landing_page/qdrant-landing/content/ai-agents/ai-agents-features.md at 8743ade87cae8c662dc61cd134c3578f06268d13

mirror of https://github.com/qdrant/landing_page.git synced 2026-10-09 21:08:31 +02:00

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Abdon Pijpelink 45f19f30ee Break up "Distributed Deployment" page into new "Scaling & Resilience" section (#2491 )

* 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

2026-07-16 09:11:58 +02:00

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image, title, description, cases, featuresTitle, features, sitemapExclude

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description

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featuresTitle

features

sitemapExclude

src	alt
/img/ai-agents-dashboard-cloud.png	Dashboard cloud

AI Agents with Qdrant

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.

id	title	description
0	Multimodal Data Handling	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.

id	title	description
1	Adaptive Learning	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.

Qdrant equips AI agents to adapt, learn, and collaborate efficiently.

icon

title

description

link

src	alt
/icons/outline/precision-blue.svg	Precision

Contextual Precision

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.

text	url
Hybrid Search	/articles/hybrid-search/

icon

title

description

link

src	alt
/icons/outline/multitenancy-blue.svg	Multitenancy

Multi-Agent Systems

Qdrant’s scalability and multitenancy ensures that multiple agents can collaborate in distributed systems, enabling seamless coordination and communication - key for Agentic RAG workflows.

text	url
Multitenancy	/articles/multitenancy/

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title

description

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src	alt
/icons/outline/time-blue.svg	Time

Real Time Decision Making

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.

text	url
HNSW	/articles/filterable-hnsw/

icon

title

description

link

src	alt
/icons/outline/server-rack-blue.svg	Server rack

Optimized CPU Performance for Embedding Processing

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.

text	url
Distributed Deployment	/documentation/scaling/distributed_deployment/

icon

title

description

link

src	alt
/icons/outline/speedometer-blue.svg	Speedometer

Semantic Cache for Rapid Query Handling

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.

text	url
Semantic Cache	/articles/semantic-cache-ai-data-retrieval/

true

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