Computer & IT
IoT Telemetry Storage Calculator
Use observed daily raw telemetry volume to estimate retained and forecast stored capacity, then reconcile storage with access and egress cost.
Current retained raw data-
Current stored data after compression and replicas-
Daily raw volume at forecast end-
Forecast retained raw data-
Forecast stored data after compression and replicas-
Current storage cost per month-
Forecast storage cost per month-
Forecast storage, access, and egress cost-
Annualized forecast run rate-
Stored-capacity growth through horizon-
Decision view
Device field, ingest gateway, and telemetry tiers
Device field, ingest gateway, and telemetry tiersDevice readings pass through the ingest gateway into hot observations and retained aggregates before copies determine forecast capacity.
IoT Telemetry Storage monthly costForecast month is horizontal and modeled storage, access, and egress cost is vertical.
Exact scenario comparisonRetention period (days) changes while all other entered assumptions remain constant.
| Retention period (days) | Current retained raw data | Current stored data after compression and replicas | Daily raw volume at forecast end | Forecast retained raw data | Forecast stored data after compression and replicas | Current storage cost per month | Forecast storage cost per month | Forecast storage, access, and egress cost | Annualized forecast run rate | Stored-capacity growth through horizon |
|---|
Period-by-period detail
IoT telemetry storage monthly capacity and cost forecast
How to use IoT Telemetry Storage Calculator
- Measure daily telemetry after protocol and ingestion overhead.
- Choose retention for raw, aggregated, and downsampled data deliberately.
- Check forecast bytes together with write throughput and query workload.
Calculator guide
Understanding IoT Telemetry Storage Calculator
IoT telemetry capacity is controlled by device count, message frequency, payload size, retention, compression, and replicas.
Calculation method
How the calculation works
Translate daily IoT telemetry storage creation into retained raw volume, apply compression and replica count, project monthly growth, and reconcile current and forecast storage capacity and run-rate cost. Retain daily raw volume for the selected days, divide by compression, multiply by copies, and compound ingest growth across the forecast months.
Telemetry pipeline
Route device signals into hot and retained time-series tiers
The visual shows device sources, the ingest gateway, hot observations, aggregated history, replicas, and forecast capacity.
Worked situations
Practical examples
- Adding devices can be represented by positive monthly ingest growth.
- Downsampling older readings can improve the effective compression ratio.
- A raw stream and a curated analytics copy may act as separate stored copies.
Better inputs
Useful tips
- Separate hot raw data from long-term aggregates.
- Include metadata and late-arriving events.
- Track message count as well as bytes for service limits.
Before relying on the result
Limitations and common mistakes
- The model starts from daily GB and does not derive volume from device message rate.
- Write operations, stream processing, and query compute are simplified.
- One blended storage rate is used.
Reference
Key terms
- Telemetry ingest
- Sensor and device data entering the platform each day.
- Downsampling
- Reducing time-series resolution for older data.
- Replica
- Additional stored copy used for resilience or analytics.
Important note
Calculated from the entered technical values using the displayed model. Validate topology, workloads, capacity, security, redundancy, and observed performance before implementation.
Frequently asked questions
Can this calculate storage from messages per second?
No. Convert that workload to daily GB first.
Should aggregates count as another copy?
Count them when they occupy separately billed storage.
Does the estimate include streaming compute?
No, except for any amount included in the access-cost input.