Amazon S3 vs DynamoDB - Key Differences

Moving data from physical data centers to the cloud represents one of the most critical infrastructure decisions organizations face today. Data professionals often find themselves caught between Amazon S3's object storage capabilities and DynamoDB's NoSQL database features, each presenting compelling advantages for different scenarios. This choice becomes particularly challenging when organizations need both massive-scale storage for analytics workloads and millisecond-latency access for operational applications.

The decision between S3 and DynamoDB isn't just about storage versus database functionality. Modern AWS services have evolved significantly, with S3 introducing transactional capabilities through Apache Iceberg tables and DynamoDB expanding beyond traditional NoSQL limitations with multi-region strong consistency. Understanding these evolving capabilities, along with cost implications and integration patterns, determines whether your data architecture scales efficiently or creates operational bottlenecks.

This comprehensive comparison examines both services through the lens of real-world implementation challenges, helping you navigate the architectural trade-offs that impact long-term system performance and cost optimization.

What Is Amazon S3 and How Does It Work?

Amazon S3 (Simple Storage Service) is a scalable, high-availability cloud-based object-storage solution. It is built to store and manage large datasets across many applications, including websites and IoT devices.

In Amazon S3, you store data in buckets, which are containers that can hold large numbers of objects. Every object in a bucket has a key name that uniquely identifies it. After adding data to a bucket, you can retrieve, download, copy, or delete objects as needed.

S3 has evolved significantly beyond basic object storage to become an intelligent data platform. The service now supports Apache Iceberg tables through S3 Tables, providing transactional capabilities with up to 3× faster query performance. Additionally, S3 Vectors introduces native vector storage for AI workloads, reducing vector storage costs by up to 90% while enabling real-time similarity searches directly within the object store.

Since January 2023, AWS has mandated server-side encryption with AES-256 for all new object uploads, ensuring data protection by default without additional configuration overhead. This security enhancement applies universally across all storage classes while maintaining transparent pricing structures.

Key Features of Amazon S3

• S3 Versioning – Maintain several versions of an object within the same bucket to recover from accidental changes or deletions.
• Strong Consistency – Read-after-write consistency for all objects without affecting performance or cost.
• Access Management – Control access via AWS IAM policies, S3 access points, bucket policies, and ACLs.
• Storage Monitoring – Track the operational health of S3 resources with Amazon CloudWatch and set billing notifications.
• Intelligent Tiering – Automatically moves objects between access tiers based on usage patterns, optimizing costs without performance impact.
• Metadata Intelligence – Provides SQL-queryable metadata for real-time object discovery and analysis across your storage.

What Is Amazon DynamoDB and How Does It Work?

DynamoDB is a fully managed NoSQL key-value database offered by Amazon and used by more than one million customers. It provides fast, scalable performance with minimal latency.

Data is stored in tables as items with attributes. Each item is identified by its primary key: either a partition key or a combination of partition and sort keys.

Modern DynamoDB has expanded beyond traditional NoSQL limitations with breakthrough innovations like multi-region strong consistency capabilities and cost-optimized storage tiers. The service now offers Standard-Infrequent Access storage that reduces costs by 60% for long-tail data while maintaining millisecond latency performance. The November 2024 pricing overhaul reduced on-demand throughput costs by 50%, making serverless applications significantly more cost-effective.

Key Features of DynamoDB

• Auto Scaling – Automatically adjusts read and write capacity as workloads change.
• Multi-Region Replication – Global tables replicate data across multiple AWS Regions with strong consistency options.
• Caching – Amazon DynamoDB Accelerator (DAX) provides fully managed in-memory caching.
• Point-in-Time Recovery (PITR) – Continuous backups with restoration to any second in the preceding 35 days.
• Warm Throughput Monitoring – Provides real-time visibility into instantaneous capacity headroom for proactive scaling.
• Zero-ETL Integration – Direct replication to Amazon Redshift, OpenSearch, and SageMaker without manual pipelines.

What Are the Key Differences Between S3 vs DynamoDB?

Main takeaway: Amazon S3 is optimized for storing and retrieving large amounts of unstructured data, whereas DynamoDB is designed for fast, low-latency access to structured data.

Latency

• S3 – High throughput with low latency for certain storage classes; S3 Express One Zone delivers single-digit millisecond latency for frequently accessed data.
• DynamoDB – Millisecond latency for items under 4 KB; optimized for real-time workloads with DAX providing microsecond caching.

Batch Operations

• S3 – S3 Batch Operations for large-scale object actions across millions of objects.
• DynamoDB – Batch reads/writes via API calls with support for up to 25 items per batch request.

Performance & Speed

• S3 – Up to 3,500 writes/s and 5,500 reads/s per prefix; scales to handle exabyte-scale workloads.
• DynamoDB – Up to 20 million requests/s with minimal admin; faster for small, frequent queries with automatic partitioning.

What Are the AI and Vector Storage Capabilities of S3 and DynamoDB?

The emergence of artificial intelligence and machine learning workloads has fundamentally transformed how organizations approach data storage. Both S3 and DynamoDB have evolved to support AI-native architectures, though each serves distinct roles in the modern AI data stack.

S3 Vectors: Native AI Storage Revolution

S3 Vectors represents a paradigm shift in cloud storage, introducing native vector embedding support directly within object storage infrastructure. This specialized architecture reduces vector storage and query expenses by up to 90% compared to traditional methods while integrating seamlessly with Amazon Bedrock Knowledge Bases, OpenSearch Service, and SageMaker.

The technology enables real-time retrieval-augmented generation (RAG) implementations where vector similarity searches execute directly against the object store. Organizations can now store vector embeddings alongside source data in S3, eliminating complex ETL pipelines while supporting sub-second similarity queries across petabyte-scale datasets.

DynamoDB's Role in AI Workflows

While DynamoDB doesn't provide native vector storage, it serves as the operational backbone for AI applications requiring real-time state management and metadata indexing. DynamoDB's millisecond latency makes it ideal for storing user sessions, conversation context, and model inference results in AI-powered applications.

The service's integration with Amazon Bedrock enables conversational AI applications to maintain session context across millions of concurrent users, while its atomic counters support token usage tracking and rate limiting for AI model inference.

Hybrid AI Architecture Patterns

Modern AI applications increasingly combine both services in complementary roles. S3 Vectors handles embedding storage and similarity searches, while DynamoDB manages application state and metadata. This pattern enables cost-effective AI implementations where bulk vector operations occur in S3 while transactional metadata operations utilize DynamoDB's consistency guarantees.

How Do Modern Integration Patterns Optimize S3 and DynamoDB Together?

Zero-ETL Integration Ecosystem

AWS has fundamentally transformed data integration through zero-ETL capabilities that eliminate manual transformation pipelines. DynamoDB now features bidirectional integration with Amazon Redshift, enabling real-time analytics on operational data without duplication.

Similarly, S3 Tables automatically expose Apache Iceberg metadata to Athena, Redshift, and EMR, creating a unified analytics surface across object stores and databases. This integration reduces data movement costs while providing millisecond-fresh analytics on operational datasets.

Hybrid Data Lakehouse Architecture

Modern data architectures implement S3 as the foundation for data lakehouse patterns while leveraging DynamoDB for operational metadata and real-time access patterns. S3 Tables provide ACID-compliant transactions for analytical workloads, while DynamoDB Streams capture operational changes in real-time.

This architecture enables organizations to maintain single sources of truth in S3 while supporting millisecond query performance through DynamoDB indexes. Data flows from operational systems into DynamoDB for immediate processing, then streams to S3 Tables for historical analysis and machine learning model training.

Event-Driven Data Orchestration

DynamoDB Streams integrate with Kinesis Data Streams to support million-per-second change data capture, positioning DynamoDB as a stateful orchestrator in serverless architectures. These streams can trigger Lambda functions that enrich data before landing in S3, creating sophisticated event-driven pipelines without provisioned infrastructure.

The combination enables real-time data processing where DynamoDB captures operational events, Lambda functions perform enrichment and validation, and S3 serves as the durable storage layer for downstream analytics. This pattern scales automatically while maintaining sub-second processing latency.

How Do the Cost Structures Compare Between S3 and DynamoDB?

Amazon S3 Pricing

S3 pricing depends on:

• Storage class – Standard ($0.023 / GB), Express One Zone (reduced 85% for requests in 2025), Glacier Flexible Retrieval ($0.0036 / GB).
• Data transfer – First 100 GB/month free; afterward $0.09 / GB outbound.
• Storage management – S3 Inventory ($0.0025 per million objects), Object Tagging ($0.01 per 10,000 tags).

S3 Intelligent-Tiering automatically optimizes storage costs by moving objects between access tiers based on usage patterns, eliminating manual lifecycle management while providing up to 95% cost savings for rarely accessed data. The service's March 2025 price reduction for object tagging further reduces data governance costs.

DynamoDB Pricing

DynamoDB pricing highlights:

• On-demand – $0.625 per million write request units, $0.125 per million read request units.
• Provisioned – $0.00065 per write-capacity unit; $0.00013 per read-capacity unit.
• Standard-IA – 60% lower storage costs for infrequently accessed data.

The November 2024 pricing overhaul reduced on-demand throughput costs by 50% while making global table replicated writes 67% cheaper. This restructuring makes DynamoDB more cost-competitive for unpredictable workloads and global applications requiring multi-region deployment.

What Are the Security and Compliance Considerations for S3 vs DynamoDB?

S3 Security Framework

Amazon S3 implements defense-in-depth security through multiple layers of protection. The mandatory server-side encryption introduced in January 2023 ensures all data receives AES-256 encryption by default, eliminating configuration drift risks. S3 supports customer-managed keys through AWS KMS with automatic rotation, while S3 Bucket Keys reduce encryption costs by minimizing KMS requests.

Access control operates through a complex hierarchy of IAM policies, bucket policies, and Access Control Lists (ACLs). The S3 Block Public Access feature provides account-level protection against unintended data exposure, while S3 Access Points enable fine-grained network controls for specific applications or teams.

For compliance-heavy workloads, S3 Object Lock provides Write Once Read Many (WORM) capabilities essential for regulatory retention. Multi-Factor Authentication Delete adds additional protection for permanent object deletions, while comprehensive access logging through CloudTrail and S3 server access logs enables detailed audit trails.

DynamoDB Security Architecture

DynamoDB security integrates tightly with AWS Identity and Access Management (IAM), supporting resource-based policies that control table-level and item-level access. The service provides transparent encryption at rest using AWS-owned keys or customer-managed keys through AWS KMS, with point-in-time recovery maintaining encryption for backup data.

Fine-grained access control enables attribute-level permissions within items, supporting use cases where different user roles require access to specific data fields. VPC endpoints enable private network connectivity without internet gateway exposure, while on-demand backup and point-in-time recovery provide data protection without impacting application performance.

DynamoDB Streams capture data modification events for real-time processing while maintaining security through IAM-based access controls. These streams integrate with AWS Lambda and Kinesis for secure event-driven architectures without exposing sensitive data during transit.

Compliance and Regulatory Alignment

Both services support major compliance frameworks including SOC 2, GDPR, HIPAA, and PCI DSS. S3 provides superior audit capabilities through detailed access logging and object versioning, making it ideal for industries requiring comprehensive data lineage tracking. DynamoDB offers better operational security for transactional systems where data modification auditing focuses on application-level changes rather than storage-level access.

What Are the Common Challenges and Decision-Making Factors When Choosing Between S3 and DynamoDB?

Performance and Scalability Trade-offs

Data professionals frequently encounter challenging decisions around latency requirements versus cost optimization. S3 provides massive throughput for large object storage but exhibits variable latency ranging from 100 milliseconds to several seconds under load. This unpredictability creates challenges for user-facing applications requiring consistent response times below 100 milliseconds.

DynamoDB delivers single-digit millisecond performance for items under 4 KB but faces the notorious hot partition problem where uneven access patterns concentrate load on individual partitions, causing throttling regardless of overall table capacity. This demands careful key design and deep understanding of access patterns during the architecture phase.

Organizations often struggle with capacity planning complexities. DynamoDB requires sophisticated monitoring to balance read/write capacity units against cost, while provisioned mode risks underutilization during low-traffic periods. S3 offers more predictable pricing based on storage volume but becomes expensive for high-frequency small object retrievals where request charges dominate storage costs.

Data Modeling and Structural Constraints

The fundamental architectural differences between S3 and DynamoDB create significant modeling challenges. DynamoDB's 400 KB item limit forces decomposition strategies for large documents, while the prohibition on JOIN operations necessitates denormalized data structures that increase maintenance complexity and storage overhead.

S3 imposes no structural constraints beyond 5 TB object limits, enabling storage of arbitrarily complex files. However, this flexibility comes without native query capabilities, pushing filtering and search logic to client applications or requiring additional services like Athena for SQL-based analytics.

Versioning presents another decision complexity. S3 provides built-in object versioning enabling point-in-time recovery and audit trails, but doubles storage costs for frequently modified objects. DynamoDB lacks native versioning, requiring custom implementations through application logic or Lambda-based change capture systems.

Cost Prediction and Management Difficulties

DynamoDB cost forecasting proves particularly challenging due to request-based pricing that fluctuates with application usage patterns. Write-heavy workloads quickly become expensive, especially for applications requiring frequent updates to large numbers of items. The service's hidden costs through secondary indexes that duplicate storage and throughput expenses often surprise organizations during scaling.

S3 cost optimization requires navigating seven storage classes with varying retrieval costs and lifecycle policies for automated tier transitions. While generally more predictable than DynamoDB, S3 expenses can spike unexpectedly when access patterns shift or small object retrieval patterns increase request volumes.

Both services lack granular cost attribution per application feature, complicating showback and chargeback implementations in multi-tenant environments. Organizations frequently struggle with "bill shock" when usage patterns change unexpectedly, requiring continuous monitoring and optimization to maintain cost predictability.

Integration and Operational Complexity

Modern data architectures increasingly require hybrid implementations leveraging both services, creating operational complexity around data consistency and synchronization. Organizations must design custom integration patterns to maintain data coherence between S3's eventual consistency model and DynamoDB's configurable consistency options.

Security configuration presents ongoing challenges, particularly for S3 where misconfigured bucket policies have caused high-profile data breaches. The complexity of S3's permission system through bucket policies, ACLs, and IAM policies requires continuous attention to prevent configuration drift and unauthorized access.

Monitoring and troubleshooting deficiencies compound operational overhead. DynamoDB metrics in CloudWatch provide limited insight into partition-level performance, while S3 monitoring lacks granular request tracing during performance incidents. Both services exhibit opaque throttling behaviors that require manual analysis to identify root causes and implement optimizations.

How Can You Integrate Your Data with Amazon S3 and DynamoDB Using Airbyte?

Airbyte offers over 600 pre-built connectors to move data between S3, DynamoDB, and hundreds of other sources or destinations without extensive manual work.

Airbyte's Integration Capabilities

Airbyte provides seamless integration with both S3 and DynamoDB through specialized connectors that handle the complexity of data synchronization. The platform's open-source foundation eliminates vendor lock-in while providing enterprise-grade security and governance capabilities essential for production data workflows.

For S3 integration, Airbyte supports multiple file formats including Parquet, JSON, and CSV with automatic directory structuring and compression optimization. The S3 connector handles IAM role delegation, bucket region selection, and lifecycle policy coordination to optimize both performance and cost.

DynamoDB integration includes incremental synchronization using change data capture, table-per-stream isolation, and intelligent error handling with AWS SDK backoff strategies. Airbyte's CDC capabilities ensure only modified items replicate, reducing DynamoDB read capacity consumption while maintaining data freshness.

Advanced Integration Patterns

Airbyte enables sophisticated hybrid architectures where S3 serves as the analytical data lake while DynamoDB handles operational metadata and real-time access patterns. Organizations can implement bidirectional synchronization where operational data flows from DynamoDB to S3 for analytics while processed insights return to DynamoDB for application consumption.

The platform's transformation capabilities through dbt integration enable post-sync normalization, converting nested DynamoDB JSON structures into SQL-queryable tables in S3. This eliminates complex ETL development while maintaining data quality controls and validation rules.

PyAirbyte SDK allows embedding these integration patterns directly into Python applications, enabling programmatic pipeline governance and custom transformation logic. This approach reduces operational overhead while providing fine-grained control over data movement and transformation processes.

Key Integration Features

• Custom connector development with Airbyte's low-code Connector Development Kit
• Data transformation via dbt integration
• Change-data capture (CDC) replicates only new or changed records
• Enterprise-grade security (TLS, SOC 2 Type II, GDPR, HIPAA, ISO-27001)
• PyAirbyte enables Python developers to build data-enabled applications with direct access to Airbyte's connector ecosystem

What Are the Optimal Use Cases for Each Service?

Choose DynamoDB When You Need

• Low-latency, real-time applications requiring sub-10 millisecond response times
• Transactional consistency with ACID compliance for financial or inventory systems
• Serverless architecture with automatic scaling for unpredictable workloads
• Global applications requiring multi-region active-active deployment with strong consistency
• High-velocity data ingestion supporting millions of requests per second
• Operational metadata management for hybrid data architectures

Choose S3 When You Need

• Large-scale object storage for unstructured data including images, videos, and documents
• Data lake architecture supporting analytics and machine learning workloads
• Cost-effective long-term storage with automated tiering across multiple access patterns
• AI and vector storage capabilities for semantic search and retrieval-augmented generation
• Backup and disaster recovery with cross-region replication and versioning
• Compliance-driven archival with Write Once Read Many (WORM) capabilities

Hybrid Architecture Benefits

Modern cloud architectures increasingly combine both services, leveraging S3 for bulk storage and DynamoDB for metadata indexing. This approach provides cost optimization through intelligent data placement while maintaining performance for different access patterns.

Consider implementing S3 Tables for analytical workloads requiring transactional consistency while using DynamoDB for operational data requiring millisecond response times. The combination enables comprehensive data strategies that scale cost-effectively while supporting diverse application requirements.

Evaluate your workload's access patterns, latency requirements, data structure, and budget to select the configuration that aligns best with your objectives. Consider implementing both services in complementary roles to maximize the benefits of AWS's integrated ecosystem while avoiding the limitations of single-service architectures.