Amazon Web Services offers two distinct services for handling streaming data: Kinesis Data Streams and Kinesis Data Firehose. While both process real-time data and share the Kinesis brand, they serve fundamentally different purposes and operate on different architectural principles. Choosing incorrectly between them can lead to unnecessary complexity, higher costs, or architectural limitations that force expensive refactoring down the line. Understanding the technical differences, operational characteristics, and ideal use cases for each service ensures you select the right foundation for your streaming data infrastructure from the start.
Understanding the Fundamental Architectural Differences
The most critical distinction between Kinesis Data Streams and Firehose lies in what each service actually does with your data. Kinesis Data Streams is a durable, scalable buffer that temporarily stores streaming data for consumption by multiple independent applications. It doesn’t inherently move data anywhere—instead, it holds records for a retention period (1 to 365 days) while various consumers read and process them at their own pace. Think of it as a distributed queue or message bus specifically optimized for high-throughput streaming workloads.
Kinesis Data Firehose, by contrast, is a delivery service. It takes streaming data from sources and reliably delivers it to specific destinations—primarily S3, Redshift, OpenSearch, or third-party services like Splunk and Datadog. Firehose focuses on getting data from point A to point B with minimal operational overhead. It handles batching, compression, encryption, and retry logic automatically, eliminating the infrastructure management that comes with building your own delivery pipelines.
This fundamental difference cascades into everything else about how these services work. Data Streams gives you complete control over how data is processed but requires you to build and maintain consumer applications. Firehose abstracts away consumer complexity but limits you to its supported destinations and processing model. Neither approach is universally better—the right choice depends entirely on your specific use case and architectural requirements.
Data Retention and Replay Capabilities
Kinesis Data Streams retains data for a configurable period, from 24 hours up to 365 days. During this retention window, any number of consumers can read the stream, and individual consumers can replay data by resetting their position in the stream. This capability proves invaluable when you need to reprocess data due to bugs in consumer logic, when adding new analytics pipelines that need historical context, or when performing backfill operations for new consumers.
Consider a scenario where you’re streaming application logs to Kinesis Data Streams. Initially, you have a single consumer writing logs to S3. Later, you decide to add real-time alerting based on error patterns. With Data Streams, you can deploy the new alerting consumer and have it process the last seven days of logs to establish baseline patterns before switching to live monitoring. This temporal flexibility simply doesn’t exist with Firehose.
Firehose has no retention concept—it’s a pure delivery pipeline. Once Firehose delivers data to its destination, the service has no further relationship with that data. If you need to reprocess or replay data, you must read it back from the destination storage. This works for some use cases but creates challenges for others, particularly when destinations don’t support efficient time-based querying or when you need multiple independent processing paths over the same data.
Consumer Model and Processing Flexibility
Kinesis Data Streams supports multiple concurrent consumers, each maintaining independent processing positions in the stream. You might have one consumer calculating real-time analytics, another populating a search index, a third feeding a machine learning pipeline, and a fourth archiving data to S3—all reading from the same stream simultaneously without interfering with each other. Each consumer can use different processing frameworks (Lambda, Kinesis Client Library, Apache Flink, Spark Streaming) based on its specific requirements.
This multi-consumer model enables evolutionary architecture where you add capabilities incrementally without disrupting existing systems. Your initial implementation might just archive data to S3, but over time you can add real-time dashboards, alerting systems, and machine learning pipelines, all consuming the same stream. The original archiving consumer continues working unchanged while new consumers independently process the data stream.
Firehose operates with a single-purpose delivery model. Each Firehose delivery stream has one source and one destination. If you need to deliver the same data to multiple destinations, you must either create multiple Firehose delivery streams (which means sending the same data multiple times) or first write to Kinesis Data Streams and then use multiple Firehose streams to fan out to different destinations. This limitation fundamentally shapes how you architect data flows.
Key Service Comparison
Latency Characteristics and Performance Considerations
The performance profiles of these services differ dramatically, and understanding these differences is critical for selecting the right service for latency-sensitive applications.
Real-Time Processing with Data Streams
Kinesis Data Streams delivers records to consumers with sub-second latency. Producers write records to the stream, and consumers can begin processing those records within 200-1000 milliseconds under typical conditions. This makes Data Streams suitable for truly real-time use cases: fraud detection that must analyze transactions as they occur, real-time bidding systems that evaluate ad opportunities within milliseconds, or operational monitoring that alerts on anomalies within seconds of occurrence.
The shard-based architecture of Data Streams enables this low latency. Each shard provides dedicated throughput capacity (1 MB/sec writes, 2 MB/sec reads), and adding shards scales performance linearly. Data written to one shard becomes immediately available to consumers reading from that shard. There’s no batching or buffering beyond what the client SDK does locally—records flow through the system as individual events.
This immediate availability enables sophisticated streaming analytics patterns. You can build applications that maintain running aggregations, detect patterns across multiple events, or correlate data from different sources—all operating on data that’s seconds old at most. For scenarios where every second matters, Data Streams provides the foundation.
Near-Real-Time Delivery with Firehose
Firehose operates on a near-real-time delivery model with minimum latency of 60 seconds. This delay is inherent to how Firehose works—it buffers incoming records and flushes them to the destination either when the buffer size reaches a threshold (typically 5 MB for S3) or when a time interval elapses (minimum 60 seconds). This batching is fundamental to Firehose’s efficiency and cost-effectiveness but makes it unsuitable for low-latency requirements.
The 60-second floor isn’t a configuration error or something you can optimize away—it’s baked into the service design. AWS chooses this trade-off because batching dramatically reduces the number of API calls to destination services, improving both cost and reliability. Writing one 5 MB file to S3 is far more efficient than writing 5,000 individual 1 KB files, both in terms of S3 API costs and downstream query performance.
For many use cases, this latency is perfectly acceptable. Log aggregation, clickstream analytics, and data lake ingestion rarely require sub-minute freshness. The operational simplicity Firehose provides—automatic compression, format conversion, error handling, and retry logic—often outweighs the latency limitation. But for real-time dashboards, instant alerting, or interactive applications, Firehose’s latency makes it a non-starter.
Scaling Behavior and Capacity Management
How these services scale differs fundamentally and impacts both cost and operational complexity.
Shard-Based Scaling in Data Streams
Kinesis Data Streams uses a shard-based model where you explicitly provision capacity by adding or removing shards. Each shard provides fixed throughput: 1 MB/sec or 1,000 records/sec for writes (whichever limit you hit first) and 2 MB/sec for reads across all consumers combined. If your application writes 5 MB/sec, you need at least 5 shards. If you have three consumers each reading at 2 MB/sec, you need at least 3 shards to support the read throughput.
This explicit provisioning requires capacity planning. You must understand your throughput requirements and provision enough shards to handle peak load plus some buffer for growth. Under-provisioning leads to throttling where the service rejects writes that exceed shard capacity. Over-provisioning wastes money on unused capacity.
AWS offers two modes for managing shard capacity: provisioned and on-demand. Provisioned mode requires you to manually adjust shard count as load changes, though you can automate this using Application Auto Scaling. On-demand mode automatically scales based on observed throughput, eliminating capacity planning at the cost of higher per-GB pricing. For predictable workloads, provisioned mode costs less. For spiky or unpredictable workloads, on-demand mode simplifies operations.
The shard model also impacts parallelism. Each shard can only be read by one consumer instance at a time within the same consumer application (when using the Kinesis Client Library). To achieve higher read parallelism, you need more shards. This means scaling reads requires scaling the entire stream, not just adding consumers. Planning shard counts requires considering both write throughput and read parallelism requirements.
Serverless Scaling in Firehose
Firehose scales completely automatically with zero capacity planning required. You create a delivery stream, send data to it, and Firehose handles all scaling behind the scenes. There are no shards to manage, no capacity to provision, and no throttling to handle—Firehose simply accepts whatever data rate you throw at it (up to service limits).
This serverless model dramatically reduces operational overhead. You don’t monitor throughput metrics, adjust capacity, or worry about peak loads. Firehose scales up during traffic spikes and scales down during quiet periods automatically. This makes it ideal for workloads with variable or unpredictable throughput where provisioning for peak capacity would be wasteful.
The pricing model reflects this simplicity—you pay only for the data volume processed, with no charges for idle capacity. There’s no penalty for creating delivery streams that sit mostly idle, and no need to carefully right-size resources. This consumption-based pricing often makes Firehose more cost-effective than Data Streams for low-volume or intermittent workloads.
However, Firehose does have throughput limits per delivery stream (5,000 records/sec, 5 MB/sec). For higher throughput, you need multiple delivery streams with data distributed across them. Unlike Data Streams where you explicitly control parallelism through shard count, Firehose’s limits are opaque and require support ticket escalation to increase.
Cost Structures and Economic Considerations
The pricing models differ significantly and can heavily influence which service costs less for your specific workload.
Data Streams Pricing Components
Kinesis Data Streams charges for three main components:
Shard hours represent the primary cost. Each shard costs a fixed hourly rate regardless of whether you’re using its full capacity. If you provision 10 shards, you pay for 10 shards continuously, even if your actual throughput is low. This creates a base cost floor that makes Data Streams expensive for low-volume workloads. However, once you’re fully utilizing shard capacity, it becomes very cost-efficient on a per-GB basis.
PUT payload units charge for data ingestion based on 25 KB increments. Each record or batch of records rounded up to the nearest 25 KB unit incurs a charge. This means small records (1 KB each) are expensive since you still pay for 25 KB units. Batching helps significantly—sending 25 records of 1 KB each in a single PUT costs the same as sending one 25 KB record.
Extended retention beyond the default 24 hours costs extra per shard-hour. If you’re using Data Streams primarily for buffering with short retention, this cost is minimal. But if you’re leveraging the replay capability with week-long or month-long retention, these charges become significant.
The cost structure favors high-volume, sustained workloads where you can keep shards well-utilized. For applications writing hundreds of megabytes per second continuously, Data Streams provides excellent per-GB economics. For applications with sporadic or low-volume data, the fixed shard hour costs make it expensive.
Firehose Pricing Simplicity
Kinesis Firehose charges based solely on data volume ingested, with pricing per GB varying by destination type. There are no base charges, no shard hours, and no per-record costs. You pay for exactly what you use with sub-GB granularity.
This simple consumption pricing makes Firehose highly cost-effective for low-volume workloads. If you’re ingesting 1 GB per day, you pay for 1 GB. With Data Streams, you’d need at least one shard running continuously, paying for capacity whether or not you’re using it. For sporadic workloads, Firehose can cost 10-100x less than Data Streams.
Additional charges apply for optional features: data format conversion (per GB converted) and Lambda transformation (standard Lambda pricing). These are optional enhancements that you only pay for if you use them, maintaining the consumption-based model.
The economic crossover point depends on throughput volume and retention requirements. Generally, Firehose is cheaper below 10-20 MB/sec sustained throughput, while Data Streams becomes more economical above that level, especially if you’re utilizing the data for multiple purposes (which Firehose can’t support natively).
🎯 Decision Framework: When to Use Each Service
• Multiple consumers need the same data
• You need data replay capability
• Custom processing logic is essential
• Building event-driven architectures
• High sustained throughput (50+ MB/sec)
• Real-time analytics or ML inference
• 60+ second latency is acceptable
• Single destination is sufficient
• Minimal operational overhead needed
• Low or variable throughput
• Simple transformation requirements
• Data lake ingestion or archival
Integration Patterns and Ecosystem Compatibility
How these services integrate with the broader AWS ecosystem and external tools significantly impacts architectural decisions.
Data Streams Integration Capabilities
Kinesis Data Streams integrates deeply with AWS services designed for stream processing. AWS Lambda can directly consume Data Streams, automatically handling shard management and checkpointing. Kinesis Data Analytics reads from Data Streams for SQL-based stream processing. Amazon Managed Service for Apache Flink uses Data Streams as a source for sophisticated streaming applications.
The Kinesis Client Library (KCL) provides a battle-tested framework for building custom consumers in Java, Python, Go, Node.js, Ruby, and .NET. KCL handles the complexity of distributed stream processing: shard assignment, failover, checkpointing, and rebalancing when consumer instances join or leave. This abstraction lets you focus on record processing logic rather than distributed systems mechanics.
For applications that need to consume from Data Streams but don’t require millisecond latency, Enhanced Fan-Out provides dedicated read throughput for each consumer without counting against the shard’s 2 MB/sec read limit. This feature enables high-scale consumption patterns where many consumers read the same stream without throttling each other.
Third-party tools like Apache Kafka Connect, Logstash, and Fluentd have connectors for Kinesis Data Streams, enabling hybrid architectures that bridge AWS and on-premises systems. This ecosystem support makes Data Streams a viable option even for organizations with existing streaming infrastructure.
Firehose Destination Ecosystem
Kinesis Firehose’s value proposition centers on its managed destinations. It natively delivers to:
Amazon S3 with automatic partitioning, compression, and encryption—the most common use case for data lake ingestion
Amazon Redshift via S3 staging with automatic COPY commands—enabling real-time data warehouse loading
Amazon OpenSearch Service for log analytics and full-text search without managing delivery infrastructure
Third-party SaaS platforms including Splunk, Datadog, New Relic, MongoDB Atlas, and HTTP endpoints—enabling turnkey integration with observability and analytics tools
Each destination integration includes retry logic, dead letter queue handling, and monitoring. This eliminates the operational burden of building and maintaining delivery reliability, which is non-trivial when dealing with third-party APIs that may experience downtime or rate limiting.
Lambda transformation functions provide a powerful extension point. You can inject custom processing logic—data enrichment, filtering, format conversion, or PII redaction—without building separate consumer applications. Firehose handles the operational aspects (retry, scaling, monitoring) while you provide just the transformation logic.
Operational Complexity and Maintenance Requirements
The operational overhead differs dramatically and should factor heavily into your decision, especially for teams with limited DevOps resources.
Managing Data Streams Operations
Running Kinesis Data Streams requires ongoing operational attention. You must monitor shard-level metrics, manage capacity changes, handle consumer application lifecycle, implement proper error handling and retry logic, and maintain checkpointing infrastructure. Consumer applications represent custom code you must deploy, monitor, scale, and debug.
Shard splits and merges for scaling require careful orchestration to avoid data loss or processing gaps. While AWS provides APIs to automate this, you must build the automation logic and handle edge cases. Alternatively, on-demand mode automates scaling but at higher cost and with less predictable performance.
Consumer lag monitoring is critical—if consumers fall behind, you need to detect this and either scale consumers or increase shard count to provide more read throughput. Troubleshooting consumer issues requires examining application logs, understanding KCL behavior, and diagnosing whether problems stem from your code, AWS services, or external dependencies.
For organizations with strong DevOps practices and teams experienced in distributed systems, this operational complexity is manageable and worthwhile for the control it provides. For teams seeking simplicity or lacking streaming expertise, the maintenance burden can be overwhelming.
Firehose’s Managed Simplicity
Kinesis Firehose requires minimal operational involvement. You create a delivery stream, configure the destination, and data starts flowing. There are no consumer applications to deploy, no scaling decisions to make, and no capacity planning required. AWS handles all reliability, retry logic, and scaling automatically.
Monitoring reduces to watching delivery success/failure metrics and checking the error bucket for failed records. Troubleshooting usually involves examining the error records to identify data format issues or Lambda transformation bugs. There’s no distributed system complexity to debug—the service either delivers data or routes failures to the error location.
This operational simplicity makes Firehose accessible to teams without deep streaming expertise. Data engineers can configure delivery streams without understanding distributed systems concepts like sharding, checkpointing, or consumer groups. The reduction in operational overhead often outweighs any architectural limitations the service imposes.
Conclusion
Choosing between Kinesis Data Streams and Firehose fundamentally depends on whether you need the flexibility and low latency of a general-purpose stream buffer or the operational simplicity of a managed delivery pipeline. Data Streams excels when building real-time applications with complex processing requirements, multiple consumers, or sub-second latency demands. Firehose wins for straightforward delivery to supported destinations where near-real-time latency suffices and operational simplicity outweighs architectural flexibility.
The decision isn’t always binary—many production architectures use both services in concert, leveraging Data Streams for real-time processing while using Firehose as one consumer handling durable archival to S3. This hybrid pattern captures the strengths of each service: low-latency processing with flexible consumption patterns from Data Streams, combined with zero-maintenance delivery to data lakes via Firehose. Understanding these services’ distinct characteristics and intentionally selecting the right tool for each use case ensures your streaming infrastructure meets both technical requirements and operational constraints effectively.