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 Weights & Biases


  • 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


  • 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


  • 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

  • Incur the risk of using tools that can’t compete
  • Hundreds of companies like Graphcore, NVIDIA, and Lyft choose Weights & Biases despite having the option to build on their own
  • Co-designs the product with top-notch institutions such as OpenAI
  • Over 500,000 ML practitioners love using Weights & Biases and stay connected through Weights & Biases’ Fully Connected to share ideas, learn from industry leaders, discover the latest tools, and more

Never lose track of another ML project