AIConf 2025 Overview of AWS services about machine learning and generative AI. https://www.aiconf.it/e/sessione/3727/Amazon-Q-and-Amazon-Bedrock-fully-managed-vs-custom

1. Amazon Q and Bedrock,
fully managed vs custom
Alessandra Bilardi
Data & Automation Specialist @ Corley Cloud
>>AI CONF 2025

2. AI Conf 2025
Amazon Q and Bedrock,
fully managed vs custom

3. AI Conf 2025
Oltre 500 progetti su AWS
Corley Cloud è una realtà certificata
con innumerevoli riconoscimenti e
un portfolio di centinaia di progetti
AWS sviluppati in diversi ambiti:
cloud native, migrazione, machine
learning & AI, serverless, IoT,
sicurezza e cloudOps.
Advanced Partner AWS

4. AI Conf 2025
Data & Automation Specialist @ Corley Cloud
alessandra.bilardi@corley.it
corley.it
Alessandra Bilardi

5. AI Conf 2025
Alessandra Bilardi
Data & Automation Specialist @ Corley Cloud
alessandra.bilardi@corley.it
corley.it

6. AI Conf 2025
Alessandra Bilardi
Data & Automation Specialist @ Corley Cloud
alessandra.bilardi@corley.it
corley.it

7. AI Conf 2025
SUMMARY
Machine learning steps and actors
Generative AI with Amazon Q
Generative AI with Amazon Bedrock
Chat bot

8. AI Conf 2025
Machine learning steps and actors

9. AI Conf 2025
What are the steps of ML ?
➔ The data may arrive ready for learning,
but often some processing is needed
➔ Model training could be delegated to an
AI system, except for custom steps
➔ Evaluation is a prediction for which we
know the expected values, for which we
can calculate metrics
➔ The prediction works on new data
processed with point 1 with the best
model saved in point 3
Preparation
Training
& Tuning
Testing
& Evaluation
Prediction
(inference)

10. AI Conf 2025
ML system

11. AI Conf 2025
ML system

12. AI Conf 2025
ML system

13. AI Conf 2025
ML system

14. AI Conf 2025
ML

15. AI Conf 2025
ML

16. AI Conf 2025
Are there other steps or actors in ML ?
➔ Embeddings are objects that contain
information about text, images, videos,
audio or code
➔ The prompt is the text that contains the
behavior that the model must have, the
instructions to follow to respond to the
request posed.
➔ Augmented Generation (AG) techniques
allow us to exploit generalist models by
providing them with instructions (the
prompt), context (an extract of the
embeddings) and a request to obtain a
specific response.
Question
AG
Answer
Embeddings
& Prompt
LM

17. AI Conf 2025
Use case - Chat bot - preparation steps

18. AI Conf 2025
Use case - Chat bot - preparation steps

19. AI Conf 2025
Use case - Chat bot - preparation steps

20. AI Conf 2025
Use case - Chat bot - preparation steps

21. AI Conf 2025
Use case - Chat bot - preparation steps

22. AI Conf 2025
Use case - Chat bot - preparation steps

23. AI Conf 2025
Use case - Chat bot - preparation steps

24. AI Conf 2025
Use case - Chat bot - preparation steps

25. AI Conf 2025
Use case - Chat bot - preparation steps

26. AI Conf 2025
Use case - Chat bot - preparation steps

27. AI Conf 2025
Generative AI with Amazon Q

28. AI Conf 2025
Amazon Q

29. AI Conf 2025
Amazon Q

30. AI Conf 2025
Amazon Q

31. AI Conf 2025
Amazon Q
Business

32. AI Conf 2025
Amazon Q
Business
1. Embedding (as needed)

33. AI Conf 2025
Amazon Q
Business
1. Embedding (as needed)

34. AI Conf 2025
Amazon Q
Business
1. Embedding (as needed)

35. AI Conf 2025
Amazon Q
Business
1. Embedding (as needed)
2. Request (question)
3. RAG
4. Response (answer)

36. AI Conf 2025
Amazon Q
Business
1. Embedding (as needed)
2. Request (question)
3. RAG
4. Response (answer)

37. AI Conf 2025
Amazon Q
Developer
1. Request

38. AI Conf 2025
Amazon Q
Developer
1. Request
2. Embedding (as needed)

39. AI Conf 2025
Amazon Q
Developer
1. Request
2. Embedding (as needed)
3. Context

40. AI Conf 2025
Generative AI with Amazon Bedrock

41. AI Conf 2025
Amazon Bedrock
1
2 Knowledge base (embedding)
3 Agent
Models
4 Prompt

42. AI Conf 2025
Models

43. AI Conf 2025
Models

44. AI Conf 2025
Models

45. AI Conf 2025
Models

46. AI Conf 2025
Models

47. AI Conf 2025
Models

48. AI Conf 2025
Models

49. AI Conf 2025
Knowledge base

50. AI Conf 2025
Knowledge base

51. AI Conf 2025
Knowledge base

52. AI Conf 2025
Knowledge base

53. AI Conf 2025
Knowledge base

54. AI Conf 2025
Agent

55. AI Conf 2025
Agent

56. AI Conf 2025
Agent

57. AI Conf 2025
Agent

58. AI Conf 2025
Agent

59. AI Conf 2025
Agent

60. AI Conf 2025
Agent

61. AI Conf 2025
Agent

62. AI Conf 2025
Prompt

63. AI Conf 2025
Prompt

64. AI Conf 2025
Prompt

65. AI Conf 2025
Prompt

66. AI Conf 2025
Prompt

67. AI Conf 2025
Flows

68. AI Conf 2025
Flows

69. AI Conf 2025
Flows

70. AI Conf 2025
Amazon Bedrock
1
2 Model evaluation
3 Playground
Data automation
4 Prompt routers / caching

71. AI Conf 2025
Chat bot

72. AI Conf 2025
Use case - Chat bot - preparation steps

73. AI Conf 2025
Use case - Chat bot - preparation steps

74. AI Conf 2025
Use case - Chat bot - production version 1.0

75. AI Conf 2025
Use case - Chat bot - production version 1.0

76. AI Conf 2025
Use case - Chat bot - production version 1.0

77. AI Conf 2025
Use case - Chat bot - production version 1.0

78. AI Conf 2025
Use case - Chat bot - production version 1.0

79. AI Conf 2025
Use case - Chat bot - production version 1.1

80. AI Conf 2025
Use case - Chat bot - production version 2.0

81. AI Conf 2025
Use case - Chat bot - production version 2.0

82. AI Conf 2025
Use case - Chat bot - production version 2.0

83. AI Conf 2025
Use case - Chat bot - production version 2.0

84. AI Conf 2025
Use case - Chat bot - production version 2.0

85. AI Conf 2025
Which infrastructure for the
ChatBot ?

86. AI Conf 2025
Solutions
1. like ChatGPT, max 30s

87. AI Conf 2025
Solutions
1. like ChatGPT, max 30s
2. extend 30s timeout

88. AI Conf 2025
Solutions
1. like ChatGPT, max 30s
2. extend 30s timeout
Goals
● ↓ the response time
● ↓ the inference costs

89. AI Conf 2025
Inference
➔ AWS Lambda
➔ Amazon Fargate
➔ Amazon EC2

90. AI Conf 2025
Inference
➔ AWS Lambda
➔ Amazon Fargate
➔ Amazon EC2
➔ Amazon SageMaker

91. AI Conf 2025
Inference
➔ AWS Lambda
➔ Amazon Fargate
➔ Amazon EC2
➔ Amazon SageMaker
➔ Amazon Bedrock / Q

92. AI Conf 2025
Inference
Needs
➔ GPU
➔ AWS Lambda
➔ Amazon Fargate
➔ Amazon EC2
➔ Amazon SageMaker
➔ Amazon Bedrock / Q

93. AI Conf 2025
Inference
Needs
➔ GPU
➔ Model loading
✖
✖
➔ AWS Lambda
➔ Amazon Fargate
➔ Amazon EC2
➔ Amazon SageMaker
➔ Amazon Bedrock / Q
✖

94. AI Conf 2025
Inference
Needs
➔ GPU
➔ Model loading
✖
✖
➔ AWS Lambda
➔ Amazon Fargate
➔ Amazon EC2
➔ Amazon SageMaker
➔ Amazon Bedrock / Q
✖

95. AI Conf 2025
Comparison of solutions

96. AI Conf 2025
AWS Services Comparison for a Chatbot
Services Difficulty Embeddings Training $ Inference $
Amazon Q
Business
$0.264 / hour
/ 200MB
$20 / user /
mo
Bedrock
fine tuning $2 / 1000
queries
$0.0079 / 1k
tokens
$30 / hour
Bedrock
on demand
$0.00072 for input / 1k tokens
and for output / 1k tokens
Amazon
SageMaker
$2 / 1000
queries
$0.921 / hour $0.921 / hour

97. AI Conf 2025
AWS Services Comparison for a Chatbot
Services Difficulty Embeddings Training $ Inference $
Amazon Q
Business
$0.264 / hour
/ 200MB
$20 / user /
mo
Bedrock
fine tuning $2 / 1000
queries
$0.0079 / 1k
tokens
$30 / hour
Bedrock
on demand
$0.00072 for input / 1k tokens
and for output / 1k tokens
Amazon
SageMaker
$2 / 1000
queries
$0.921 / hour $0.921 / hour

98. AI Conf 2025
AWS Services Comparison for a Chatbot
Services Difficulty Embeddings Training $ Inference $
Amazon Q
Business
$0.264 / hour
/ 200MB
$20 / user /
mo
Bedrock
fine tuning $2 / 1000
queries
$0.0079 / 1k
tokens
$30 / hour
Bedrock
on demand
$0.00072 for input / 1k tokens
and for output / 1k tokens
Amazon
SageMaker
$2 / 1000
queries
$0.921 / hour $0.921 / hour

99. AI Conf 2025
AWS Services Comparison for a Chatbot
Services Difficulty Embeddings Training $ Inference $
Amazon Q
Business
$0.264 / hour
/ 200MB
$20 / user /
mo
Bedrock
fine tuning $2 / 1000
queries
$0.0079 / 1k
tokens
$30 / hour
Bedrock
on demand
$0.00072 for input / 1k tokens
and for output / 1k tokens
Amazon
SageMaker
$2 / 1000
queries
$0.921 / hour $0.921 / hour

100. AI Conf 2025
Services Embeddings $ Training $ Inference $
Amazon Q Business 190 20 (user / mo)
Bedrock fine tuning
2
1.5089 22320
Bedrock on demand 1.0714 (per 1k token)
Amazon SageMaker
2 0.0154
3.68 (serverless)
685.22 (provisioned)
AWS Services Comparison for a Chatbot
Excluded from costs: ML storage, data processing and provisioned concurrency (serverless only)
Example: 1 training of 191011 tokens + 1 request of 20s for every hour, every day for a month

101. AI Conf 2025
Services Embeddings $ Training $ Inference $
Amazon Q Business 190 20 (user / mo)
Bedrock fine tuning
2
1.5089 22320
Bedrock on demand 1.0714 (per 1k token)
Amazon SageMaker
2 0.0154
3.68 (serverless)
685.22 (provisioned)
AWS Services Comparison for a Chatbot
Excluded from costs: ML storage, data processing and provisioned concurrency (serverless only)
Example: 1 training of 191011 tokens + 1 request of 20s for every hour, every day for a month

102. AI Conf 2025
Services Embeddings $ Training $ Inference $
Amazon Q Business 190 20 (user / mo)
Bedrock fine tuning
2
1.5089 22320
Bedrock on demand 1.0714 (per 1k token)
Amazon SageMaker
2 0.0154
3.68 (serverless)
685.22 (provisioned)
AWS Services Comparison for a Chatbot
Excluded from costs: ML storage, data processing and provisioned concurrency (serverless only)
Example: 1 training of 191011 tokens + 1 request of 20s for every hour, every day for a month

103. AI Conf 2025
Services Embeddings $ Training $ Inference $
Amazon Q Business 190 20 (user / mo)
Bedrock fine tuning
2
1.5089 22320
Bedrock on demand 1.0714 (per 1k token)
Amazon SageMaker
2 0.0154
3.68 (serverless)
685.22 (provisioned)
AWS Services Comparison for a Chatbot
Excluded from costs: ML storage, data processing and provisioned concurrency (serverless only)
Example: 1 training of 191011 tokens + 1 request of 20s for every hour, every day for a month

104. AI Conf 2025
Services Embeddings $ Training $ Inference $
Amazon Q Business 190
Bedrock fine tuning
2
1.5089 22320
Bedrock on demand 1.0714 (per 1k token)
Amazon SageMaker
2 0.0154
3.68 (serverless)
685.22 (provisioned)
AWS Services Comparison for a Chatbot
Excluded from costs: ML storage, data processing and provisioned concurrency (serverless only)
Example: 1 training of 191011 tokens + 1 request of 20s for every hour, every day for a month

105. AI Conf 2025
Thanks
for listening!

106. Thank you!
>>AI CONF 2025
👉 slides & videos: https://www.improove.tech/videos