The machine learning developer platform raised $2.3 Mn in its seed funding round led by Sequoia India and Southeast Asia'...
Invest in TrueFoundry
"Like TrueFoundry’s team, we believe that in the future, companies will not be spending so much engineering bandwidth on a ML platform layer. Instead, they’ll start with a streamlined system that also supports multi-cloud from day one," said Hadley Harris, founding GP of Eniac Ventures. More and more businesses are upgrading existing models and releasing new ones to gain a competitive edge.
Read more about TrueFoundry's highlights in the news:
Problem
Enterprise spend on ML teams and the infrastructure cost of training the models have skyrocketed...
As language models get more complex, they also get more expensive to create and run, locking out a lot of companies. It's very hard for places with smaller budgets to get the same level of results as tech giants like Google. Additionally, building out a high performance data science team with extensive data literacy skills is a hefty investment, since the average salary for a data scientist working in the U.S. is $195K. Research indicates that the number will likely rise, as the U.S. is predicted to experience a shortage of 250K data scientists by 2024.
...yet only a handful of businesses reap value from ML:
Despite the astronomical costs, many companies are willing to invest in ML models to improve their products. However, these projects often fail because they tend not to account for the different requirements of different organizations with varying levels of maturity. The statistics are jarring:
90% models never make it to production
57 days is the median time for deploying trained models
50% of deployed models break due to scaling issues
The current ML workflow — a food delivery app example
Solution
TrueFoundry accelerates ML deployment, saving companies hundreds of millions of dollars
TrueFoundry is helping users extract better value out of their ML/AI initiatives with a developer platform that both solves problems specific to ML development workflows and supports rapid deployment. Built on Kubernetes, the custom platform works as a cloud-agnostic solution that can be deployed on AWS, Google Cloud, and Microsoft Azure. The bottom line is, data is the new oil, and TrueFoundry assists companies in using ML to generate greater business value.
to speed up developer workflows — with full
security and control for the Infra team.
Product
A developer platform that significantly boosts revenue and decreases costs
Built on Kubernetes, TrueFoundry's custom platform works as a cloud-agnostic solution that can be deployed on AWS, Google Cloud, and Microsoft Azure. It helps companies validate what works quickly before spending too many resources through shadow launches, A/B tests via 1-click deployments, and feedback collection from stakeholders via web-apps as well as reduce failures by setting off system, data, and model performance monitoring dashboards/alerts the moment the model is deployed.
TrueFoundry's product comes in two parts:
A client library that allows developers to create containerized versioned services out of their ML models. This is currently available as a python client, CLI and Jupyter Notebook magic commands.
An Infrastructure layer with full support for role-based access control, secrets-management, SSO integration, and running on managed-Kubernetes cluster.
Infrastructure management
Model deployment
Integrated model
Traction
Projecting $500k by EOY '23, $4M EOY '24
TrueFoundry is beginning deployments with early paying customers targeting $25K - $50K ACV’s with mid-market companies and $100K - $200K ACV’s with enterprises. Additionally, the company has design partners in India, Southeast Asia and the U.S. that are leveraging the platform to accelerate their ML pipelines. With all this traction, TrueFoundry is projecting $500K SaaS ARR EO23 growing to $4M EO24 and targeting 90% mature gross margins.
Not only is TrueFoundry receiving a tremendous amount of engagement, but it's also getting positive feedback. More than 5% of the world’s Kaggle Masters and Grandmasters have expressed love about how the platform allows them to move their model beyond the training stage and quickly expose it to the users while following best MLOps practices.
Customers
Delivering transformative value to stakeholders within mid-market and enterprise companies increases loyalty
“I think this is amazing. I have spoken to many people in the ML Pipeline space but no one has openly told me you insert this line of code and that’s why it gives me a lot of confidence. The way you have thought of the code structure and orchestration is superb. Nothing is different to me, nothing is alien to me, and yet, it is simpler. When are you guys ready to give this out? I would like to personally use it at NRoad.” - Hrishi, Co-founder at NRoadCorp
“The TrueFoundry product was actually amazing. I have previously faced issues deploying and monitoring models, but I feel the way TrueFoundry is being built and the new features that are about to come, it will definitely solve problems for a lot of ML developers.” - Kaggle Grandmaster
TrueFoundry has iterated on ICP to narrow down to the following 2 segments:
Building with early customers who have experienced decrease in Time to Value
Business model
Diversified business model creates deep moat
TrueFoundry's solution is available in three ways depending on the use case:
SaaS Model: Users get an API key from TrueFoundry's web-based platform, deploy their model on its managed Kubernetes cluster, and get an API endpoint along with detailed dashboards
Managed Infra Model: This solution is packaged as a helm chart, meaning that the data and cluster credentials remain under the user's cloud. This does require some support from the DevOps team, but TrueFoundry makes the process easy through its Terraform code.
Hybrid Cloud Model: With this approach, one Kubernetes cluster lives in TrueFoundry's cloud and the other one lives in the user's cloud. The RDS and S3 buckets also stay in the user's cloud, making setup and maintenance much faster with minimal involvement from the DevOps team on an ongoing basis.
Market
'The future is now for ML: the $15B opportunity that will only get bigger', says McKinsey
The global ML market size was valued at $14.9B in 2021 and is forecasted to reach around $302.6B by 2030, expanding at a CAGR of 38.1%. The COVID-19 pandemic has accelerated the adoption of AI/analytics, as a PwC study found that 86% of business leaders believe AI is becoming a “mainstream technology” at their respective companies. Momentum doesn't appear to be stopping anytime soon.
TrueFoundry is in prime position to capitalize on this momentum by automating repetitive tasks in the ML pipeline to allow data scientists and engineers to focus on higher-value, more creative projects. It helps ML teams get 10x faster results and cuts their production timelines from several weeks to a few hours.
Competition
A differentiated, developer-first platform that beats competitors in speed and usability
TrueFoundry competes with the following major cloud players:
Amazon released more than 200 machine-learning features and capabilities in the last year, supporting tens of thousands of customers. This growth means that AWS revenue could hit $100B revenue milestone in 2023.
Since being acquired by Google in 2014, DeepMind has struggled to break even, but it raked in $1.1B in revenue in 2020, more than 3x the $361M it gained in 2019. It's a positive sign that Google's investment in ML/AI is starting to pay off.
Microsoft Azure is the leader in the space. Microsoft invested $1B into the startup in 2019, and it remains a crucial piece of the overall strategy, as seen in the decision to invest billions more into OpenAI.
However, TrueFoundry prioritizes developer experience and the ability to support multi-cloud from Day 0. TrueFoundry makes it easy to get started (<5 mins set up time) and put ML models to production. As user needs mature, the platform supports version control, monitoring set-up, CI/CD, Auto-scaling, etc. The gentle learning curve increases accessibility for all, which in turn increases market share.
Focus on developer experience and abstraction of infra positions TrueFoundry to outcompete SageMaker and other ML companies:
Vision and strategy
Automating the ML pipeline is easily scalable and applicable to a wide range of industries
TrueFoundry believes that 5-10 years from now, all organizations will thrive or fail based on how quickly they tie data to real business ROI, which means higher velocity of delivering ML enabled Products. Every ML developer will be able to present their model to the entire world in a matter of minutes. ML will be where compilers are today, which is exactly why TrueFoundry will win.
Funding
Backed by Sequoia India (Attentive, Beam) and Naval Ravikant (AngelList founder)
Other investors include: Eniac Ventures (Airbnb, Alloy), Builders Fund (Builders of ML platform at Salesforce), CIO @ Deutsche Bank (Dilip Khandelwal), Founder @ Kaggle (sold to Google - Anthony Goldbloom), Founder @ Rubrik (Soham Mazumdar), CTO/Founder @ ClearCo (Satwik Seshasai), CTO @ Greenhouse (Mike Boufford), CTO/Founder @ Productiv (Ashish Aggarwal), Founders @ Whatfix (Khadim Bhatti & Vara Kumar), CTO @ AlphaSense (Raj Neervannan), Sr Engineer Lead @ Google (Aditya Kalro), among others, Sr. ML Architect @ SnowFlake (Tal Shakhed), and Heads of ML at SentiLink, Migo, Upwork, among others.
Leadership
Led by serial founder with prior exit and a team of high-quality engineers from world-renowned organizations
Nikung Bajaj, CEO and co-founder, is riding a successful exit of EntHire, the OS of talent search for tech hiring needs, which was acquired by InfoEdge. Previously, he served as the tech lead for the Conversational AI Team at Facebook that launched Portal. Nikung was also the ML team lead at Reflektion, building recommender systems for 600M users at scale. He holds a masters in computer science from UC Berkeley.
The other two cofounders, Abhishek Choudhary and Anuraag Gutgutia, worked together with Nikung at EntHire before its acquisition. All three of them possess an ideal mix of technical and entrepreneurial skills. The rest of the team consists of world-class engineers from leading companies, including Gojek, PostMan, and Amazon AWS.
Team has seen and built ML infra at top tech companies
Disclaimers
In addition to the carried interest Republic Deal Room Advisor LLC is entitled to for the syndicated investments it organizes, certain principals of Republic Deal Room Advisor LLC may have a personal interests in these investments, as disclosed below. When making an investment decision please review any applicable disclosures as they represent pre-existing financial interests held by those principals of Republic Deal Room Advisor LLC.
We do not represent that the information contained herein is accurate or complete, and it should not be relied upon as such. Opinions expressed herein are subject to change without notice. Certain information contained herein (including any forward-looking statements and economic and market information) has been obtained from and/or prepared by TrueFoundry or other third-party sources and in certain cases has not been updated through the date hereof. While such sources are believed to be reliable, Republic Deal Room Advisor LLC does not assume any responsibility for the accuracy or completeness of such information. Republic Deal Room Advisor LLC does not undertake any obligation to update the information contained herein as of any future date.