data across Elastic's Observability, Security and Search solutions.
Model API
Hosted inference endpoint that runs Elastic-managed LLMs, the ELSER sparse-embedding model and third-party embedding models for ingest, search and chat without provisioning ML nodes in a customer's own Elasticsearch deployment.
Qdrant sells these in a stack layer with no product recorded for Elastic yet — nothing on the other side to compare them against.
API service
Embedding generation run inside Qdrant Cloud so text or images are vectorised and searched in a single API call.
Data service
Apache-2.0 vector database and similarity-search engine written in Rust, run on the user's own infrastructure.
Managed Qdrant clusters on AWS, Google Cloud and Azure, with a free single-node tier and consumption billing above it.
In-process vector search engine for embedded devices, autonomous systems and mobile agents, in beta.
Qdrant's managed control plane over clusters a customer runs in their own Kubernetes, in any cloud, on-premises or at the edge.
Fully on-premise Qdrant deployment for buyers who cannot let a vendor-run control plane touch their environment.
Announced serverless edition of Qdrant Cloud in which a collection is created and queried with no cluster to size or scale.