Build vs. Buy

Weighing the value of in-house development against a platform trusted by the world’s leading ML teams

In-House Solution

Weights & Biases

ROI of ML projects

The inability to handle a large number of experiments could drastically affect productivity and result in developing less performant models
Real-world ML systems commonly incur massive ongoing costs due to the maintenance problems of traditional code plus an additional set of ML-specific issues

Capable of running thousands of experiments iteratively and collaboratively to build successful models fast and deploy them into production
Automatically track and optimize GPU utilization alongside model performance metrics which can generate 30-50% utilization gains

Development team

Building custom software often requires multiple engineers. Depending on how many end users the tool serves, the number of resources needed for development increases
The average salary of a software engineer in the US is $125,000
Developing for enterprise could require as many as: 5 dedicated engineers or more, which equates to $625,000+

Founded by a leadership team serving customers in ML for over 10 years
Increasing the size of the product and engineering staff by more than 55% year over year to solve the day-to-day problems of ML practitioners
Performs monthly high-quality releases to ensure the platform is consistently improving, pivoting with market changes, and meeting customers’ expectations

Time to value

The general software development timeline:
– Planning & Requirements: 2-4 weeks
– Design & Architecture: 2-4 weeks
– Development: 3-8 Months
– Implementation: 2-4 weeks
– Testing & Compliance: 3-6 weeks
– Total Potential Time: 6-12 months
On average, companies take 3-6 months to onboard an engineer

Get up and running in 60 seconds
Run your first experiment in 5 minutes
On average, it takes 3-4 weeks to onboard users on W&B

Maintenance

Typically takes 1-4 engineers to monitor, troubleshoot and improve internal tooling
– Total potential cost:
$125,000 – $500,000
Requires at least one rotating engineer for support to deal with incidents
Reliant on the developers involved to implement any new features or upgrades — costing time and resources. And, if they leave the company, there will be little to no support

50+ engineers building, improving, and maintaining W&B based on customer feedback and pivoting with market changes
Maintains a high NPS of over 75
Ensures uptime is greater than 99.95%
Customer Success, Support, and Solutions Architecture teams are here to continue to help customers enable adoption, troubleshooting, and training

Interoperability

Ensuring systems can coexist with each other is incredibly complex
Integration challenges risk creating bottlenecks further down the line

Integrated into every popular ML framework
Instrumented into over 9,000 popular ML repos
Tech partner integrations are a turnkey experience — Amazon Sagemaker, Vertex AI, and Kubeflow to name a few

Scalability

Planning for scale is often overlooked in internal development, leading to high maintenance costs, poor user experience, and even cascading failures
At times, as the solution scales, the team may need to rearchitect their tech stack, resulting in huge inefficiencies

Automatically scales to handle the artifacts and metadata that are logged as users run thousands of experiments
Multiple teams and projects can leverage research findings and progress from W&B ensuring efficient collaboration

Never lose track of another ML project