AWS Kendra 2026: Setup to Enterprise Search

About the Author

Murugesh R is an AWS DevOps Engineer at AgileSoftLabs, specializing in cloud infrastructure, automation, and continuous integration/deployment pipelines to deliver reliable and scalable solutions.

Key Takeaways

• AWS Kendra is a fully managed intelligent search service using NLP to understand query intent and extract precise answers from enterprise documents.
• NLP-powered search differs from keyword matching by semantic understanding—delivers specific answers vs document lists.
• Cost structure: Developer Edition $810/month (10K docs, 4K queries/day); Enterprise Edition $1,008/month (100K docs, 8K queries/day).
• 14+ native connectors including S3, SharePoint, Salesforce, ServiceNow, Confluence, OneDrive, and Google Drive—with automatic ACL sync.
• ROI metrics: 40-60% search time reduction, 30-50% fewer support tickets, 70-80% less document review time for legal cases.
• RAG architecture: Kendra retrieval + Amazon Bedrock/Claude for grounded, hallucination-free generative AI responses.
• Production-ready: SOC 1/2/3, ISO 27001, HIPAA eligible, PCI DSS, FedRAMP authorized—regulated industries compliant.
• Best applications: Natural language Q&A over enterprise docs; not for e-commerce product search or real-time log analytics.

Introduction: The Enterprise Search Problem

The average enterprise employee spends 3.6 hours per day searching for information, according to McKinsey research. That's 19.8% of their work week lost to searching — not doing. For a company with 500 knowledge workers at an average salary of $80,000, that's roughly $14.4 million per year in lost productivity from inefficient information retrieval.

The root problem isn't that organizations lack search tools. Most enterprises already run Elasticsearch, SharePoint Search, or Google Workspace search. The problem is that traditional keyword-based search was designed for a different era. When an employee asks, "What is our parental leave policy for employees in California?", keyword search returns 200 documents containing the words "leave," "policy," and "California" — ranked by term frequency. The employee then manually opens documents, scans for the relevant paragraph, and hopes they found the right version.

AWS Kendra solves this by replacing keyword matching with natural language understanding. Instead of returning a list of documents, Kendra reads the question, understands the intent, searches across all connected data sources, and returns the specific answer — extracted from the correct document, with the source highlighted. The employee gets "Employees in California receive 12 weeks of paid parental leave under Policy HR-2024-07" in seconds.

At AgileSoftLabs, we've delivered 60+ enterprise search and AI implementations across healthcare, finance, legal, and technology sectors. This guide covers everything you need to evaluate, implement, and optimize AWS Kendra for your organization — based on real production experience. Explore our AI & Machine Learning solutions to see how we help enterprises implement intelligent search systems.

What Is AWS Kendra? Architecture and Core Concepts

AWS Kendra is a fully managed intelligent search service powered by machine learning. It uses natural language processing (NLP) to understand the meaning behind queries — not just match keywords — and returns precise answers extracted from your organization's documents. Kendra is part of the AWS AI/ML service family, alongside Amazon Comprehend, Lex, and Textract.

How Kendra Processes a Query

Kendra returns three types of results for every query: factoid answers (direct answers extracted from documents), document excerpts (relevant passages with the answer highlighted), and document rankings (traditional ranked list as a fallback). This layered approach ensures that users receive the most precise answer possible, accompanied by source documents for verification.

Core Components

• Index: The central search repository where Kendra stores processed document data, embeddings, and metadata
• Data Source Connectors: Pre-built integrations that sync documents from enterprise platforms (14+ native connectors)
• Document Enrichment: Pre-processing pipeline using Lambda functions for transformation, classification, or augmentation
• Experience Builder: No-code interface for deploying search UIs directly from the Kendra console

For businesses seeking to integrate AI-powered search capabilities across their knowledge repositories, our custom software development services offer a comprehensive solution, spanning from architecture design to production deployment.

AWS Kendra vs OpenSearch vs Elasticsearch vs Azure Cognitive Search

Choosing the right enterprise search solution depends on your use case, query patterns, and infrastructure requirements. Here's a detailed comparison based on production deployments:

When to Use Each Service

Choose Kendra when your users ask questions in natural language and need direct answers (HR portal, customer support, internal knowledge base). Choose OpenSearch/Elasticsearch when you need high-volume keyword search with complex filtering, aggregations, and sub-100ms latency (product catalog, log analytics, application monitoring). Choose Azure Cognitive Search when your organization is Microsoft-centric and your data lives in SharePoint, Azure Blob Storage, and SQL Server.

Many production architectures use Kendra + OpenSearch together — Kendra for employee Q&A, OpenSearch for application search. Review our case studies to see hybrid search implementations in production environments.

Core Features That Make AWS Kendra Different

1. Natural Language Processing (NLP) That Understands Intent

AWS Kendra uses deep learning models trained on millions of enterprise documents to understand query intent — not just match keywords. This means it handles synonyms, context, and phrasing variations automatically. The query "How do I request PTO?" and "What's the process for taking vacation days?" return the same answer because Kendra understands they mean the same thing.

Kendra classifies each query into one of four types:

• Factoid questions ("What is...?", "How many...?") → Returns a precise answer extracted from a document
• Descriptive questions ("Explain the process for...") → Returns a relevant passage with context
• Keyword queries ("expense report template") → Returns ranked documents matching the concept
• Navigational queries ("HR benefits page") → Returns the most relevant single document

2. Relevance Tuning Without Code Changes

Kendra provides a relevance tuning console where administrators can boost or demote results based on document attributes — without writing code or retraining models. You can configure:

• Field boosting: Boost documents where the query matches the title (higher relevance than body matches)
• Freshness ranking: Prioritize recently updated documents over older versions
• Source boosting: Rank official policy documents higher than informal Slack conversations
• Custom attributes: Boost by department, document type, author, approval status, or any custom metadata

3. 14+ Native Data Source Connectors

Kendra connects to enterprise platforms out of the box — no custom ETL pipelines needed for common sources:

4. Access Control That Respects Source Permissions

AWS Kendra inherits access permissions from connected data sources. If a user doesn't have access to a document in SharePoint, they won't see it in Kendra results — even if the document contains the best answer. This is critical for enterprises where HR documents, financial reports, and legal contracts must remain restricted to authorized personnel.

Important Note: Kendra syncs ACLs during data source sync operations, not in real-time. For sensitive environments, configure frequent incremental syncs (every 1-2 hours) and implement a manual sync trigger for immediate permission revocations.

Real-World Use Cases: Where AWS Kendra Delivers Measurable ROI

Enterprise search impacts every department differently. Here are the use cases where Kendra delivers the highest return on investment:

From Our Experience: Kendra for a Healthcare Knowledge Portal

We implemented AWS Kendra for a healthcare organization with 3,000+ employees across 12 facilities. Their knowledge base included 45,000+ documents across SharePoint, Confluence, and S3 — clinical protocols, compliance procedures, HR policies, and training materials.

Results after implementation:

• Average search time dropped from 8 minutes to 45 seconds
• First-result accuracy improved from 23% to 78%
• IT helpdesk tickets decreased by 42% in the first quarter
• Deployment cost: ~$4,200/month (paid for itself within 6 weeks from helpdesk ticket reduction)

For similar implementations in healthcare and other regulated industries, explore our cloud development services that provide comprehensive AWS architecture and deployment support.

Step-by-Step: Setting Up AWS Kendra for Enterprise Search

Here's the complete setup process for deploying AWS Kendra — from index creation through first query.

Step 1: Create a Kendra Index

// Create a Kendra index using AWS SDK v3
import { KendraClient, CreateIndexCommand } from "@aws-sdk/client-kendra";

const client = new KendraClient({ region: "us-east-1" });

const createIndex = async () => {
  const command = new CreateIndexCommand({
    Name: "enterprise-knowledge-base",
    Edition: "ENTERPRISE_EDITION",  // or "DEVELOPER_EDITION" for testing
    RoleArn: "arn:aws:iam::123456789012:role/KendraIndexRole",
    Description: "Company-wide knowledge search index",
    UserContextPolicy: "USER_TOKEN", // Enable ACL-based filtering
  });

  const response = await client.send(command);
  console.log("Index ID:", response.Id);
  return response.Id;
};

Step 2: Connect Data Sources

// Connect S3 as a data source
import { CreateDataSourceCommand } from "@aws-sdk/client-kendra";

const connectS3 = async (indexId) => {
  const command = new CreateDataSourceCommand({
    IndexId: indexId,
    Name: "company-documents-s3",
    Type: "S3",
    Configuration: {
      S3Configuration: {
        BucketName: "company-knowledge-base",
        InclusionPrefixes: ["policies/", "procedures/", "training/"],
        ExclusionPatterns: ["*.tmp", "*.bak", "archive/*"]
      }
    },
    RoleArn: "arn:aws:iam::123456789012:role/KendraS3DataSourceRole",
    Schedule: "cron(0 */2 * * ? *)"  // Sync every 2 hours
  });

  const response = await client.send(command);
  console.log("Data Source ID:", response.Id);
};

Step 3: Query the Index

// Query Kendra with natural language
import { QueryCommand } from "@aws-sdk/client-kendra";

const searchKendra = async (indexId, question) => {
  const command = new QueryCommand({
    IndexId: indexId,
    QueryText: question,
    PageSize: 10
  });

  const response = await client.send(command);

  // Process results by type
  response.ResultItems.forEach(item => {
    if (item.Type === "ANSWER") {
      console.log("Direct Answer:", item.DocumentExcerpt.Text);
      console.log("Source:", item.DocumentURI);
    } else if (item.Type === "DOCUMENT") {
      console.log("Document:", item.DocumentTitle.Text);
      console.log("Excerpt:", item.DocumentExcerpt.Text);
    }
    console.log("Relevance:", item.ScoreAttributes.ScoreConfidence);
  });
};

Critical Setup Consideration: IAM Role Configuration

Kendra requires separate IAM roles for the index, each data source, and FAQ uploads. The most frequent deployment failure is permission errors because teams use a single role for everything. Always use least-privilege roles — never attach AdministratorAccess to Kendra roles in production.

AWS Kendra Pricing: What It Actually Costs (2026)

Understanding the cost structure prevents surprises and helps right-size your deployment.

Real-World Cost Estimates

• Small team (50 users, 10,000 docs, ~1,000 queries/day): ~$810/month (Developer Edition)
• Mid-size org (500 users, 50,000 docs, 5,000 queries/day): ~$2,500-4,000/month (Enterprise Edition + additional capacity)
• Large enterprise (5,000+ users, 500,000 docs, 20,000 queries/day): ~$8,000-15,000/month (Enterprise Edition + additional capacity units)

Cost Optimization Strategies

Three strategies that consistently reduce Kendra costs by 30-50%:

1. Start with Developer Edition — it handles 10,000 documents and 4,000 queries/day, sufficient for most proof-of-concept deployments
2. Use FAQ indexing aggressively — upload your top 200-500 frequently asked questions as structured FAQ data (cheaper to serve, higher accuracy)
3. Implement caching for repeated queries — a Redis cache layer with 15-minute TTL reduces query volume significantly

For comprehensive cost analysis and optimization strategies, explore our products portfolio featuring AI-powered solutions for enterprise knowledge management.

AWS Kendra + Generative AI: The RAG Architecture

One of the most powerful patterns emerging in 2026 is using AWS Kendra as the retrieval layer in Retrieval-Augmented Generation (RAG) architectures. Instead of letting an LLM generate answers from its training data (risking hallucination), you use Kendra to retrieve relevant documents first, then pass those documents to an LLM for natural language answer synthesis.

This architecture — Kendra for retrieval + Amazon Bedrock (Claude, Llama, or Titan) for generation — gives you the accuracy of Kendra's document retrieval with the natural language fluency of an LLM, while keeping answers grounded in your actual documents.

When NOT to Use AWS Kendra: Honest Limitations

Kendra is excellent for natural language question-answering over enterprise documents — but it's not the right tool for every search use case. Here's where AWS Kendra is the wrong choice:

• E-commerce product search: Kendra is designed for document search, not product catalog browsing with faceted filtering and price ranges. Use OpenSearch or Algolia.
• Log analytics and monitoring: Kendra's 1-3 second latency and per-query pricing make it unsuitable for high-volume log analysis. OpenSearch is purpose-built for this.
• Real-time data: Kendra syncs data periodically (minimum every 1-2 hours). If you need search over data that changes every minute, use streaming-based indexing.
• Very high query volume (>50,000 queries/day): Cost becomes prohibitive at this scale. Consider OpenSearch or a hybrid architecture.
• Highly structured data querying: If your "search" is really SQL queries against structured data, use Amazon Athena or RDS.
• Multi-cloud mandates: Kendra is AWS-only. For cloud-agnostic solutions, consider open-source alternatives.

The Hybrid Pattern We Recommend

For most enterprises, the highest-value architecture is Kendra for knowledge Q&A + OpenSearch for application search. Kendra handles the "What is our vacation policy?" questions that employees ask in natural language. OpenSearch handles high-volume, latency-sensitive application search. This hybrid approach optimizes cost and user experience simultaneously.

Security and Compliance for Enterprise Deployments

Enterprise search systems index your most sensitive data. AWS Kendra provides enterprise-grade security:

• Encryption at rest: All index data encrypted using AWS KMS — customer-managed CMKs or AWS-managed keys
• Encryption in transit: TLS 1.2+ for all API communications
• Access Control Lists (ACLs): Inherits permissions from source systems (SharePoint, Salesforce, ServiceNow)
• VPC deployment: VPC endpoints for private network access
• IAM integration: Fine-grained API access control using IAM policies
• Audit logging: CloudTrail integration captures every API call

Compliance Certifications

AWS Kendra is covered under AWS's compliance programs, including: SOC 1/2/3, ISO 27001, ISO 27017, ISO 27018, HIPAA eligible, PCI DSS Level 1, FedRAMP Authorized (Moderate), GDPR compliant, and CCPA compliant. For healthcare organizations, Kendra can be included in your Business Associate Agreement (BAA) with AWS.

Conclusion: Transform Enterprise Search with AI-Powered Intelligence

AWS Kendra transforms enterprise search from frustrating keyword matching to intelligent question-answering. By combining machine learning with natural language understanding, Kendra enables employees to find answers in seconds instead of minutes, reduces support ticket volume by 30-50%, and creates a unified search experience across all your organization's knowledge repositories.

Start with the Highest-Impact Approach

Deploy Developer Edition, connect your top 2-3 data sources (typically SharePoint + S3 + Confluence), upload your 200 most frequently asked questions as structured FAQ data, and configure basic relevance tuning (title boosting + freshness ranking). This minimal deployment — achievable in 1-2 weeks — will demonstrate the value of intelligent enterprise search to stakeholders.

Ready to Implement Intelligent Enterprise Search?

Expert AWS Kendra Implementation and AI Search Solutions

At AgileSoftLabs, we've deployed AI-powered search solutions for enterprises across healthcare, finance, legal, and technology — from proof-of-concept to production serving thousands of daily users. Our team handles the full implementation lifecycle: architecture design, data source integration, relevance tuning, security configuration, and RAG integration with generative AI.

What We Deliver:

• AWS Kendra implementation — index setup, connector configuration, ACL mapping, and FAQ indexing
• RAG architecture — Kendra + Amazon Bedrock for grounded, hallucination-free knowledge assistants
• Hybrid search architecture — Kendra for Q&A + OpenSearch for application search
• Custom search UI development — React/Next.js frontends with real-time search, filters, and analytics
• Relevance tuning and optimization — field boosting, freshness ranking, synonym management

Get a Free Search Architecture Consultation

Contact our team for a complimentary search architecture review and discover how AWS Kendra can transform your organization's knowledge management. Your employees are spending 3.6 hours a day searching — Kendra can give that time back.

For more insights on implementing AI-powered solutions, visit our blog for the latest best practices, case studies, and technical guides on enterprise AI, machine learning, and intelligent automation.

Frequently Asked Questions

1. What is AWS Kendra and its key features?

Fully managed ML service for intelligent enterprise search over unstructured/semi-structured data. Features: Semantic understanding (no keywords needed), auto-ML relevance, 20+ connectors (S3, SharePoint, Confluence), FAQ enrichment, Intelligent Ranking reranking, analytics dashboard, VPC/private endpoints.

2. How to set up AWS Kendra index step-by-step?

1. Console: Kendra > Create index > Choose Developer ($0.32/hr) or Enterprise ($1.4/hr) > IAM role (auto-create or custom).
2. Set KMS encryption + edition capacity.
3. Token type (No access/JWT/OpenID Connect) + IAM Identity Center.
4. Review/create (5-10 min). AWS CLI: aws kendra create-index.

3. What's AWS Kendra pricing in 2026?

Hourly: Developer $0.37/hr (~$266/mo, 20K docs/8K queries/day); Enterprise $2.30/hr (~$1,657/mo, 100K docs/unlimited). Storage $1.125/100K docs/mo. Query units $0.00039/additional. Free tier: 750 dev hours/mo first 12 months. Use AWS Pricing Calculator.

4. How does Kendra Intelligent Ranking pricing work?

ML reranker: $0.10/hr base + $0.0001/query unit (beyond 1K/day). Boosts relevance 20-30% for complex queries; toggle per index.

5. What data sources does Kendra support?

Native: S3, RDS (PostgreSQL/MySQL), Aurora, DocumentDB, JDBC, Salesforce, SharePoint, OneDrive, Confluence, Jira, Box, Dropbox, Google Drive, Gmail, Slack. Custom: Lambda function. Max 50 sources/index.

6. How to add S3 data source to Kendra?

Console: Indexes > Data sources > Add S3 > Select bucket/folder, IAM role (kendra:S3Read), sync mode (Full+Delta), schedule (hourly/daily), inclusion/exclusion regex, document metadata extraction. CLI: aws kendra create-data-source.

7. What IAM roles/policies for Kendra setup?

Service role: AmazonKendra-ForIndex (index mgmt), AmazonKendra-S3Connector (data access). User policy examples: kendra:CreateIndex, kendra:DescribeIndex, kendra:Query with resource ARNs. Attach to groups.

8. How to build custom search UI for Kendra?

1. Get Query API endpoint from console.
2. Frontend (React): Fetch JWT token, POST to /api/search with QueryText.
3. Display results (title/snippet). Embed Developer iframe for quickstart. Sample code on AWS GitHub.

9. What security best practices for Kendra?

- Encrypt data/index (KMS customer-managed).
- VPC endpoints (no public internet). - Token-based auth (principal groups).
- Monitor CloudTrail, GuardDuty.
- Least privilege IAM.
- Rotate credentials via Secrets Manager.

10. How to configure token-based access control?

JWT claims: cognito:groups or custom roles. IAM Identity Center syncs users/groups. Policy: {"Effect":"Allow","Action":"kendra:Query","Resource":"index-arn","Condition":{"StringEquals":{"kendra:UserGroups":["sales"]}}}.

11. What's Kendra sync frequency and costs?

Schedules: Continuous (Enterprise only), hourly, daily, weekly. Costs: Free base syncs; $0.0001/doc processed beyond storage allowance. Delta detects changes via ETag/modified date.

12. How to test and optimize Kendra search?

Console test queries; Experience Builder for UI. Analytics: Top queries, zero results, click feedback. Tune: Synonyms, thesaurus, FAQ dataset, relevance thresholds, stop words.

13. Kendra Developer vs Enterprise edition?

Developer: POC/low-volume ($0.37/hr, 750 free hrs); no custom docs, 50 concurrent queries. Enterprise: Production ($2.30+/hr scalable); custom Lambda connectors, 1K+ queries, multi-region.