ML Monitoring Build vs. Buy

Making the Right Choice for Your ML Strategy

A comprehensive ML monitoring and performance solution is a critical component of successful enterprise production AI.

Many MLOps teams consider building in-house monitoring software, which can be costly in terms of time, resources, and financial investment.

CLIENT BUILD IN-HOUSE DISCOVERY

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DEVELOPMENT BUILD TIMEFRAME

Please select one:

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RESOURCE ALLOCATION

Please fill out any fields that are relevant to your potential in-house build project staffing.

full-time employees
amount
Annual Salary
contractors
amount
Annual cost

Project Manager

DevOps

Data Engineer

Data Scientist

Backend Developer

Frontend Developer

UX/UI Product Designer

QA Engineer

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CLOUD COMPUTING & INFRASTRUCTURE

Enterprises have many choices when it comes to selecting cloud computing vendors to help support MLOps scalability including Amazon AWS, Google GCP or Microsoft Azure.

Below enter the estimated annual cost you expect to incur based on the number of servers, GPU and backoffice infrastructure required.

ARTHUR ON PREMISES PRICING TOOL

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number of models

Select the number of models your prospect has.

Total number of models
Price Per model per year
Total Price per year*
10
$10,000
$180,000
25
$7,800
$275,000
75
$6,250
$548,750
100
$5,700
$650,000
250
$4,400
$1,180,000
500
$3,200
$1,680,000
1000
$2,600
$2,680,000
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enterprise license contract

Specify the enterprise license contract length.

Compare costs

Compare build vs. buy monitoring and performance platform costs

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YOUR COST

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Arthur Platform

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your savings

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Why Choose Arthur AI?

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Seamless Integration - Easily integrates with your existing AI systems, ensuring a smooth transition and immediate impact.

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Scalability - Designed to grow with your business, Arthur AI effortlessly scales to meet your evolving needs.

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Expert Support - Gain access to our team of AI experts, providing you with the guidance and support to maximize your AI's performance.

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Advanced Analytics & Insights - Arthur AI delivers unparalleled analytics and insights, enabling businesses to derive actionable intelligence.

“It takes companies an average of 1-2 years or more to build, test, and launch a basic in-house ML monitoring solution.”

Driving impact at scale from automation and AI
Digital / McKinsey report - 2023

See what Arthur can do for you.
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The decision to build or buy is crucial for ensuring the long-term success and efficiency of your AI operations.

Get a demo
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Additional Benefits of Arthur

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Accelerate your MLOps GTM launch with a SaaS installation or select Arthur’s on-premise solution which provides the capability to deploy the platform in your data center or virtual private cloud.

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Measure model performance with custom and out-of-the-box metrics for tabular, NLP, and CV models.

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Keep sensitive data confidential by ensuring proper access control protocols that ensure only users with proper permissions can view the data.

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Achieve greater team efficiency. The solution is fully-managed and supported by our engineers 24/7

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Ingest models faster with a highly scalable microservices architecture that can ingest up to 1MM transactions per second

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Innovate more quickly.  Free your in-house team to work on more strategic and visionary projects.