Optimize long-term video storage prices with Amazon Kinesis Video Streams heat storage tier

Introduction

Amazon Kinesis Video Streams supplies highly effective options for cloud-based video administration in bodily safety and surveillance organizations. Whereas organizations want steady recording capabilities, conventional storage approaches usually result in elevated prices. Kinesis Video Streams excels at real-time video administration and short-term storage, serving enterprise deployments with intensive digicam networks. Now, the revolutionary Kinesis Video Streams heat storage function enhances these capabilities, delivering cost-efficient options for long-term video retention, and enabling organizations to optimize their storage methods.

On this publish, we present how by successfully utilizing the newly launched Kinesis Video Streams heat storage function, you may construct a way more cost-efficient answer for long-term video retention whereas sustaining the efficiency and accessibility your operations require.

What did we launch?

On November twenty sixth, 2025, Amazon Kinesis Video Streams added a brand new heat storage tier, delivering cost-effective storage for prolonged media retention. The usual Kinesis Video Streams storage tier (now designated as the new tier) stays optimized for real-time knowledge entry and short-term storage. The brand new heat tier allows long-term media retention with sub-second entry latency at diminished storage prices.

Buyer advantages

Whereas Amazon Kinesis Video Streams sizzling storage excels at real-time streaming, regulatory compliance usually requires prolonged retention durations, making it pricey for bodily safety and enterprise video surveillance clients. Prospects that sometimes used Kinesis Video Streams sizzling tier for streaming however then moved knowledge to different cost-efficient storage can now preserve media in Kinesis Video Streams’ heat tier with out incurring a value penalty. This brings a number of benefits: (a) Prospects can preserve easier architectures with out managing knowledge migration, scale back their operational overhead, and preserve their knowledge readily accessible throughout the Kinesis Video Streams ecosystem. (b) Bigger datasets stay in Kinesis Video Streams, clients profit from extra full historic knowledge and fewer fragmentation throughout storage techniques. This allows enhanced analytics capabilities by simpler processing of steady knowledge streams, higher temporal evaluation potential, and streamlined ML/AI mannequin coaching. As well as, ingestion into Kinesis Video Streams heat storage tier is priced per persevered fragment depend. Which means builders management prices by adjusting fragment dimension – bigger fragments scale back ingestion prices, whereas smaller fragments ship decrease latency. This flexibility lets builders optimize their particular use circumstances.

Structure

Amazon Kinesis Video Streams tiered storage structure supplies a seamless integration between real-time streaming capabilities and cost-optimized long-term storage. The structure consists of two parts: Video ingestion, that features IoT gadgets and cameras stream video knowledge to KVS endpoints, and the Storage tier. The storage tier choice relies on the stream’s configuration, and fragments are saved in both sizzling or heat tier.

Key advantages of this structure are:

• Unified API: Identical Amazon Kinesis Video Streams APIs work throughout each storage tiers
• Seamless playback: Purposes can retrieve video no matter storage tier
• Dynamic configuration: Storage tiers might be modified with out service interruption
• Automated lifecycle: Constructed-in insurance policies handle knowledge transitions and retention

Value concerns

The desk beneath reveals Amazon Kinesis Video Streams’ sizzling and heat tier pricing construction for US East (N. Virginia) area. Notice that heat tier storage costs have a major discount whereas consumption prices stay constant throughout each tiers. Heat ingestion is priced based mostly on fragment depend moderately than per GB, which supplies predictable value administration for long-term retention eventualities. Heat tier storage comes with a 30-day minimal retention interval, which means AWS fees clients for no less than 30 days even in the event that they delete the info earlier.

Kinesis Video Streams is constructed on Amazon Easy Storage Service (Amazon S3)’s knowledge retailer, guaranteeing your video knowledge is saved with excessive sturdiness and availability. Nonetheless, the frequency of accessing saved video knowledge can fluctuate considerably throughout completely different use circumstances. For instance, think about a situation the place CCTV uncooked knowledge should be retained for 30 days for safety compliance functions. If no particular occasions happen, the frequency and segments of knowledge that have to be reviewed will lower over time. In such conditions, heat tier performance lets you scale back storage prices whereas sustaining the identical sturdiness and availability of storage.

Amazon Kinesis Video Streams’ heat tier is constructed on Amazon S3 Commonplace-Rare Entry (S3 Commonplace-IA) to offer the next advantages:

• As much as 67% value discount: Important value financial savings in comparison with commonplace Amazon Kinesis Video Streams storage
• Identical sturdiness and availability: 99.999999999% sturdiness and 99.9% availability assure
• Seamless integration: Helps compatibility with current Amazon Kinesis Video Streams workflows and APIs
• Automated lifecycle administration: Automated knowledge administration by configurable insurance policies

Now let’s have a look at 3 ways to optimize prices utilizing the nice and cozy tier.

1. Storage duration-based value optimization

Heat tier is designed for value optimization in use circumstances requiring long-term storage. Whereas the nice and cozy tier presents comparatively decrease storage prices, it requires a minimal retention interval of 30 days.

Due to this fact, to be used circumstances requiring solely short-term storage, sizzling tier could also be more cost effective.

Actual-world value evaluation instance: Let’s look at 1,000 CCTV cameras working 8 hours day by day:

• Ingestion interval: 30 days
• Bitrate: 4Mbps
• Fragment period: 2 seconds
• AWS Area: us-east-1 area

Value comparability based mostly on retention interval

Key findings: As seen above, underneath the identical fragment size, heat tier value advantages are greater for longer retention durations, sometimes ranging from 30 days.

2. Fragment length-based value optimization

One other revolutionary function of heat tier is fragment-based billing as an alternative of the standard GB-based billing of sizzling tier. This billing strategy permits for vital discount in ingestion prices by adjusting fragment size.

The desk beneath reveals how advantages of heat tier enhance in comparison with sizzling tier when the fragment size will increase. That is for 30-day retention.

Key findings: Beneath the identical retention interval, heat tier advantages are greater for longer fragment lengths. The chart beneath can help as resolution chart. For 30-day retention, heat tier breaks even at fragment size of 1.06 sec, i.e. whole value is cheaper in heat tier vs sizzling tier for fragment lengths longer than 1.06 sec. For 60-day retention, that break even occurs at 0.68 sec.

3. Understanding storage value variations by digicam decision

The nice and cozy tier pricing based mostly on fragment depend mannequin brings value benefits for cameras with greater bitrates. See comparability in desk beneath for 1,000 digicam deployment over 30 days with 5-second fragments.

Key findings:

• Larger bitrates yield better financial savings: 4 Mbps streams obtain 48% value discount, whereas 1Mbps streams see 9% financial savings
• Constant ingestion value benefit: Heat tier ingestion stays at $1,687.50 no matter bitrate, whereas sizzling tier prices scale linearly with knowledge quantity
• Storage value scaling: Heat tier storage prices scale proportionally with bitrate, however at considerably decrease charges than sizzling tier
• Break-even evaluation: All bitrate eventualities demonstrated substantial value advantages when utilizing Heat tier for prolonged retention durations

This fragment-based pricing mannequin allows considerably more cost effective storage and processing of high-resolution video content material with bigger knowledge volumes. The upper the video high quality and bitrate, the extra pronounced the associated fee benefits develop into when using heat tier storage.

Storage tier settings

The storage interval settings for current streams might be simply up to date, and the settings are instantly utilized to fragments collected after the replace.Beneath is the AWS CLI command for updating or making a stream in heat tier storage in Amazon Kinesis Video Streams.

Replace storage configuration:

You probably have an current stream in sizzling tier, you may replace the stream settings to heat tier as follows.

```bash 
STREAM_INFO=$(aws kinesisvideo describe-stream 
--stream-name "$STREAM_NAME" 
--region $REGION)

CURRENT_VERSION=$(echo "$STREAM_INFO" | jq -r '.StreamInfo.Model')
aws kinesisvideo update-stream-storage-configuration 
--stream-name "$STREAM_NAME" 
--current-version "$CURRENT_VERSION" 
--stream-storage-configuration DefaultStorageTier="WARM" 
--region $REGION
```

Create stream with heat tier:

You’ll be able to create a brand new heat tier stream utilizing the next command.

```bash
aws kinesisvideo create-stream 
--stream-name $STREAM_NAME 
--media-type "video/h264" 
--data-retention-in-hours 720 
--stream-storage-configuration '{
"DefaultStorageTier": "WARM"
}' 
--region $REGION
```

The storage interval settings for created streams might be simply modified, and the settings are instantly utilized to fragments collected after the change. Stream playback stays uninterrupted even when storage configuration adjustments are utilized.

Cleansing up

To keep away from ongoing fees, ensure to delete the take a look at streams you created throughout this walkthrough. Do not forget that deleting a stream completely removes all saved video knowledge, whatever the storage tier configuration.

Delete the stream:

```bash
STREAM_INFO=$(aws kinesisvideo describe-stream 
--stream-name "$STREAM_NAME" 
--region $REGION)

STREAM_ARN=$(echo "$STREAM_INFO" | jq -r '.StreamInfo.StreamARN')
CURRENT_VERSION=$(echo "$STREAM_INFO" | jq -r '.StreamInfo.Model')

aws kinesisvideo delete-stream 
--stream-arn "$STREAM_ARN" 
--current-version "$CURRENT_VERSION" 
--region $REGION
```

Vital notes:

• Stream deletion is irreversible and removes all related video knowledge
• Each Sizzling and Heat tier knowledge might be completely deleted
• Guarantee you have got backed up any necessary video knowledge earlier than deletion
• The deletion course of could take a couple of minutes to finish

Conclusion

Amazon Kinesis Video Streams’ tiered storage functionality delivers an economical strategy for managing video knowledge. Via this function organizations can dramatically scale back storage prices whereas sustaining operational excellence. These prices might be managed by controlling three variables: fragment size, retention interval, and determination bitrate. (a) Beneath the identical fragment size, heat tier value advantages are greater for longer retention durations, sometimes ranging from 30 days; (b) underneath the identical retention interval, heat tier advantages are greater for longer fragment lengths; (c) underneath the identical fragment size and retention interval, heat tier value advantages are greater because the bitrates enhance.

The important thing to profitable implementation lies in precisely understanding your group’s particular entry patterns, retention necessities, and price optimization objectives. Begin with pilot implementation on non-critical streams, monitor outcomes, and progressively increase to your complete video infrastructure. Kinesis Video Streams tiered storage will not be merely a value discount device. It’s a strategic enabler that makes sustainable development of video-enabled IoT functions economically viable.

Subsequent steps

Are you able to optimize prices in your video answer with Kinesis Video Streams tiered storage? Right here’s your path ahead:

In regards to the authors

Andre Sacaguti Andre Sacaguti is a Sr. Product Supervisor at AWS, engaged on Kinesis Video Streams. He helps organizations flip video knowledge into actionable insights, exploring how AI brokers could make streaming video smarter and extra interactive. Earlier than becoming a member of AWS, Andre constructed and launched IoT merchandise at T-Cell and Qualcomm, serving to linked gadgets work smarter and extra securely.

Jinseon Lee Jinseon Lee is a Senior IoT GTM Options Architect specializing in IoT and Robotics at AWS APJ. With over 12 years of expertise in know-how and software program growth, Jinseon has labored in numerous roles serving to shoppers design and implement optimum cloud architectures.