MotherDuck

Year founded: 2022

Total funding: $47.5 million

Valuation: $175 million

Key investors: Andreessen Horowitz, Redpoint, Altimeter, Amplify

What it does: MotherDuck is the commercial entity behind DuckDB, an emerging database tool that works on local devices.

What investors are saying: DuckDB is considered by investors a novel and highly promising technology as an alternative to big, expensive high-powered products like Snowflake and BigQuery. However, the technology is applied in only a handful of cases and largely experimental.

But investors point to its highly flexible nature, where it treats databases like libraries, and the opportunities it brings to moving analytics onto local devices. That could manifest in the form of real-time analytics tools that are updated and rendered much more quickly than a product like Tableau.

Elementl

Year founded: 2018

Total funding: $15.8 million

Valuation: $60 million, according to PitchBook

Key investors: Sequoia Capital, Index, Amplify, Slow Ventures, Coatue

What it does: Elementl is the commercial entity behind Dagster, an orchestration tool companies use to ensure data operations happen in the correct order without failure.

What investors are saying: Many investors are highly skeptical of Elementl given the nearly omnipotent presence of Airflow. It's unclear whether it will be able to take on Airflow, and an alternative product exists in the form of Prefect.

Investors say Dagster takes a very modern approach to orchestration and, like Prefect, has an opportunity to unseat Airflow. How quickly it gains adoption is an open question, making Elementl a risky proposition.

A wrinkle recently emerged with the transition of its highly respected founder and CEO, Nick Schrock, to chief technology officer. This followed some concerns about the product's growth.

Convex

Year founded: 2020 

Total funding: $29.6 million

Valuation: $125 million, according to PitchBook

Key investors: Andreessen Horowitz, Elad Gil, SV Angel, General Catalyst

What it does: Convex creates tools that enable JavaScript developers to have consistent data across all services without using a database.

What investors are saying: For investors who believe in the impending collision of database design paradigms — online analytical processing (OLAP) and online transaction processing (OLTP) — Convex sits in a high-value spot. The differences between the two types of systems, however, is converging to a point that people are only concerned with the time it takes to process a request, insiders say.

Mosaic

Year founded: 2020

Total funding: $33.7 million, according to PitchBook

Valuation: $136.7 million, according to PitchBook

Key investors: Lux Capital, Correlation Ventures, DCVC

What it does: Mosaic creates tools to Excellerate the efficiency of training machine-learning models.

What investors are saying: While some companies can afford to spend the time and resources to train a model with billions of parameters, most machine-learning problems don't require that scale. MosaicML creates tools that help companies analyze the trade-offs of computing, size, speed, and other areas to optimize their training of those models.

Investors point to its cofounder Naveen Rao and his background as the head of AI at Intel as reasons they are excited about the company. But they consider MosaicML to be in the "dark-horse" category, as optimizing training is seen as a considerably difficult problem.

Kumo

Year founded: 2021

Total funding: $36.6 million

Valuation: $250 million, according to PitchBook

Key investors: Sequoia Capital, Frank Slootman, Clem Delangue, SV Angel

What it does: Kumo provides companies with a way to automatically generate predictions based on customer data. It uses tools like the lingua franca of analysts, the Structured Query Language.

What investors are saying: Investors say the field Kumo is operating in is one of the more difficult ones in analytics and machine learning.

That makes it a high-risk, high-reward bet. It sits at the convergence of analytics and machine learning, which is ideal for customers but traditionally relies on a hodgepodge of tools duct taped together. Analytics tools often run on the SQL programing language, while the latter uses machine-learning and data-science packages running in Python.

Prefect

Year founded: 2018

Total funding: $49 million, according to PitchBook

Valuation: $282 million, according to PitchBook

Key investors: Bessemer Venture Partners, Tiger Global Management, Quiet Capital

What it does: Prefect is an orchestration tool that manages data ingestion and usage. It was originally built for data science.

What investors are saying: Prefect was able to exploit an early opportunity in the weakness and general dissatisfaction with Airflow, particularly among data scientists. While Airflow was considered a go-to tool, it didn't support more advanced and faster use cases required for machine-learning tools.

But with Airflow expanding its capabilities, and now with a larger commercial entity behind it, investors are not certain Prefect will be able to unseat the product. They also note that Prefect is not the only tool trying to do so after the emergence of Dagster.

Hightouch

Year founded: 2018

Total funding raised: $54.2 million

Valuation: $450 million

Key investors: Barr Moses, Amplify Partners, Iconiq, Y Combinator, Bain Capital Ventures

What it does: Hightouch creates a tool called reverse ETL, or extract, transform, load, for moving data from databases like Snowflake and BigQuery into tools like Salesforce and Segment.

What investors are saying: The explosive growth of data-warehousing tools like BigQuery and Snowflake, as well as the rise of companies like Databricks, has reshaped how data analysis happens. While data from Salesforce and other tools is exported to Snowflake, Hightouch and its primary competitor, Census, exist to move it back into Salesforce.

Investors have been impressed with the growth of Hightouch's business, as it has gone through a series of pivots, like moving from focusing on marketing to engineers, to eventually land on building a reverse-ETL tool.

Adept

Year founded: 2021

Total funding: $65 million

Valuation: Unknown

Key investors: Greylock, Addition Capital

What it does: Adept uses machine-learning models trained on massive amounts of conversational text to create a simple input that translates into actions. One example would be telling its tool, ACT-1, to create an entry in a spreadsheet.

What investors are saying: The company counts two of the writers of the seminal paper on today's most-widely used AI technique, "Attention Is All You Need," as part of its team, Ashish Vaswani and Niki Parmar. That technique powers the majority of modern AI tools like large language models, and the rest of its team comes from high-profile places like Google Brain.

Hex

Year founded: 2019

Total funding: $73.5 million

Valuation: $352 million, according to PitchBook

Key investors: Databricks, Snowflake, Redpoint, Amplify Partners, Andreessen Horowitz

What it does: Hex is a collaborative tool for data analysis that enables data scientists and analysts to work in the same workspace.

What investors are saying: Hex sits at the center of the increasingly overlapping roles of a data scientist and a data analyst. While both traditionally specialize in different languages, SQL for the former and Python for the latter, Hex creates a space where each can be used interchangeably for data analysis.

It's also connected to and built on top of modern data tools like Snowflake and Databricks. Investors' interest is piqued by its ability to bring analysis and visualization of data closer to where the coding is happening. That potentially cuts out the need for complicatged dashboards and allows data scientists and analysts to create more robust visualizations to explain trends in company data.

Census

Year founded: 2018

Total funding raised: $80.3 million

Valuation: $560 million, according to PitchBook

Key investors: Sequoia Capital, Andreessen Horowitz, Insight Partners, Tiger Global Management

What it does: Census creates tools that help companies move data from data warehouses like BigQuery and Snowflake back into their apps like Salesforce or Segment. It's a tool that's colloquially called reverse ETL.

What investors are saying: Like Hightouch, Census' growth has dovetailed with the shift in modern data-analysis workflows to Snowflake via tools like DBT. Some investors consider Hightouch and Census relatively close competitors, though they say Hightouch has been more aggressive and public in its growth efforts.

RudderStack

Year founded: 2014

Total funding: $82 million

Valuation: $576 million, according to PitchBook

Key investors: Insight Partners, Kleiner Perkins, S28 Capital

What it does: RudderStack builds a data-pipeline tool that tracks how customers interact with a product — from paying for it to engaging with it. It then creates tools for different teams at a company to use insights drawn from that data.

What investors are saying: RudderStack also provides reverse-ETL tools, like Hightouch and Census do, though it does not focus on them as a stand-alone business. RudderStack does raise some concerns for investors over whether it is trying to replace a customer-data-pipeline tool and whether that will be successful.

Materialize

Year founded: 2019

Total funding: $100.5 million

Valuation: $600 million, according to PitchBook

Key investors: Kleiner Perkins, Redpoint, Lightspeed Venture Partners

What it does: Materialize builds databases suited for real-time use cases that require very-fast processing and analytics.

What investors are saying: Real-time data pipelines are becoming increasingly important to the biggest players in the space, such as Snowflake and Databricks. Materialize specializes in that element, which has long been seen as a bit of an Achilles' heel for Snowflake. The problem is more complex than what some describe as "syntactic sugar," which gives the company's specialization an edge to coexist with those larger players.

Stability AI

Year founded: 2020

Total funding raised: $111 million

Valuation: $1 billion

Key investors: Coatue, Lightspeed Venture Partners

What it does: Stability AI is the backer of Stable Diffusion, a popular text-to-image AI generator. It's widely available for free and has caught fire on the internet, while OpenAI's DALL-E 2 text-to-image generator is not freely available.

What investors are saying: Stability AI was able to very quickly get a sign-off by much of the venture world given the rampant popularity of the product after it launched. Investors say the company is looking at a model similar to a holding company.

There are a number of concerns about the company and its relationship with Stable Diffusion. A separate research firm, Runway, built what would become Stable Diffusion. Stability AI issued a takedown request to Hugging Face for Stable Diffusion on Runway's code repository, though it was later rescinded.

Investors, however, say the company has engaged in outreach to a very large number of venture firms compared with its competitor Midjourney, which insiders say has largely been very quiet.

Jasper

Year founded: 2018

Total funding: $146.69 million, according to PitchBook

Valuation: $1.5 billion

Key investors: Insight Partners, Coatue, Bessemer Venture Partners, Foundation Capital

What it does: Jasper uses OpenAI's text-generation tool GPT-3 to create marketing copy for a website.

What investors are saying: Jasper has had one of the fastest ramps in annual recurring revenue, signaling the value that an application built on top of generative AI can have. It's an early company to build a business on top of tools like GPT-3, with investors saying it's a sign of how quickly those types of businesses can be built in what they're calling the "app layer."

Snorkel

Year founded: 2019

Total funding raised: $138.3 million, according to PitchBook

Valuation: $1 billion, according to PitchBook

Key investors: Lightspeed Venture Partners, GV, Greylock, Addition, BlackRock

What it does: Snorkel gives companies a way to automatically label data, such as images, that they can feed into machine-learning models and monitor their performance. Labeling is traditionally very costly and requires a lot of manual intervention, which Snorkel tries to automate.

What investors are saying: While many bring up Snorkel as a clear emerging powerhouse thanks to its adoption, there is a lot of uncertainty over how successful it will be. That's due to the scale and difficulty of what it is trying to build. The leadership team comes from Palantir, where they have put together a business model that should gain early traction, they say.

Tecton

Year founded: 2019

Total funding raised: $160 million

Valuation: $900 million

Key investors: Kleiner Perkins, Sequoia Capital, Andreessen Horowitz, Databricks, Snowflake

What it does: Tecton creates a tool called a feature store, which helps companies manage their machine-learning models. It allows companies to save computational work that might be repeated when updating machine-learning models.

What investors are saying: Tecton is one of the more controversial companies in investor discussions, with many saying the whole category of feature stores was overhyped and didn't justify an independent company. But with increasing interest in real-time machine-learning applications, particularly from Snowflake and Databricks, Tecton is back in focus.

Investors and insiders say Tecton has surprised the venture community with the customers it's acquired, in addition to the success of its open-source project Feast. And with the increasing prevalence of real-time use cases, Tecton is set to power a wide variety of new products.

Hugging Face

Year Founded: 2016

Funding raised: $164.9 million, according to PitchBook

Valuation: $2 billion

Key investors: Lux Capital, Sequoia Capital, Coatue, Addition, SV Angel, Kevin Durant

What it does: Hugging Face hosts pretrained machine-learning models that anyone can download and use to immediately build AI-powered products. In addition, it's trained its own massive large language model called Bloom, which it has made open source.

What investors are saying: Hugging Face has become a beloved developer tool and one of the largest communities of machine-learning and data-science practitioners. And most of the top large tech companies, such as Meta, Google, and Microsoft, use Hugging Face in some form.

Investors say Hugging Face has built a massive moat thanks to its enthusiastic community. It also has a competitor for Streamlit, which Snowflake acquired for $900 million earlier this year.

Cohere

Year founded: 2019

Funding raised: $164.9 million, according to PitchBook

Valuation: Unknown

Key investors: Index Ventures, Tiger Global, Fei-Fei Lin, Geoff Hinton

What it does: Cohere serves sophisticated large language models that allow businesses to better understand a variety of data they are collecting. They could use Cohere to parse customer-support tickets and understand what the issues are, for example.

What investors are saying: While there is some uncertainty around whether the company could be successful, most point to the investments and involvement of Fei-Fei Lin and Geoffrey Hinton, two critical figures in the AI community, as a sign of its potential.

Investors say it could be successful in working with companies that need models that are more sophisticated than those that Hugging Face offers. But there's also some caution around whether companies like OpenAI and Google could offer a more competitive, lower-cost product.

Weights & Biases

Year founded: 2017

Total funding raised: $200 million

Valuation: $1.04 billion, according to PitchBook

Key investors: Bloomberg Beta, Insight Partners, Coatue, Felicis Ventures, Nvidia

What it does: Weights & Biases creates tools that help machine-learning practitioners monitor the quality and performance of their models. They can use them to determine how to fine-tune their models.

What investors are saying: Weights & Biases has constantly surprised investors with its performance from both a community-growth and a business-growth perspective. They typically lump Weights & Biases in with Hugging Face as a startup that is a clear winner in the category and expected to be one of the more successful machine-learning startups.

Investors also say Weights & Biases has been immensely successful in its fundraising and performance thanks to its founder, Lukas Biewald, who is a large part of the reason it reached its valuation.

Monte Carlo

Year founded: 2019

Total funding: $240.25 million

Valuation: $1.6 billion, according to PitchBook

Key investors: Redpoint, Iconiq, Salesforce, GGV, IVP, Accel

What it does: Monte Carlo helps companies monitor the quality of their data that is used in analytics and machine-learning tools. 

What investors are saying: The company is a somewhat divisive syllabu among investors. The ability to do forensics on data could be of high value, and all point to Monte Carlo's cofounder Barr Moses as a key factor in its success.

But some are concerned about the potential of the problem space and that its valuation may be getting ahead of its growth, though it is growing quickly.

Astronomer

Year founded: 2015

Total funding: $282.9 million

Valuation: Less than $1.3 billion

Key investors: Salesforce, Insight Partners, Sutter Hill Ventures, Venrock, JPMorgan

What it does: Astronomer is the commercial entity that backs Airflow, the largest open-source tool for organizing and managing data tasks.

What investors are saying: Astronomer stewards a tool that has almost universal adoption in data-management stacks. Airflow, originally built at Airbnb, is a ubiquitous tool companies use to ensure that data ingestion and analysis tasks happen in the right order and don't break down.

Investors point to how Airflow has nearly universal penetration in companies that rely on processing data in big chunks to update their tools and machine-learning models, also known as batch processing. Astronomer is seen by some to have revived Airflow after it stagnated without proper leadership.

But investors point to some consistent discontent with Airflow as an opening for a competing orchestration product, such as Prefect or one built by a larger company. Still, it's not clear whether there will be a product that can unseat Airflow.

Anyscale

Year founded: 2019

Total funding: $259 million, according to PitchBook

Valuation: $1.01 billion, according to PitchBook

Key investors: Andreessen Horowitz, Addition Capital, Intel Capital, Foundation Capital, New Enterprise Associates

What it does: Anyscale is the commercial entity behind Ray, an open-source tool that helps companies distribute their computational work across multiple hardware instances.

What investors are saying: It has attracted some of the same fervor as Databricks, as it's seen as a company that could create a new generation of data-management software. While the company hasn't necessarily built a business yet, it's hinging its success on the quick uptick in adoption of Ray. And as machine learning becomes more ubiquitous, investors are betting that Ray — like Databricks — will become a go-to tool for developers.

Starburst

Year founded: 2017

Total funding: $431.12 million, according to PitchBook

Valuation: $3.35 billion

Key investors: Index Ventures, Salesforce Ventures, Andreessen Horowitz, Coatue

What it does: Starburst builds an optimized engine for querying and analyzing data across multiple data-storage providers, whether in the cloud or on premise.

What investors are saying: Investors point to Starburst and its backing of the open-source tool Presto. Some investors were surprised by its initial growth, with Presto being a compromise for companies that couldn't fully migrate to a cloud-native tool like Snowflake or Databricks.

In particular, investors point to its works with the on-premise-database provider Teradata, which many companies rely on for on-premise data analysis. Starburst is a tool that's well positioned to get the business of companies that will never make a full migration to the cloud, they say. However, there is some concern over whether a query engine such as Starburst or Dremio can be a successful long-term business.

DBT Labs

Year founded: 2016

Total funding: $414.64 million

Valuation: $4.2 billion, according to PitchBook

Key investors: Coatue, GV, Iconiq, Salesforce, Snowflake, Databricks, Andreessen Horowitz

What it does: DBT Labs builds and runs the open-source tool DBT, which helps developers ensure data inside products like Snowflake is clean and easily accessible.

What investors are saying: DBT has become ubiquitous in developer tool kits and is considered one of the catalysts of the shift toward modern data practices.

Growing in tandem with Snowflake and Databricks, DBT runs a key part of the data-management process. Its presence continues to quickly grow, especially with the launch of its company-metrics-management tool and support for the programming language Python.

Beyond that, DBT Labs has built a cultlike following for its products and is considered one of the best companies when it comes to developer relations.

Grafana Labs

Year founded: 2014

Total funding: $569.23 million, according to PitchBook

Valuation: $6 billion, according to PitchBook

Key investors: Sequoia, Lightspeed Venture Partners, Coatue, JPMorgan

What it does: Grafana builds tools for observability, or helping companies monitor the quality and performance of the data used in various parts of their operations. It runs the open-source Grafana project, one of the biggest tools in observability.

What investors are saying: Grafana has quickly become a go-to tool for developers as more and more companies invest in analytics and machine learning.

It recently hit $100 million in annual recurring revenue, and investors are consistently impressed with the feedback its products get from its customers. But it plays in a crowded space with several other startups.

One investor pointed to Datadog, a $25.5 billion public company, for comparison, though Grafana is an open-source vendor, which there will always be room for, this investor said.

ThoughtSpot

Year founded: 2012

Total funding: $677.45 million, according to PitchBook

Valuation: $4.2 billion

Key investors: Lightspeed Venture Partners, Capital One, Khosla, General Catalyst, Fidelity, Snowflake

What it does: ThoughtSpot is a business-intelligence platform designed for more advanced data queries and use cases than Tableau and other tools can offer.

What investors are saying: ThoughtSpot is considered one of the better-positioned companies to unseat traditional visualization tools like Tableau and Looker. But investors say the tools generally are used together for different purposes.

They also consider its move to pivot to the cloud aggressive and highly risky but ultimately very rewarding, they say. It has put a lot of confidence in the leadership team to carry it across the finish line as a public company.