The provisioned throughput pricing model in AWS

 Excellent question — this is an important concept for understanding how AWS services charge for predictable performance.

Let’s break it down clearly 👇

---

⚙️ What Is the Provisioned Throughput Pricing Model?

Provisioned Throughput means you pre-allocate (reserve) a specific amount of read and write capacity for a service — typically one that needs fast and consistent performance, such as Amazon DynamoDB, Amazon Kinesis, or Amazon Bedrock Knowledge Bases.

You’re essentially saying:

“I want this level of throughput available at all times, and I’ll pay for it whether I use it or not.”

---

🧠 Key Idea

Instead of paying per request (as in “on-demand” or “pay-as-you-go”),
you provision a fixed performance level — measured in units like:

• Read Capacity Units (RCUs) and Write Capacity Units (WCUs) in DynamoDB

• Records per second or MB/s in Kinesis Data Streams

• Requests per second (TPS) in some AI APIs

You then pay for that reserved capacity per hour.

---

💡 How It Works — Example (DynamoDB)

Let’s say you set:

• 5 RCUs → supports 5 strongly consistent reads per second (for 4 KB items)

• 10 WCUs → supports 10 writes per second (for 1 KB items)

AWS guarantees this performance — even if your workload spikes — because you’ve provisioned it in advance.

You’ll be billed per RCU/WCU-hour, regardless of whether you fully use it.

---

💰 Pricing Characteristics

Characteristic	Description
Fixed Capacity	You specify throughput (reads/writes per second).
Predictable Cost	You pay a fixed rate for provisioned units.
Guaranteed Performance	AWS ensures your specified throughput is always available.
Pay for Reservation	You pay for provisioned units even if not fully used.
Auto Scaling (Optional)	You can enable auto-scaling to adjust capacity automatically with traffic.

---

🧩 Services That Offer Provisioned Throughput

Service	Description
Amazon DynamoDB	Provisioned read/write capacity for predictable low-latency DB performance.
Amazon Kinesis Data Streams	Provision shards (each shard = fixed throughput) for ingestion pipelines.
Amazon S3 Glacier	Provisioned retrieval throughput for faster data access.
Amazon Bedrock Knowledge Bases (RAG)	Provisioned inference throughput for consistent LLM query response rates.
Amazon OpenSearch / Elasticsearch	Reserved instance capacity for predictable indexing and search performance.

---

🔄 Comparison: Provisioned vs. On-Demand Pricing

Aspect	Provisioned Throughput	On-Demand / Pay-as-You-Go
Performance	Guaranteed, predictable	Automatically adjusts, variable
Cost	Fixed (whether used or not)	Variable (pay for actual usage)
Best For	Steady, predictable workloads	Spiky, unpredictable workloads
Configuration	You define throughput units	AWS scales automatically
Billing Unit	Per hour of provisioned capacity	Per request or per second/minute

---

🧭 When to Use Provisioned Throughput

✅ Good choice if your workload is:

• Stable and predictable (e.g., retail transactions per second, steady IoT data flow)

• Latency-sensitive and must never throttle

• Used in regulated environments needing guaranteed SLA

• Running 24×7 with consistent traffic

❌ Not ideal if your workload is:

• Highly unpredictable or bursty

• Low average utilization with occasional spikes

For those, on-demand mode or auto-scaling provisioned is more cost-efficient.

---

📊 Example — DynamoDB Cost Comparison

Mode	Description	Example Cost Behavior
Provisioned (10 WCUs, 10 RCUs)	Fixed throughput (10 writes + 10 reads/sec)	Same hourly cost, even if idle
On-Demand	Pay per request	Cost scales with actual reads/writes

---

🧠 Analogy

Imagine a toll road:

• Provisioned throughput = You buy a dedicated lane — always available, but you pay for it even if empty.

• On-demand = You pay per trip, and traffic may vary.

---

✅ Summary

Feature	Provisioned Throughput Model
Definition	You reserve a specific amount of performance (throughput) in advance.
Cost Type	Fixed — based on provisioned units, not actual usage.
Benefit	Predictable cost + guaranteed performance.
Trade-off	Pay for unused capacity if demand is low.
Best For	Consistent workloads needing guaranteed response rates.

---

Would you like me to show how this applies specifically to DynamoDB or Bedrock Knowledge Bases, with a small cost calculation example?