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Compare Aporia and IBM Watson Studio 
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At a Glance
Market Segments
Small-Business (55.9% of reviews)
Enterprise (51.3% of reviews)
Entry-Level Pricing
Contact Us Per Year
No pricing available
Aporia
Star Rating
(68)4.8 out of 5
Market Segments
Small-Business (55.9% of reviews)
Pros & Cons
Entry-Level Pricing
Contact Us Per Year
Browse all 2 pricing plansIBM Watson Studio
Star Rating
(164)4.2 out of 5
Market Segments
Enterprise (51.3% of reviews)
Pros & Cons
Entry-Level Pricing
No pricing available
Learn more about IBM Watson StudioSummary
AI Generated Summary
AI-generated. Powered by real user reviews.
- Users report that IBM Watson Studio excels in its breadth of data sources, scoring 8.9, which allows for extensive data integration, while Aporia, although strong, has not been rated in this area, potentially limiting its versatility in data handling.
- Reviewers mention that Aporia shines in ease of use with a score of 9.4, making it more accessible for small businesses, whereas IBM Watson Studio, with a score of 8.0, may require a steeper learning curve for new users.
- G2 users highlight that Aporia's quality of support is exceptional, scoring 9.6, which is significantly higher than IBM Watson Studio's 8.2, indicating that users may find more responsive assistance with Aporia.
- Users on G2 report that IBM Watson Studio offers superior scalability with a score of 9.0, making it a better choice for enterprise-level applications, while Aporia's scalability is rated at 8.9, which may be less robust for larger organizations.
- Reviewers mention that Aporia's no-code capabilities are impressive, scoring 9.7, which allows users to build models without extensive programming knowledge, while IBM Watson Studio's no-code features are not highlighted, suggesting a more technical approach may be necessary.
- Users say that IBM Watson Studio's deployment flexibility is noteworthy, with a score of 9.8 for ease of deployment, compared to Aporia's 8.7, indicating that IBM may provide a smoother setup process for users looking to implement their solutions quickly.
Pricing
Entry-Level Pricing
Aporia
Scaler
Contact Us
Per Year
For ML teams with multiple production models
- Everything included in ML Starter
- Unlimited rows and predictions
- Unlimited users
IBM Watson Studio
No pricing availableFree Trial
Aporia
Free Trial is available
IBM Watson Studio
No trial information availableRatings
Meets Requirements
9.3
57
8.3
121
Ease of Use
9.2
59
8.0
122
Ease of Setup
9.3
21
7.6
100
Ease of Admin
9.1
13
7.8
95
Quality of Support
9.5
54
8.2
113
Has the product been a good partner in doing business?
9.5
13
8.0
94
Product Direction (% positive)
9.6
59
8.5
115
Features by Category
Data Source Access
Not enough data
9.0
13
Not enough data
9.3
12
Not enough data
9.2
14
Data Interaction
Not enough data
9.0
14
Not enough data
9.2
12
Not enough data
9.4
12
Not enough data
9.1
13
Not enough data
9.2
12
Not enough data
9.2
13
Not enough data
9.1
13
Not enough data
9.6
12
Data Exporting
Not enough data
9.4
12
Not enough data
9.2
12
Not enough data
9.2
12
Generative AI
Not enough data
Not enough data
Deployment
8.7
29
8.8
8
8.9
31
9.2
8
9.0
26
9.0
8
8.5
28
9.4
8
9.0
29
8.8
8
Deployment
8.7
30
9.0
8
9.0
32
8.8
8
8.8
28
8.8
8
9.0
32
9.4
8
9.1
31
9.2
8
Management
8.7
26
9.3
7
9.3
29
9.6
8
8.7
28
9.0
7
8.8
26
9.0
8
Operations
9.0
34
9.0
8
8.7
29
9.0
8
8.6
28
9.3
7
Management
8.7
26
9.5
7
9.2
31
9.4
8
8.7
29
8.8
7
Generative AI
9.4
6
Not enough data
9.2
6
Not enough data
System
Not enough data
9.0
12
Model Development
Not enough data
8.5
33
Not enough data
8.8
34
Not enough data
8.5
35
Not enough data
8.3
36
Model Development
Not enough data
9.4
13
Machine/Deep Learning Services
Not enough data
8.5
27
Not enough data
8.5
34
Not enough data
Feature Not Available
Not enough data
8.6
28
Machine/Deep Learning Services
Not enough data
8.9
12
Not enough data
9.0
12
Deployment
Not enough data
8.5
32
Not enough data
8.6
33
Not enough data
8.6
30
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Agentic AI - Data Science and Machine Learning Platforms
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Not enough data
Setup
Not enough data
8.6
7
Not enough data
8.3
7
Not enough data
9.7
6
Data
Not enough data
8.6
7
Not enough data
8.6
7
Analysis
Not enough data
9.7
6
Not enough data
8.1
7
Not enough data
8.1
7
Not enough data
8.3
7
Not enough data
8.8
7
Not enough data
8.1
7
Not enough data
7.9
7
Customization
Not enough data
9.0
7
Not enough data
8.1
7
Not enough data
9.2
6
Generative AI
Not enough data
Not enough data
Not enough data
Not enough data
Scalability and Performance - Generative AI Infrastructure
9.7
11
Not enough data
9.2
11
Not enough data
9.5
11
Not enough data
Cost and Efficiency - Generative AI Infrastructure
8.8
11
Not enough data
9.2
11
Not enough data
9.5
11
Not enough data
Integration and Extensibility - Generative AI Infrastructure
8.8
11
Not enough data
9.2
11
Not enough data
9.5
11
Not enough data
Security and Compliance - Generative AI Infrastructure
9.5
11
Not enough data
8.9
11
Not enough data
9.2
11
Not enough data
Usability and Support - Generative AI Infrastructure
9.7
11
Not enough data
8.8
11
Not enough data
Large Language Model Operationalization (LLMOps)Hide 15 FeaturesShow 15 Features
Not enough data
Not enough data
Prompt Engineering - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Inference Optimization - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Garden - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Custom Training - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Application Development - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Model Deployment - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Guardrails - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Model Monitoring - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Security - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Not enough data
Not enough data
Gateways & Routers - Large Language Model Operationalization (LLMOps)
Not enough data
Not enough data
Statistical Tool
Not enough data
8.0
14
Not enough data
8.4
15
Not enough data
8.1
15
Data Analysis
Not enough data
8.7
15
Not enough data
9.0
14
Decision Making
Not enough data
8.6
14
Not enough data
8.6
15
Not enough data
8.3
13
Not enough data
8.7
14
Generative AI
Not enough data
9.3
5
Not enough data
8.3
5
Categories
Categories
Shared Categories
Aporia and IBM Watson Studio are categorized as MLOps Platforms
Unique Categories
Aporia is categorized as Large Language Model Operationalization (LLMOps) and Generative AI Infrastructure
IBM Watson Studio is categorized as Text Analysis, Data Science and Machine Learning Platforms, Predictive Analytics, and Data Preparation
Reviews
Reviewers' Company Size
Aporia
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
IBM Watson Studio
Small-Business(50 or fewer emp.)
Mid-Market(51-1000 emp.)
Enterprise(> 1000 emp.)
Small-Business
(50 or fewer emp.)
Mid-Market
(51-1000 emp.)
Enterprise
(> 1000 emp.)
Reviewers' Industry
Computer Software
20.6%
Information Technology and Services
11.8%
Computer & Network Security
10.3%
Financial Services
5.9%
Telecommunications
4.4%
Other
47.1%
Information Technology and Services
15.8%
Computer Software
13.3%
Telecommunications
8.2%
Banking
7.6%
Education Management
5.7%
Other
49.4%
Alternatives
Discussions
Aporia has no discussions with answers
IBM Watson Studio has no discussions with answers
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