mirror of
https://github.com/qdrant/landing_page.git
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Abdon Pijpelink
45f19f30ee
* Add new Scaling landing page under Operations Introduces a Scaling section with vertical vs. horizontal scaling guidance and failover best practices, linking out to detail pages. * Add new Vertical Scaling page Dedicated how-to guidance for resizing existing nodes: when to scale vertically, RAM sizing formulas, and Cloud/self-hosted resize steps. * Add new Horizontal Scaling and Resilience page Covers Raft consensus, the replication model, consistency guarantees, Multi-AZ, and the resilience terminology used elsewhere in the docs. * Move Distributed Deployment under Scaling and update all incoming links Moves distributed_deployment.md into the new scaling/ section, trims its Raft/Replication/Consistency intros into cross-links to the new Horizontal Scaling and Resilience page, adds Multi-AZ and single-replica cross-link callouts in the Cloud docs, rewrites all internal references across ~30 files to the new canonical path instead of relying on aliases, and applies Title Case to Distributed Deployment's headers. * Split Resilience out of Horizontal Scaling and Resilience Adds a dedicated Resilience page covering fault tolerance, Multi-AZ, resilience terminology, and failover best practices (moved from the Scaling landing page). Horizontal Scaling is retitled and scoped to the underlying mechanics: Raft consensus, replication, and consistency. * Reorganize Horizontal Scaling's structure Moves "How Many Qdrant Nodes Should I Run?" from Distributed Deployment into Horizontal Scaling, adds a conceptual Sharding section, and reorders Sharding/Replication/Raft Consensus/Consistency. Moves the remaining conceptual content out of Distributed Deployment: Temporary Node Failure to Resilience, Error Handling folded into Replication, sharding heuristics folded into Sharding, and the Consensus Checkpointing explanation folded into Raft Consensus. * Rename Scaling section to Scaling & Resilience Renames the section and restructures the landing page: the vertical- vs-horizontal decision is now purely about scaling, with a dedicated Resilience section covering fault tolerance through sharding and multi-node deployments. * Polish Vertical Scaling and Resilience page content Reframes Vertical Scaling's "What Not to Do" as positive "Best Practices". Reworks Resilience's structure: moves the uptime/data- integrity terminology into the intro as three distinct aspects of resilience, and renames "How Resilience Works" to "Setting Up a Resilient Qdrant Cluster". * Add diagrams illustrating sharding and replication Adds cluster diagrams to the Sharding and Replication sections on Horizontal Scaling to make the shard/replica layout easier to follow. * Add new Node Failure Recovery page Extracts the node failure recovery scenarios out of Distributed Deployment into their own page, with each bolded sub-header converted to a proper heading, and links updated across Resilience and the Scaling landing page. * Add new Consistency Guarantees page Extracts write consistency factor, read consistency, and write ordering out of Distributed Deployment into their own page, positioned after Distributed Deployment. * Add new "Deploy Behind a Load Balancer" section Explains why a load balancer is needed in front of a multi-node Qdrant cluster: avoiding a single point of failure at the entry point and making sure replicas on every node actually serve reads. * Add new "Rebalancing" section Documents how Qdrant Cloud automatically rebalances shards across nodes, as its own subsection under Sharding. * Rewrite Multi-AZ vs. Replication Factor as Multi-AZ Deployments Defines an availability zone on first use, explains why multi-AZ deployments guard against a zone going down, clarifies that Qdrant Cloud is zone-aware once enabled, and that self-hosted deployments need to place and move replicas across zones manually. * Restructure node-count guidance into One/Two/Three-or-more Node subsections Splits "How Many Qdrant Nodes Should I Run?" into three subsections and drops the "balanced" framing for two nodes: it states plainly that two nodes give more capacity without true high availability. * Add new "Which Configuration Is Right for You?" section Summarizes the one/two/three-or-more node tradeoffs in one place right after the detailed breakdown. * Add explicit _redirects entry for legacy distributed_deployment URL Closes the redirect chain: the existing /guides/ and /operations/ legacy rules both terminate at /documentation/distributed_deployment/, which previously had no explicit _redirects entry and only resolved via the Hugo alias meta-refresh page. * Fix all incoming links to Distributed Deployment and pages under Scaling Repoints two same-page anchors in distributed_deployment.md that broke when Write Ordering moved to Consistency Guarantees, and one link in cloud/create-cluster.md that broke when a Resilience heading was reworded. * Update time-based sharding diagram and restructure section * Fix a couple of broken links * Move 'Consensus Checkpointing' to 'Node Failure Recovery' page
63 lines
3.2 KiB
Markdown
63 lines
3.2 KiB
Markdown
---
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image:
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src: /img/ai-agents-dashboard-cloud.png
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alt: Dashboard cloud
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title: AI Agents with Qdrant
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description: AI agents powered by Qdrant leverage advanced vector search to access and retrieve high-dimensional data in real-time, enabling intelligent, Agentic-RAG driven, multi-step decision-making across dynamic environments.
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cases:
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- id: 0
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title: Multimodal Data Handling
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description: Qdrant enables AI agents to process and retrieve high-dimensional vectors from diverse data types (text, images, audio), supporting more comprehensive decision-making in multimodal environments.
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- id: 1
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title: Adaptive Learning
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description: Qdrant supports continuous learning by enabling efficient vector retrieval and updates, allowing agents to learn and evolve based on real-time interactions and new data points.
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featuresTitle: Qdrant equips AI agents to adapt, learn, and collaborate efficiently.
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features:
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- id: 0
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icon:
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src: /icons/outline/precision-blue.svg
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alt: Precision
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title: Contextual Precision
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description: Qdrant’s hybrid search combines semantic vector search, lexical search, and metadata filtering, enabling AI Agents to retrieve highly relevant and contextually precise information. This enhances decision-making by allowing agents to leverage both meaning-based and keyword-based strategies, ensuring accuracy and relevance for complex queries in dynamic environments.
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link:
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text: Hybrid Search
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url: /articles/hybrid-search/
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- id: 1
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icon:
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src: /icons/outline/multitenancy-blue.svg
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alt: Multitenancy
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title: Multi-Agent Systems
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description: Qdrant’s scalability and multitenancy ensures that multiple agents can collaborate in distributed systems, enabling seamless coordination and communication - key for Agentic RAG workflows.
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link:
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text: Multitenancy
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url: /articles/multitenancy/
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- id: 2
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icon:
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src: /icons/outline/time-blue.svg
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alt: Time
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title: Real Time Decision Making
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description: Qdrant’s real-time, advanced vector search enables AI agents to act instantly on live data, which is crucial for time-sensitive, autonomous decision-making.
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link:
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text: HNSW
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url: /articles/filterable-hnsw/
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- id: 3
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icon:
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src: /icons/outline/server-rack-blue.svg
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alt: Server rack
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title: Optimized CPU Performance for Embedding Processing
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description: Qdrant’s architecture is optimized for high-throughput embedding processing, minimizing CPU load and preventing performance bottlenecks. This enables AI agents in Agentic RAG workflows to execute complex, multi-step tasks efficiently, ensuring smooth operation even at scale.
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link:
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text: Distributed Deployment
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url: /documentation/scaling/distributed_deployment/
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- id: 4
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icon:
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src: /icons/outline/speedometer-blue.svg
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alt: Speedometer
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title: Semantic Cache for Rapid Query Handling
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description: Qdrant enhances AI agent efficiency with semantic caching, which preserves results of queries based on semantic equivalence rather than exact matches. This method reduces query processing times and system load by reusing previously computed answers, essential for high-throughput AI applications.
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link:
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text: Semantic Cache
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url: /articles/semantic-cache-ai-data-retrieval/
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sitemapExclude: true
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---
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