Amazon Kendra is in maintenance mode. What does that mean for existing customers, what are the alternatives, and how can enterprises migrate to a modern cognitive search platform?
Amazon Kendra has entered maintenance mode, creating an important decision point for organizations using it as their enterprise search foundation.
Existing customers can continue using Kendra, but AWS is no longer developing new features for the service. For enterprises investing in generative AI, RAG, AI agents, and modern self-service experiences, this raises a bigger question:
Is your enterprise search platform ready for the next phase of AI?
This guide explains what Kendra’s maintenance mode means, whether you need to migrate, what to evaluate in a Kendra alternative, and how SearchUnify Cognitive Search can help organizations transition to an AI-ready enterprise search architecture.
TL;DR
- Amazon Kendra is in maintenance mode, meaning existing customers can continue using it, but AWS is no longer actively developing new capabilities for the service.
- You do not need to migrate immediately, but organizations building toward generative AI, RAG, AI agents, or modern enterprise search should evaluate their long-term search strategy now.
- A Kendra migration is more than moving indexed content. Enterprises need to account for content sources, metadata, permissions, relevance configurations, integrations, and search behavior.
- SearchUnify Cognitive Search provides an alternative for enterprises looking to modernize search with federated enterprise knowledge, hybrid and semantic search, intelligent reranking, contextual relevance, generative answers, personalization, analytics, and AI-ready knowledge access.
- The goal is not simply to replace Kendra. It is to move from a maintenance-mode search engine to an evolving cognitive search and enterprise knowledge layer.
Table of Contents
- What Happened to Amazon Kendra?
- What Does Amazon Kendra Maintenance Mode Mean for Existing Customers?
- Do You Need to Migrate From Amazon Kendra?
- What Should You Look for in an Amazon Kendra Alternative?
- SearchUnify Cognitive Search: An Alternative to Amazon Kendra
- Amazon Kendra vs. SearchUnify Cognitive Search
- How to Migrate From Amazon Kendra to SearchUnify
- Frequently Asked Questions About Amazon Kendra Maintenance Mode
- The Kendra Decision Is Bigger Than Search
What Happened to Amazon Kendra?
Amazon Kendra moved to maintenance mode in 2026.
Existing customers can continue using the service, while AWS continues to provide support, bug fixes, and security updates. However, Kendra is no longer available to new customers and AWS is no longer developing new capabilities for the service.
This distinction is important.
Amazon Kendra is not shutting down immediately. It is no longer evolving.
For organizations with stable search requirements, this may not require an immediate migration.
For organizations planning to expand enterprise search into generative AI, conversational search, RAG, AI-powered self-service, and agentic workflows, maintenance mode should trigger a strategic review.
What Does Amazon Kendra Maintenance Mode Mean for Existing Customers?
Kendra customers should consider three implications.
1. Existing deployments can continue operating
There is no need to immediately switch off an existing Kendra implementation.
Organizations can continue using their current deployment while evaluating their options.
2. New capabilities will not come from Kendra
The platform is now in maintenance rather than active feature development.
That means enterprises should be cautious about building increasingly strategic workloads around capabilities that are no longer expanding.
3. Your AI roadmap may outgrow your search platform
Enterprise search is becoming part of a much larger AI architecture.
Search increasingly needs to support:
- Natural-language queries
- Semantic retrieval
- Hybrid search
- Generative answers
- RAG
- AI assistants
- AI agents
- Personalized experiences
- Permission-aware retrieval
- Enterprise knowledge access
The key question is therefore not:
“Does Kendra still work?”
It is:
“Can our search foundation support where our AI strategy is going?”
Do You Need to Migrate From Amazon Kendra?
Not necessarily.
A migration makes sense when the business value of moving outweighs the cost and risk of changing the existing search infrastructure.
You should consider evaluating alternatives if:
- Your organization has an AI-first search roadmap
- You are implementing RAG or generative AI
- Search needs to understand natural-language intent
- Users struggle to find information across multiple repositories
- Your organization has fragmented enterprise knowledge
- You need better search relevance and ranking controls
- You want contextual or generative answers
- You need search analytics to identify knowledge gaps
- You are building AI-agent experiences
- Your current search platform is becoming a technology constraint
If none of these apply, you can continue operating Kendra while creating a longer-term migration plan.
The important thing is to avoid turning a future migration into an emergency migration.
What Should You Look for in an Amazon Kendra Alternative?
Replacing an enterprise search platform should not be a feature-for-feature comparison.
The better question is:
What capabilities should your next enterprise search platform provide that your current architecture cannot?
Here are the capabilities worth evaluating.
1. Federated Enterprise Search
Enterprise knowledge rarely lives in one system.
Your search platform should be able to connect information across:
- CRM systems
- Customer support platforms
- Knowledge bases
- Communities
- Documentation
- CMS platforms
- File repositories
- Learning platforms
- Websites
- Internal applications
A federated search layer allows users to discover relevant information without knowing where it resides.
2. Semantic and Hybrid Search
Keyword matching alone is not enough for modern enterprise search.
Users increasingly search using natural-language questions, incomplete phrases, and conversational intent.
A modern platform should combine:
Keyword retrieval + Semantic understanding + Intelligent ranking
This provides the precision of traditional search while improving the ability to understand meaning and intent.
3. Contextual Relevance
Two users can enter the same query and need different information.
Search relevance should account for factors such as:
- User profile
- Role
- Product
- Geography
- Content type
- Previous behavior
- Permissions
- Query context
This is particularly important for large enterprises where thousands of documents may technically match a query but only a small subset is actually useful to the user.
4. Generative Answers
Enterprise users increasingly expect search to provide an answer rather than simply return ten blue links.
A modern enterprise search platform should be able to retrieve relevant enterprise content and generate grounded answers from that information.
This creates a progression from:
Search → Retrieve → Read
to:
Ask → Retrieve → Understand → Answer
5. Permission-Aware Search
AI-powered search cannot compromise enterprise security.
The search platform should respect source-level permissions and ensure users only retrieve content they are authorized to access.
This becomes even more important when enterprise knowledge is exposed to AI applications.
6. Search Analytics
Search analytics should answer questions such as:
- What are users searching for?
- Which queries return no results?
- Which results are being ignored?
- Where are knowledge gaps emerging?
- Which content performs well?
- Which content is outdated?
- Which queries require repeated refinement?
These insights allow search teams to continuously improve both the search experience and the underlying knowledge base.
7. AI and Agent Readiness
The next search platform should not be designed only for today’s users.
It should be able to become a knowledge layer for:
- AI assistants
- RAG applications
- Agentic workflows
- Support agents
- Customer self-service
- AI tools
- Enterprise copilots
This is where the difference between an enterprise search engine and an enterprise knowledge layer becomes important.
Ready to Modernize Your Enterprise Search?
See how SearchUnify Cognitive Search can help you move beyond traditional search with contextual relevance, generative answers, enterprise knowledge discovery, and AI-ready search.
Explore Cognitive Search
SearchUnify Cognitive Search: An Alternative to Amazon Kendra
For organizations looking to move beyond Kendra, SearchUnify Cognitive Search provides an enterprise search platform designed to unify fragmented knowledge and deliver context-aware search and AI-powered answers.
Rather than treating migration as a simple index-to-index replacement, SearchUnify enables organizations to modernize their search architecture around enterprise knowledge, relevance, analytics, and AI.
Connect Disparate Enterprise Knowledge
SearchUnify Cognitive Search can connect and index content across multiple enterprise repositories.
This allows organizations to create a unified search experience across systems instead of forcing users to search each application independently.
Common enterprise sources include:
- CRM platforms
- Support systems
- Knowledge bases
- Community platforms
- Websites
- Documentation
- Learning systems
- Content repositories
- Internal applications
The result is a centralized discovery layer across distributed enterprise knowledge.
Improve Search With Hybrid Retrieval
SearchUnify Cognitive Search combines keyword and semantic search to address different types of search intent.
Keyword search remains valuable when users need exact matches, product names, error codes, or technical terminology.
Semantic search becomes valuable when users express concepts, questions, or intent differently from the language used in the underlying content.
SearchUnify combines these approaches through its relevance architecture, allowing enterprises to optimize retrieval based on their specific search requirements.
SCORE Framework for Search Relevance
SearchUnify’s SCORE Framework provides additional control over search relevance through:
- Intelligent Reranking
- Semantic Tuning
- Flexible Semantic Ratio Control
This gives search teams more control over how results are ranked rather than relying entirely on a fixed retrieval model.
For organizations migrating from Kendra, this becomes particularly useful when recreating and improving existing relevance behavior.
Instead of asking:
“Can we reproduce our old search results?”
teams can ask:
“Can we preserve what worked and improve what didn’t?”
Contextual and Personalized Search
Enterprise search is not one-size-fits-all.
SearchUnify can personalize search experiences based on user context while respecting access permissions.
This helps surface information that is relevant to the individual user rather than treating every searcher as identical.
For example, an employee searching for a product issue may need internal technical documentation, while a customer searching for the same issue may need an approved troubleshooting article.
The underlying knowledge can be connected, while the experience remains contextual.
Generative AI Answers Grounded in Enterprise Knowledge
SearchUnify Cognitive Search extends traditional retrieval into generative experiences.
Instead of requiring users to open multiple documents and determine the answer themselves, the platform can use retrieved enterprise content to generate contextual responses.
This makes enterprise search useful for:
- Customer self-service
- Employee support
- Technical support
- Product knowledge
- Agent assistance
- Knowledge discovery
The critical distinction is that generative experiences remain grounded in enterprise knowledge rather than relying solely on a general-purpose LLM’s pre-trained knowledge.
Search Analytics and Knowledge Intelligence
Migration should not end when the new search experience goes live.
SearchUnify provides analytics that help teams understand how users interact with enterprise search.
Organizations can analyze:
- Search volume
- Popular queries
- Zero-result searches
- Search refinements
- Result engagement
- Content performance
- Search trends
These insights can identify content gaps and relevance issues that would otherwise remain invisible.
The result is a continuous optimization cycle:
Search → Analyze → Identify Gaps → Improve Knowledge → Improve Search
AI-Ready Enterprise Knowledge
The strategic value of Cognitive Search extends beyond the search box.
As enterprises introduce AI assistants and agents, they need a reliable way to give those systems access to enterprise knowledge.
SearchUnify can serve as the knowledge and retrieval layer connecting enterprise content with AI experiences.
This creates an architecture where the same governed enterprise knowledge can support:
Employees → Customers → Support Agents → AI Assistants → AI Agents
Instead of creating separate knowledge silos for every AI initiative, organizations can build around a common enterprise knowledge foundation.
Amazon Kendra vs. SearchUnify Cognitive Search
| Capability | Amazon Kendra | SearchUnify Cognitive Search |
| Product Direction | Maintenance mode: Existing customers can continue using Kendra, but AWS is no longer developing new capabilities. | Actively evolving cognitive search platform designed around enterprise search, AI-powered answers, relevance, analytics, and AI-ready knowledge. |
| Enterprise Knowledge Discovery | Enterprise search across indexed content within the Kendra ecosystem. | Federated enterprise knowledge layer that brings content from multiple enterprise systems into unified search experiences. |
| Search Approach | Combines traditional and semantic search capabilities. | Hybrid cognitive search combining keyword precision, semantic understanding, and contextual relevance. |
| Relevance Optimization | Relevance is managed through Kendra’s search configuration and ranking capabilities. | SCORE Framework provides intelligent reranking, semantic tuning, and flexible control over semantic vs. keyword relevance. |
| Contextual Relevance | Search can leverage configured attributes and relevance settings. | Context-aware and personalized search designed to surface information based on user context, content, and permissions. |
| Generative Answers | Requires additional AWS services and architecture to build broader generative AI experiences. | Generative answers grounded in enterprise search results, bringing retrieval and answer generation into the search experience. |
| Enterprise Content Sources | Supports AWS-defined data source integrations and custom data source approaches. | Connects disparate enterprise knowledge sources across CRM, support, knowledge, community, documentation, CMS, and other repositories. |
| Search Analytics | Provides search-related metrics and monitoring capabilities. | Search analytics designed for continuous optimization, including query trends, zero-result searches, content performance, and knowledge gaps. |
| Knowledge Gap Identification | Requires analysis and additional workflows to turn search behavior into broader knowledge insights. | Uses search behavior as a knowledge signal to identify content gaps, failed searches, and opportunities for knowledge improvement. |
| AI Assistant & RAG Readiness | Can serve as part of an AWS-based retrieval architecture for generative AI applications. | Designed to serve enterprise knowledge to AI-powered experiences, including RAG and AI assistants. |
| AI Agent Knowledge Access | Requires additional architecture to expose enterprise knowledge to agentic applications. | Enterprise knowledge layer for AI agents, with capabilities such as MCP-based access to enterprise search and knowledge. |
| Migration Strategy | Continuing operation is possible for existing customers while they evaluate their next step. | Supports phased modernization, allowing organizations to benchmark, migrate, validate, and progressively optimize their search experience. |
| Best Fit | Organizations that want to continue operating an existing Kendra deployment without immediate changes. | Organizations looking to modernize enterprise search and establish an AI-ready knowledge foundation. |
The objective is not simply to replace Kendra. It is to move from a maintenance-mode search engine to an evolving enterprise knowledge and cognitive search layer.
How to Migrate From Amazon Kendra to SearchUnify
A Kendra migration should be handled as a phased modernization program.
Phase 1: Discover
Inventory your current environment.
Document:
- Data sources
- Indexes
- Documents
- Metadata
- Permissions
- APIs
- Search applications
- Relevance rules
- High-value queries
- Search analytics
Phase 2: Benchmark
Build a representative query set from actual user behavior.
Include:
- High-volume searches
- Zero-result searches
- Complex queries
- Long-tail queries
- Critical customer queries
- Critical employee queries
Use this benchmark to compare your existing Kendra experience with the proposed SearchUnify experience.
Phase 3: Connect
Connect the required enterprise content sources to SearchUnify.
Map:
Content → Metadata → Permissions → Search Experience
Do not treat content migration as a simple document transfer. Preserve the context surrounding the content.
Phase 4: Tune
Configure relevance using:
- Keyword search
- Semantic search
- Reranking
- Metadata
- Synonyms
- Filters
- Business rules
- Personalization
Use the benchmark queries to validate results.
Phase 5: Validate
Run Kendra and SearchUnify in parallel where practical.
Compare:
- Relevance
- Result quality
- Search coverage
- Zero-result rate
- Latency
- Permission enforcement
- User experience
This provides evidence for the migration decision instead of relying on assumptions.
Phase 6: Roll Out
Move workloads in phases.
Start with a controlled use case, validate performance, then expand to additional search experiences and user groups.
This reduces migration risk while giving teams time to optimize the experience.
Phase 7: Optimize
Once migrated, continue monitoring search behavior.
Use analytics to identify:
- New search trends
- Content gaps
- Failed searches
- Poorly performing content
- Emerging user intent
Your new search environment should become better over time.
Frequently Asked Questions About Amazon Kendra Maintenance Mode
Q. Is Amazon Kendra being discontinued?
Amazon Kendra has moved to maintenance mode. Existing customers can continue using the service, but it is no longer being actively developed with new features.
Q. Do existing Amazon Kendra customers need to migrate immediately?
No. Existing customers can continue using Kendra. However, organizations should evaluate their long-term search and AI roadmap before committing further strategic workloads to a maintenance-mode platform.
Q. What are the alternatives to Amazon Kendra?
Organizations can evaluate AWS-native approaches such as Amazon Bedrock Knowledge Bases, as well as enterprise search platforms such as SearchUnify Cognitive Search, depending on their requirements for search, relevance, enterprise content integration, analytics, generative AI, and agentic experiences.
Q. What is the best Amazon Kendra alternative?
There is no universal “best” alternative. The right platform depends on your content sources, search requirements, security model, relevance needs, AI roadmap, and existing technology stack.
For enterprises looking for a dedicated cognitive search layer across fragmented enterprise knowledge, SearchUnify Cognitive Search is an option worth evaluating.
Q. Can Amazon Kendra be migrated to SearchUnify?
Yes. A migration can be approached as a phased process involving content-source discovery, metadata and permission mapping, query benchmarking, relevance tuning, validation, and rollout.
Q. How long does an Amazon Kendra migration take?
Migration timelines vary based on the number of content sources, volume of content, integrations, permission models, search applications, and customization. A controlled assessment and pilot should be completed before establishing a full migration timeline.
Q. What should enterprises migrate from Amazon Kendra?
Do not migrate only documents.
A successful migration should consider:
Content + Metadata + Permissions + Search Rules + User Behavior + Analytics + Integrations
These elements collectively make up the enterprise search experience.
The Kendra Decision Is Bigger Than Search
Amazon Kendra’s move to maintenance mode does not create an immediate migration emergency.
It creates a strategic decision point.
Enterprises can continue operating Kendra while evaluating what their next search architecture should look like.
The organizations that benefit most will be the ones that use this window to benchmark their current search experience, understand their AI requirements, and establish a migration path before limitations become business constraints.
The next generation of enterprise search is not just about finding documents.
It is about connecting enterprise knowledge, understanding user intent, generating grounded answers, and making that knowledge accessible to both people and AI.
SearchUnify Cognitive Search provides a path from traditional enterprise search to an AI-ready cognitive search architecture.
Ready to evaluate your post-Kendra search strategy?
See how SearchUnify Cognitive Search can help you build a unified, contextual, AI-ready enterprise search experience.
Contact us to know more.
