Why serving architecture affects latency and cost in MLOps - Visual Breakdown

Process Flow - Why serving architecture affects latency and cost

Client sends request

↓

Serving Architecture Choice

↓

Monolithic

↓

Process Request

↓

Response Sent

↓

Latency & Cost Impact Based on Architecture

↓

User Experience & Budget Outcome

The flow shows how the choice of serving architecture affects how requests are processed, which impacts latency and cost, ultimately influencing user experience and budget.

Execution Sample

MLOps

# Pseudocode for request handling
architecture = 'serverless'
if architecture == 'monolithic':
    latency = 100
    cost = 50
elif architecture == 'microservices':
    latency = 70
    cost = 70
else:
    latency = 50
    cost = 90

This code simulates how different serving architectures affect latency and cost values.

Process Table

Step	Architecture	Condition	Latency (ms)	Cost ($)	Explanation
1	monolithic	architecture == 'monolithic'	100	50	Monolithic chosen: higher latency, lower cost
2	microservices	architecture == 'microservices'	70	70	Microservices chosen: balanced latency and cost
3	serverless	else	50	90	Serverless chosen: lowest latency, highest cost
4	-	End of decision	-	-	Latency and cost set based on architecture

💡 All architecture options evaluated, latency and cost assigned accordingly

Status Tracker

Variable	Start	After Step 1	After Step 2	After Step 3	Final
architecture	undefined	monolithic	microservices	serverless	serverless
latency	undefined	100	70	50	50
cost	undefined	50	70	90	90

Key Moments - 3 Insights

Why does serverless architecture have higher cost but lower latency?

Serverless runs code on demand with fast scaling, reducing latency, but the pay-per-use model increases cost as shown in execution_table row 3.

Why does monolithic architecture have higher latency but lower cost?

Monolithic runs all in one place, causing slower response (higher latency) but simpler infrastructure lowers cost, as seen in execution_table row 1.

How does microservices balance latency and cost?

Microservices split functions, improving latency over monolithic but adding overhead costs, balancing both as shown in execution_table row 2.

Visual Quiz - 3 Questions

Look at the execution_table, what is the latency value when architecture is microservices?

A 100 ms

B 70 ms

C 50 ms

D 90 ms

💡 Hint

Check execution_table row 2 under 'Latency (ms)' column

At which step does the cost become highest according to the execution_table?

A Step 3

B Step 2

C Step 1

D Step 4

💡 Hint

Look at the 'Cost ($)' column in execution_table rows

If the architecture changed from serverless to monolithic, how would latency and cost change?

A Latency decreases, cost increases

B Both latency and cost increase

C Latency increases, cost decreases

D Both latency and cost decrease

💡 Hint

Compare latency and cost values in execution_table rows 1 and 3

Concept Snapshot

Serving architecture affects latency and cost:
- Monolithic: higher latency, lower cost
- Microservices: balanced latency and cost
- Serverless: lowest latency, highest cost
Choose based on user experience needs and budget.

Full Transcript

This visual execution shows how different serving architectures impact latency and cost. The client sends a request, which is processed differently depending on the architecture chosen: monolithic, microservices, or serverless. Each architecture affects latency and cost differently. Monolithic has higher latency but lower cost due to simpler infrastructure. Microservices improve latency but add overhead cost. Serverless offers the lowest latency with fast scaling but costs more due to pay-per-use pricing. The execution table traces these values step-by-step, and the variable tracker shows how latency and cost change with architecture. Understanding these trade-offs helps choose the right serving architecture for balancing user experience and budget.

Practice

1. Which serving architecture typically offers the lowest latency for model predictions?

easy

A. Offline serving

B. Batch serving

C. Edge serving

D. Cloud batch processing

Solution

1. Step 1: Understand latency in serving architectures

   Latency means the delay before a prediction is returned. Edge serving places the model close to the user, reducing delay.

2. Step 2: Compare architectures

   Batch serving processes data in groups and is slower. Edge serving is designed for fast responses near the user.

3. Final Answer:

   Edge serving -> Option C

4. Quick Check:

   Lowest latency = Edge serving [OK]

Hint: Edge serving is closest to users, so fastest response [OK]

Common Mistakes:

• Confusing batch serving as low latency
• Thinking cloud batch is fastest
• Ignoring edge location benefits

2. Which statement correctly describes batch serving in ML model deployment?

easy

A. Batch serving provides real-time predictions with high cost.

B. Batch serving processes data in groups and is usually cheaper but slower.

C. Batch serving always runs on edge devices.

D. Batch serving requires no compute resources.

Solution

1. Step 1: Define batch serving

   Batch serving processes multiple data points together, not one by one, which saves cost but adds delay.

2. Step 2: Evaluate options

   Batch serving processes data in groups and is usually cheaper but slower. correctly states batch serving is cheaper but slower. Other options are incorrect or unrealistic.

3. Final Answer:

   Batch serving processes data in groups and is usually cheaper but slower. -> Option B

4. Quick Check:

   Batch serving = cheaper, slower [OK]

Hint: Batch = groups, cheaper but slower [OK]

Common Mistakes:

• Thinking batch serving is real-time
• Assuming batch runs on edge devices
• Believing batch needs no compute

3. Given a model deployed with online serving and another with batch serving, which output best describes their latency and cost?

medium

A. Online serving: low latency, high cost; Batch serving: high latency, low cost

B. Online serving: high latency, low cost; Batch serving: low latency, high cost

C. Both have similar latency and cost

D. Online serving is always cheaper than batch serving

Solution

1. Step 1: Recall characteristics of online and batch serving

   Online serving provides predictions immediately (low latency) but requires more resources (high cost). Batch serving delays predictions but is cheaper.

2. Step 2: Match options to characteristics

   Online serving: low latency, high cost; Batch serving: high latency, low cost correctly matches low latency and high cost to online serving, and high latency and low cost to batch serving.

3. Final Answer:

   Online serving: low latency, high cost; Batch serving: high latency, low cost -> Option A

4. Quick Check:

   Online = fast & costly, Batch = slow & cheap [OK]

Hint: Online = fast+costly, Batch = slow+cheap [OK]

Common Mistakes:

• Swapping latency and cost roles
• Assuming both have same cost
• Thinking batch is faster

4. A team deployed a model using edge serving but notices high latency and cost. What is the most likely cause?

medium

A. Edge serving always causes high latency and cost

B. Batch processing was mistakenly used instead of edge serving

C. The model is deployed in a cloud data center far from users

D. The model is too large to run efficiently on edge devices

Solution

1. Step 1: Understand edge serving constraints

   Edge devices have limited resources. Large models can slow down processing and increase cost.

2. Step 2: Analyze options

   The model is too large to run efficiently on edge devices explains the likely cause. Batch processing was mistakenly used instead of edge serving is incorrect because batch serving is different. The model is deployed in a cloud data center far from users describes cloud serving, not edge. Edge serving always causes high latency and cost is false.

3. Final Answer:

   The model is too large to run efficiently on edge devices -> Option D

4. Quick Check:

   Large model on edge = high latency/cost [OK]

Hint: Large models slow edge devices, raising latency and cost [OK]

Common Mistakes:

• Confusing edge with cloud serving
• Assuming edge always has high latency
• Mixing batch and edge serving

5. A company wants to minimize prediction latency for users worldwide but has a limited budget. Which serving architecture balances latency and cost best?

hard

A. Combine edge serving for critical regions and batch serving elsewhere

B. Deploy models only in a central cloud data center

C. Use batch serving exclusively for all predictions

D. Deploy large models on every user device

Solution

1. Step 1: Analyze latency and cost trade-offs

   Central cloud has higher latency for distant users. Batch serving is cheap but slow. Edge serving is fast but costly.

2. Step 2: Evaluate hybrid approach

   Combining edge serving in key regions reduces latency where needed, while batch serving elsewhere controls costs.

3. Final Answer:

   Combine edge serving for critical regions and batch serving elsewhere -> Option A

4. Quick Check:

   Hybrid edge + batch balances latency and cost [OK]

Hint: Hybrid edge and batch serving balances speed and cost [OK]

Common Mistakes:

• Choosing only cloud causing high latency
• Using batch only causing slow responses
• Deploying large models on all devices is costly