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
Jump to a section
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 Weights & Biases
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 Weights & Biases 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 Weights & Biases ensuring efficient collaboration
Social proof