If you’re running Apache Solr for search, now is a great time to migrate to Amazon OpenSearch Service. Amazon OpenSearch Serverless offers a modern, managed destination that greatly reduces operational overhead. Migration Assistant for Amazon OpenSearch Service now supports Apache Solr sources from versions 6.x through 9.x. Migration Assistant now includes an AI assistant that you can drive from your preferred AI tools. The assistant walks you through the migration, providing a detailed report with timelines, blockers, schema and query translation.
In this post, you will learn why now is the time to take advantage of the ease of operations and native AI capabilities of OpenSearch Serverless, and migrate from Solr.
The challenge of managing Solr
Many organizations have Solr deployments that have run for years, carrying accumulated technical debt: older versions, custom patches, and operational processes originating with engineers who have left. Running Solr in production requires ongoing investment in upgrades, security patches, monitoring, failure recovery, and capacity planning. The operational burden compounds as your deployment ages and the engineers who built it move on, leaving the remaining team with a system that is difficult to modify safely.
Meanwhile, search has evolved. Users interact through chat interfaces and AI agents that synthesize information on their behalf. These patterns require semantic understanding, hybrid retrieval, and agentic capabilities like memory and Model Context Protocol (MCP) support. OpenSearch is an open source software suite for search, analytics, and observability, licensed under the Apache License V2.0 and based on Apache Lucene. OpenSearch provides a broad and deep set of vector capabilities for AI workloads: multiple engines (Facebook AI Similarity Search (FAISS) and Lucene), multiple algorithms (Hierarchical Navigable Small World (HNSW) and Inverted File (IVF)), quantization for cost management, hybrid search with score normalization, neural sparse search, and a connector framework. You can use the OpenSearch connector framework to connect to your own models hosted in services such as Amazon Bedrock and Amazon SageMaker. With these capabilities, you can build chat-based search, power AI agents, and implement Retrieval Augmented Generation (RAG) workflows on your search engine.
Technical debt in custom patches and undocumented procedures can make shipping new features on Solr slow and risky. OpenSearch Serverless provides a modern engine with all the capabilities just mentioned, along with automatic scaling, and minimal infrastructure to maintain. Even if you remain focused on traditional search, OpenSearch Serverless delivers relevant results with less operational effort.
OpenSearch Serverless simplifies search
Amazon OpenSearch Serverless alleviates infrastructure provisioning, capacity planning, and data lifecycle management. You create a collection, send data, and run queries. OpenSearch Serverless automatically matches compute to your workload, independently scaling up indexing and search compute during spikes, and scaling compute to zero when idle. You pay only for storage when no requests are being processed. For variable traffic patterns, OpenSearch Serverless can cost up to 60% less than an OpenSearch Service domain provisioned for peak.
The vector engine supports HNSW and IVF with FAISS and Lucene, plus quantization techniques to manage cost as your collection grows. With Automatic Semantic Enrichment, you can enable a sparse model with a single setting to augment text with vectors and improve relevance without building an embedding pipeline. GPU acceleration reduces HNSW index build times from hours to minutes. OpenSearch Serverless also supports agents and RAG workflows.
If your workload requires tight control over infrastructure: specific instance types, custom plugin configurations, or extreme scale beyond what serverless deployments target, Amazon OpenSearch Service domains are the alternative destination. Domains give you full control over the cluster with one-click provisioning, patching, and backups. Migration Assistant supports both destinations, so you can pick the deployment model that fits your workload without changing your migration approach.
Migration Assistant for Amazon OpenSearch Service
Migration Assistant has helped customers move from self-managed Elasticsearch and OpenSearch to OpenSearch Service since December 2023. It now supports Solr sources. Migration Assistant now includes an AI-assisted experience that you can drive from your preferred AI tools, like Kiro, Claude Code, and others, to plan a migration, deploy the necessary infrastructure, and execute both historical and live traffic migration.
Historically, migrations required weeks of planning and assessments before any data movement could begin, and the process was often error-prone. The AI-assisted experience provides an agent-guided workflow that helps you structure, execute, and validate your migration faster and more reliably. You can use Migration Assistant itself to do your assessment, or connect with the skills in the AWS Model Context Protocol (MCP) server. Migration Assistant also now supports live traffic capture and replay for Solr, so you can validate the new environment with real workloads before cutting over.
Migration Assistant supports migrations to OpenSearch Serverless and OpenSearch Service domains from a range of Solr, Elasticsearch, and OpenSearch versions. It’s open source, so you can use it for migrating to OpenSearch Serverless in all commercial AWS Regions and AWS GovCloud (US) Regions where OpenSearch Serverless is available.
Migrate now
OpenSearch Serverless gives you vector, hybrid, and semantic search, automatic scaling, scale-to-zero compute, and zero operational overhead. Migration Assistant for Amazon OpenSearch Service provides the AI-assisted tools to get there: plan with the agent, deploy the infrastructure, run a backfill from your Solr backups, and validate with live traffic capture and replay before you cut over. Start by pointing your AI tool of choice to Migration Assistant to get a plan, timeline, and cost estimate.
For more information, see the Amazon OpenSearch Service documentation and the Migration Assistant for Amazon OpenSearch Service documentation.
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