managed search

Managed Solr Service for Enterprise AI Search

Transform Solr into an AI-ready retrieval engine to enable vector and hybrid search for RAG workloads.

AI-Ready Solr Search Infrastructure

Build AI search, vector and hybrid retrieval, RAG and agentic workflows across your enterprise data without the operational overhead.

ENTERPRISE-READY SEARCH

Solr at Scale, Zero Operational Burden

Apache Solr delivers enterprise AI search capabilities for mission-critical search. The challenge with in-house Solr is that it can drain time and resources. A managed Solr service restores operational agility and avoids the lock-in found in alternative platforms. Your teams gain a stable foundation for vector search, hybrid retrieval and RAG pipelines without changing the architecture that already works.

BUILT FOR BUILDERS

Full Solr Capabilities, Fully Managed

Solr enables DenseVectorField, HNSW ANN search, hybrid retrieval, filters, facets and relevance controls. You focus on embeddings, ranking strategy and application logic. SearchStax Managed Search handles provisioning, scaling and Solr upgrades with 24/7 monitoring so you can deliver features faster.

Deploy Solr Where Your LLMs Live

Deploy Solr to AWS, Azure or Google Cloud and keep vector and hybrid retrieval close to your model endpoints. With Managed Search your Solr is available as a multi-cloud architecture without vendor lock-in. Choose private VPC/VNet isolation when grounding LLMs on sensitive internal data.

Solr Capabilities That Power AI Retrieval

Keep Retrieval Close to Your Data

Solr keeps retrieval near your data to reduce pipeline complexity and eliminates unnecessary duplication as AI workloads scale.

Modernize Search without Rebuilding

Solr supports vector fields, hybrid retrieval, flexible ranking and custom app logic without disrupting existing schema or rules.

RAG Depends on Strong Retrieval

Solr provides text search, vector similarity and metadata filtering in one engine, which delivers reliable context.

Enterprise AI Use Cases on Solr

Scenarios that teams consistently ask for

AI Knowledge Search

Surface accurate answers from policies and documents. Solr’s hybrid retrieval improves grounding for copilots.

AI Commerce Discovery

Support semantic queries and product-specific terms in one ranking. Solr’s hybrid scoring matches industry patterns.

Regulated Content Q&A

Run RAG over compliance materials while keeping data inside governed zones. LLM receives only retrieved snippets.

DevOps Assistants

Retrieve runbooks, logs and API docs for troubleshooting. Solr filtering narrows retrieval to the right systems.

Vector Search with Solr

Use vector similarity to find similar cases, products or records. ANN search improves speed at scale.

Agentic Workflows

Agents treat Solr as a retrieval tool: gather context, refine queries, then take actions in downstream systems.

Security and Compliance for AI-Ready Solr

Enterprise controls built into a managed Solr service.

Enterprise Data Safeguards Provided:

Security, certifications and data residency handled within the service.
Vector data, logs and RAG pipelines stay inside your controlled environment.
Backup and recovery protect the Solr retrieval layer from failures.
Governance features support permissioned access and predictable AI behavior.

SearchStax is global, proven and trusted

Our customers make SearchStax the largest Solr footprint in the world

1,400+ Customers

45+ Languages

#1 Site Search software on G2

Review Your AI Retrieval Architecture

See how Managed Search handles Solr so you can focus on building and tuning AI retrieval. We’ll review your AI use case and walk through how Managed Search supports production-ready Solr for AI, without the operational overhead.

Have AI questions for Solr?
We have Solr AI answers.

Can Solr be used securely for RAG with enterprise data?

Yes. In a SearchStax deployment, Solr runs inside controlled environments with SOC 2, ISO 27001, HIPAA-eligible, GDPR and data residency safeguards depending on region. Retrieved snippets stay within your environment before being passed to the LLM, which preserves control over sensitive data. See more Security and Compliance.

Does Solr support vector search for AI and RAG workloads?

Yes. Apache Solr includes DenseVectorField for storing embeddings and supports HNSW approximate nearest-neighbor search for vector similarity. These features allow Solr to act as the retrieval layer for AI search, hybrid retrieval, and RAG patterns.

Does Solr generate embeddings?

No. Embeddings are produced outside Solr using an LLM or embedding model (such as OpenAI, Hugging Face, or a local model). The vectors are then indexed into Solr’s DenseVectorField. SearchStax Managed Solr provides the infrastructure to store, query, and scale those vectors reliably.

How do I blend vector and keyword results?

Solr supports hybrid retrieval, where vector similarity (KNN) and lexical relevance (BM25 or custom ranking) can be combined. Practitioners can rerank or blend the two signals using Solr’s query parsers, boosting rules, or application-side scoring.

Does Solr eliminate the need for a separate vector database?

For many enterprise workloads, yes. Solr provides vector search, hybrid retrieval, filters, facets, and metadata-aware search in one engine. This allows teams to keep operational and AI retrieval data in a single system without introducing an additional vector store layer.

How do I run Solr at scale for AI and RAG workloads?

Managed Search provides feature-rich and on-demand scaling, and automated failover with automated cloud operations, so Solr stays stable as vector search, hybrid retrieval, and RAG workloads grow. The service handles cluster expansion, monitoring, and recovery so teams do not manage scaling themselves.

Does Solr fit in agentic AI workflows?

Agentic systems require a retrieval component to fetch structured and unstructured context before taking actions. Solr provides domain-aware retrieval through vector search, keyword search, and filters, making it the retrieval layer that agents can call for context grounding, knowledge lookup, and multi-step planning.

What if I have multiple environments (e.g., dev, staging, production)?

Managed Search supports multiple environments, allowing you to easily set up, manage, and maintain separate Solr environments for development, testing, staging, and production—all under one solution.

How do I keep Solr stable for production AI use cases?

SearchStax Managed Search provides SLA-backed uptime, on-demand scaling, automated backups, disaster recovery, patching, monitoring, and 24×7 operations, which reduces the operational burden typically required to run Solr at scale for vector and RAG workloads.