Apply to Register Here

Founded in 2015, Activant is a global investment firm with $1.5 B AUM that partners with high-growth founders and teams that are transforming commerce.

Activant

We’re always looking to learn about new perspectives, new research, and new ideas.

Get in touch

Disclaimers & Terms of Use

Privacy Policy

323 Railroad Avenue
Greenwich, CT 06830
USA

Greenwich

New York

Berlin

Constantia Uitsig Wine Estate
Spaanschemat River Road
Cape Town 7806
South Africa

Cape Town

Harish is providing the Amazon Prime fulfillment experience to everyone else through a combination of AI and an asset-light network of warehouses across the United States.

Alex is automating workflows for the world’s largest enterprises by using process mining to build the connective tissue for businesses.

Mayan is delivering Autonomous IT to the largest companies in the world through systems of intelligence.

Carsten built one of the largest commerce platforms in the world, which powers enterprise merchants and retailers across the globe and now serves as the SAP commerce engine.

Soups is stopping financial crime in its tracks by building the first end-to-end platform for fraud, risk, and compliance.

Adam is enabling restaurant owners to take back their customer relationships by digitizing their entire online and offline operations in a single platform.

Activant is a research-led growth firm that invests in relentless founders

Read past issues

Monthly Newsletter

Browse Research Library

Research

Perspectives

Home

Activant is a research-led global investment firm that partners with high-growth companies.

We partner with founders who have won their initial battles and are ready for the next challenge.

We typically invest when two things come together. First, the company has the potential to achieve escape velocity and transform an entire industry. Second, we believe the founders can lead the company through hyper growth, and our support can significantly increase the probability of this success. 

The critical time window to escape competitors and transform an industry is not open for long.

That’s why after we invest, the real work begins.

About

We only partner with a few founders per year. But when we do, we’re fully committed.

Our portfolio

Companies

Read our investment rationales and market perspectives.

A team built to support and advise yours.

We invest behind insights.

View all research

Founders who aspire to build something bigger than themselves chose to partner with us at their most critical point.

Team

Andrew grew up in Northern Ireland on either side of the Good Friday Agreement in a family that lived in the thick of it. He first visited the U.S. for a summer camp in Vermont, and later moved full-time to attend Harvard University, where he studied behavioral economics.

Andrew co-founded an ed-tech platform in 2014 and ran the company for two and a half years before joining Activant in its first fund. As an early team member, Andrew has been part of scaling Activant alongside its portfolio companies. He runs the firm's New York office and is a connector of ideas and people.

"For as long as I can remember, I’ve always wanted to build companies. Partially for the curiosity, partially for the competition, but mostly to push the boundaries of what's possible. I count my lucky stars every day to wake up and work alongside some of the best founders in the world. There is no greater privilege in my book."

Edtech startup focused on development of personalized critical thinking curriculums for students.

We are hardly the first to claim a new technology is poised to upend work as we know it. With every wave of innovation comes a chorus of such pronouncements, many of which turn out to be hyperbole. Technology may make our jobs easier, but most of the time, the core of what we do remains fundamentally the same. But spend five minutes exploring ChatGPT, or any comparable Large Language Model (LLM), and it’s clear that this wave is different.

The reason is simple. Most technologies are simply tools that help us accomplish tasks and be more productive. AI, on the other hand, can think – or at least provide the appearance of thinking. It’s like having another knowledge worker on the payroll (for a fraction of the cost).

As we explore the world of AI Agents and the potential to shape how we work, we will share three perspectives that we hold at Activant and illustrate what we think is possible through a hypothetical example.

Activant Perspective 1: LLM-enabled software will autonomously accomplish highly complex tasks with increasingly less guidance.

This is the beauty (and threat) of LLMs: they can do work we thought would long be the purview of humans. It’s unsurprising, therefore, that many people worry their job will soon be automated away by AI. In the short term, this will lead to dislocation. But technological revolutions are never zero-sum; in fact, they have always been accretive. As workers adapt, they become more productive, and the companies they work for can do more with less. Some stop there, but most businesses opt to invest the gains back into their infrastructure and people. In the end, revenues go up, companies do better, and the economy grows.

That said, we are still in the very early days of this cycle – and the early days of the technology itself.

Activant Perspective 2: LLMs alone are not enough to achieve Perspective 1.

An LLM can’t complete most tasks on its own. It is merely a dense set of attention networks: essentially, statistics on a massive scale. Yes, they can engage in human-like “thought,” but to generate real value from any of these models, a human would have to integrate them into actual workflows and make use of their decision-making competence.

For example, imagine that you hired workers to lay bricks. You could lead them to a stack of bricks and explain where and how you want them laid. They could perfectly understand your instructions, but understanding alone isn’t enough because the workers would also need to be able to lay bricks.

Bricklayers need to have more than just cognition. They need to have arms, legs, and physical strength. Furthermore, they need a memory to recall how you wanted each of the bricks to be arranged, and a way of correcting any errors so they don’t cause the eventual structure to fail.

If LLMs are to extend beyond mere Q&A they need (1) a suite of tools to impact the real world, (2) memory repositories to remember what actions they’ve taken, and (3) auto-critique algorithms to error correct along the way.

When all these components come together, an “Agent” is born.

 An AI Agent is orchestration software that combines an LLM with memory, tools, and auto-critique mechanisms.

 An AI Agent is a highly advanced bot that can complete work that has been assigned to it in natural language.

Though still in their infancy, Agents are poised to fundamentally reshape the way we work. They will become natural extensions of our organizations and complete day-to-day tasks like newly hired employees. They will help us get work done, and even collaborate with others on our behalf. Eventually, they will be able to work together, breaking down tasks into atoms of work able to be handled by a full “office” of Agents, some of which orchestrate, while others specialize.

That is why this wave is different. Working together, Agents will be able to handle increasingly complex tasks, replacing knowledge workers in a variety of fields. As they do, what it means to work – and to lead – will change dramatically, as both will increasingly depend on a mix of human and human-like capital.

Activant Perspective 3: Agent teaming is the future of autonomously completed, complex work – 

Agent ecosystems have four key components:

providing “conscious thought” within AI agents

external APIs facilitating task completion and agent interaction with the real world

 data storage mechanisms that retain and recall information

the ability to correct mistakes when completing tasks

 – agents can effectively “team” and autonomously complete complex work. The exhibit below describes the individual components of agent ecosystems, and we will attempt to bring things to life using a hypothetical example. Let’s consider the job of an Activant Investment Analyst!

Re-imagining the role of an Activant Investment Analyst

Among many other things, we often rely on our analysts to create market maps, which requires them to:

Find companies that exist in that industry,

Compare these companies quantitatively and qualitatively, and

Place them on a two-dimensional grid according to this comparison.

Until recently, this was almost inconceivable as a task for machines but let’s explore how we might put our “Agent Analyst” to use.

1. Understanding the industry (and task): Large Language Models

For our Agent Analyst to understand the task at hand, it needs to comprehend instructions in natural language. While LLMs provide that ability, requests literally come in many different shapes and sizes. To compare foundation models and model providers, one would likely consider the number of parameters in a model, whether it is open-source or closed-source, and the provider modality. One might also consider whether it includes “Fine-Tuning-as-a-Service” (FTaaS), the process of training a pre-trained model on subject-matter-specific data to produce a model that performs better in specific use cases.

When comparing foundation models, the number of parameters in a model is often a good approximation of output quality. As a rule of thumb, the more parameters a foundation model has, the better its performance across different benchmarks such as reasoning, web browsing, or even game 

 Although fine-tuning a model can boost its performance on a particular task, larger models with more parameters tend to have better generalization skills, making them more useful.

As seen in the exhibit on the following page, model sizes are increasing exponentially while model performance is also increasing, but not at the same rate.

At the heart of any LLM lies its architecture: a blueprint dictating how various attention heads and layers are organized. There are also "weights,” which are numeric values assigned to parameters in the neural network, refined and adjusted through training to enhance performance. Training involves feeding data into the model, allowing it to learn pattern matchings, and then adjusting its weights based on prediction accuracy measured via various benchmarks. It's through this process that the model obtains its ability to understand and generate context dependent and human-like text.

, openly share their architecture, weights, and sometimes even their training methodologies. This approach not only reveals the intricacies of their design but also democratizes access.

Conversely, some entities opt for a more guarded approach. Closed-source models, such as GPT-4 by OpenAI, protect the details of their architecture, weights, or training processes, presenting them as a kind of “black box.” Instead of allowing direct model access, they employ APIs as intermediaries, thereby controlling and often monetizing the interaction between the end user and the model.

Since this article’s initial publication, the AI landscape has evolved significantly, and we have updated our Activant Market Map of foundational models. Some changes are significant. For example, OpenAI was previously placed on the open-source side, but the company has since focused on closed-source offerings and has deprecated many of its former open-source models.

Provider modality relates to the data type a provider offers models for. This includes various transformations such as text-to-video, text-to-text, text-to-image, and image-to-text, among others. Companies that operate in just one domain are labelled as “uni-modal”; by contrast, “multi-modal” companies provide models spanning multiple domains. As AI Agents continue to evolve, we expect a growing emphasis on modality in foundation models (more than just LLMs).

Today, OpenAI, Google, and Anthropic dominate the industry, but the next few years will see considerable competition as investment in R&D increases in a proverbial arms race. Given this uncertainty – and the promise of better models always on the horizon – it’s hard to imagine agents relying on a single provider. From an investment perspective, backing one LLM provider is essentially a vote of confidence in its R&D team, but past performance is not necessarily indicative of future results.

Having understood the industry and the task, the Agent Analyst now needs the ability to search for companies. In other words, the Agent Analyst needs a tool.

Simply put, tools are snippets of code that LLMs can use to create, modify, and utilize external resources to execute tasks they couldn’t otherwise complete on their own.

Most tools are problem specific. In this case, all the Agent Analyst needs is a connection to Google or SerpAPI, a prominent wrapper for Google’s Search API. Unsurprisingly, other tasks will require different tools. Game this out, and you quickly arrive at the need for toolkits and tool aggregators. As tasks become more complex, agents will rely on not one but a variety of tools to accomplish a single objective. Through tool aggregators, Agents will be able to access the resources they need programmatically and as those needs arise.

Single tools refer to companies or services that specialize in just one function, such as searching the internet, booking a flight, or creating audio with a realistic voice. While these offerings do have certain strengths, integrating them into a toolbox, and thus into agents, is often cumbersome. Agent libraries provide the orchestration framework that combines all the components in the Agent ecosystem, and in some prominent examples toolboxes need to be set up before an Agent itself is even initialized. This means that if there's a need to integrate additional tools later, the agent would have to be initialized all over again. Given these considerations, embedding single tools into an Agent's workflow can be quite labor-intensive and time-consuming.

By contrast, tool aggregators offer Toolboxes-as-a-Service (TaaS). Theoretically, aggregators can compile an infinite number of tools and distribute them via a single, unified API. In practice, they typically provide tools that have been identified by an Agent as necessary to complete a given task. If, however, an Agent requires additional tools, it can access them without restarting.

A subset of tool aggregators, LCNC companies could provision tools and/or provide tool-building functionality for 

 As long as they offer an API, agents can leverage these solutions as they do tool aggregators, integrating (and even custom-building) tools into their workflows on the fly.

Many LCNC platforms have existed prior to the advent of LLMs and already have large libraries of powerful, ready-built tools to draw upon. Several companies, including 

, have begun offering Agents-as-a-Service (AaaS), using these libraries to jump ahead of competitors, many of whom are starting from scratch. Others may choose to offer APIs to facilitate quick adoption within the agent ecosystem and more reliably maintain their market share.

Because of their ease of integration and ease of use, we believe tool aggregators and API-based LCNC platforms are the best positioned to take advantage of this new agent ecosystem. As platforms like Llama Index and Relevance AI grow, Agents will be able to dynamically access a vast library of toolsets without needing to update their structure or code, increasing both their functionality and value.

As our Agent Analyst has progressed, it has gained the capability to discern tasks and search for companies. However, identifying a company means nothing if an Agent can’t remember it for later use. Agents require a dedicated memory component that can store company names and their associated descriptions.

Over the years, the software industry has seen a multitude of data-structure innovations, from traditional SQL-based relational tables to modern streaming services like Kafka. Yet, to truly grasp the intricacies of Agent development and the depth of virtual cognition, it's helpful to draw parallels with human memory.

At a high level, human memory is split into three core categories: sensory memory, short-term (or working) memory, and long-term memory.

In humans, sensory memory can be thought of as the direct inputs from our various senses of taste, smell, sight, touch, and hearing. These are perceived subconsciously, processed, and then filtered for relevance.

sensory memory captures the responses generated from the tools AI Agents utilize.

 Human neural frameworks efficiently filter out redundant and useless sensory data, elevating only important information for conscious processing. The sensory memory of an Agent operates on similar principles, 

sifting through vast data while retaining only what's significant.

Over time, we believe that sensory memory will include the ingestion of many different data types,

 from unstructured data files (such as DOCX, PDF, EPUB, or recordings) to semi-structured datasets (such as messages and CRM data).

Having processed inputs on the subconscious level, Agents are left with the inputs that require conscious thought. In humans, conscious thoughts reside in working or short-term memory. When we actively think about a topic, we do not consciously remember the context for the thought, but it is nevertheless recalled. In fact, our short-term memory is believed to have the capacity of about seven items

 and lasts for 20-30 seconds. For Agents, using the context windows defined by LLMs to store short-term memory is the default solution. As context windows increase in length and new techniques for prompt engineering solve existing constraints, the context window will be an even more effective location for short-term memory.

In the context of our Agent Analyst, short-term memory can be thought of as remembering the larger task at hand (i.e., creating a market map) when deciding what smaller task (i.e., finding a company) needs to be accomplished next. In practice, this information will largely live inside the context window of the LLM API calls. While it’s essential for the Agent to complete a task, we believe that there is little opportunity for investment in short-term memory due to the limited additional infrastructure required to build it.

Once subconscious and conscious memory is in place, an Agent Analyst will need some way of saving information for longer periods. This is where long-term memory comes into play.

In humans, long-term memory is finite due to the limited capacity of the brain. As a result, we have developed two primary mechanisms to retain knowledge: repetition and adrenaline. Agents lack this capacity constraint. Instead, they are limited by the amount of memory that they could conceivably access. For example, if our Agent Analyst was integrated into our drive and needed to search for the PowerPoint example of our Market Map, the size of the context window would limit the number of folders that it could select from in its search.

In our view, connections to long-term memory repositories are no different than tools, in that they are blocks of code that can be executed and return a response.

Over the next decade, we believe that the entire ecosystem of data management will grow even faster than it has historically because of increased Agent data utilization. Agents will be able to build pipelines from company data, leverage this data in their own processes, and integrate outside data into existing workflows. As the Agent ecosystem grows, the demand for data stores will increase even more rapidly, making the volumes of unstructured company data – which today sits untouched – significantly more valuable.

We’ve listed a handful of data structures and the impact we anticipate from Agents below:

Our Agent Analyst has successfully understood the industry, used a search tool to find all the companies within it, and saved them in a database for future output. As a final step, we need the Agent to place the logos of each of those companies onto an Activant Market Map, fully understanding the significance of each company’s position.

Plotting logos, of course, is the easy part of the challenge; understanding where to place a logo in a subjective two-dimensional plane is the truly complex task. It requires iterations, constant comparisons to other companies, and perhaps even outside input from the Activant team. Therefore, the Agent needs a mechanism for critiquing itself. We call this auto-critique.

While we foresee potential challenges like defensibility, this remains a vital component of the ecosystem with several approaches underway: a) prompt engineering, b) human-in-the-loop, and c) auto-critique.

Prompt engineering is the process of forming and refining natural language statements and questions to achieve an optimal outcome from an LLM.

In practice, LLMs are only capable of comprehending and responding to inputs that fit within a defined context window. For example, GPT-4’s context window is about eight thousand tokens, meaning only about seven thousand words can be provided as context at any one 

In our view, prompt engineering is likely a temporary solution, and one we expect the industry to shift away from as LLMs improve in terms of task comprehension and context window size (though the timeline for this shift remains uncertain). Meanwhile, existing businesses in this domain have a chance to pivot their business models, leveraging their current distribution to tap into emerging opportunities.

Human-in-the-loop refers to the use of humans to validate that Agents are accurately processing and reasoning through tasks. It is just what it sounds like: leveraging humans to correct or modify the reasoning of LLMs. Currently, developers use this method when building Agents, as they want visibility into their inner workings. Because it requires that people scale linearly with computing resources, it will likely be unsustainable in the long term.

Auto-critique refers to the autonomous use of LLMs to check and modify Agent reasoning about a task. This is an autonomous process that allows Agents to improve and amend their own output by reflecting on past responses and errors, all without human intervention. To do this, Agents draw on new input from a variety of sources, including external tools, other Agents, affiliated LLMs, and heuristic functions. Once they arrive at a satisfactory result (governed by predefined metrics for evaluating self-reasoning), a stop condition is granted, allowing Agents to finalize their output.

Teaming: The Rise of the Planet of the Multi-Agent Systems

The information contained herein is provided for informational purposes only and should not be construed as investment advice. The opinions, views, forecasts, performance, estimates, etc. expressed herein are subject to change without notice. Certain statements contained herein reflect the subjective views and opinions of Activant. Past performance is not indicative of future results. No representation is made that any investment will or is likely to achieve its objectives. All investments involve risk and may result in loss. This newsletter does not constitute an offer to sell or a solicitation of an offer to buy any security. Activant does not provide tax or legal advice and you are encouraged to seek the advice of a tax or legal professional regarding your individual circumstances.

This content may not under any circumstances be relied upon when making a decision to invest in any fund or investment, including those managed by Activant. Certain information contained in here has been obtained from third-party sources, including from portfolio companies of funds managed by Activant. While taken from sources believed to be reliable, Activant has not independently verified such information and makes no representations about the current or enduring accuracy of the information or its appropriateness for a given situation.

Activant does not solicit or make its services available to the public. The content provided herein may include information regarding past and/or present portfolio companies or investments managed by Activant, its affiliates and/or personnel. References to specific companies are for illustrative purposes only and do not necessarily reflect Activant investments. It should not be assumed that investments made in the future will have similar characteristics. Please see “full list of investments” at https://activantcapital.com/companies/ for a full list of investments. Any portfolio companies discussed herein should not be assumed to have been profitable. Certain information herein constitutes “forward-looking statements.” All forward-looking statements represent only the intent and belief of Activant as of the date such statements were made. None of Activant or any of its affiliates (i) assumes any responsibility for the accuracy and completeness of any forward-looking statements or (ii) undertakes any obligation to disseminate any updates or revisions to any forward-looking statement contained herein to reflect any change in their expectation with regard thereto or any change in events, conditions or circumstances on which any such statement is based. Due to various risks and uncertainties, actual events or results may differ materially from those reflected or contemplated in such forward-looking statements.

An informational guide for users exploring the space

Towards the AI Agent Ecosystem (Updated)

Usage-based billing (UBB) isn’t new, but it likely has never been more important. We are all used to paying for the products and services provided by utilities, mobile phone companies, doctors, dentists, and ride-hailing services like Uber based on what we consume, the service provided, or the distance traveled. In fact, UBB has been around since the beginning of commerce when a value was placed on a good or service and you “paid for what you used.”

Pricing is a powerful lever, and smart strategists shifted many pricing models to bundles or flat fees to maximize revenues and profits. This was particularly prevalent in the early days of software pricing and is still used by many companies today. Asana, Atlassian and Figma, for example, price almost exclusively on a subscription basis with rates tied to the number of user seats, company size, location and functionality included. Streaming companies like Netflix, Hulu and Spotify do the same with a single access fee and unlimited use of the platforms. SaaS companies, however, have begun to change the game and 

 reports that 3 out of 5 now have some form of 

 and this number is expected to increase to 80% or more in the next few years. The $660bn global software market is forecast to continue growing at a compound annual growth rate (CAGR) of at least 5.6% for the next five years, with the $228bn SaaS market growing even quicker at a CAGR of 

 We believe that this market development, along with the explosive growth of AI, will fundamentally change the way software and services are priced and that UBB will become an essential component of pricing plans. In fact, given that pricing is increasingly becoming a data problem and that customer experience is paramount, the platforms that win will likely be data- and customer-centric hybrid pricing solutions!

Many of us have been schooled in the notion that “price drops to the bottom line,” and this is true for most businesses. If products have positive unit economics and fixed costs can be leveraged, a large portion of price increases do indeed flow through to the bottom line. The reality of optimizing a business, particularly a growth business, is naturally a lot more difficult. Price impacts customer acquisition and, when considered with retention, the lifetime value (LTV) of an account. The ratio of LTV to the customer acquisition cost (CAC) is the math that your entire business is based upon, and investors obsess about it. Optimizing price is an imperative and a key lever for growth.

There is no shortage of pricing models to choose from. Consider cost-plus, value-based, competitor-based, location-based, bundle pricing, and dynamic pricing, just to name a few. For many online businesses and cloud-based software solutions, the default was a subscription pricing model where users were charged a recurring fee for continued access to the product or service. The environment, however, is changing and UBB models are becoming increasingly common.

The rise in UBB is being driven by a few powerful forces that are likely to increase as the cumulative impact of the changes they are fueling become self-reinforcing. In the short and medium term, we see no end to the positive impact on UBB of the following.

driving up the number of machines consuming services rather than their human operators. Software is interacting with software, so it is very tough to comprehend, monitor or control what is happening without tools to support visibility and rules to govern the interactions. In addition to this, it goes without saying that automation typically leads to productivity improvements and the replacement of human labor so shifting from seat-based subscription models to UBB makes a lot of sense.

Ubiquity of application programming interfaces (APIs)

 and the ability to seamlessly and effortlessly connect to sources of data and services to build into new products and services, often in real-time.

, which is compounding the impact of automation on jobs and creating whole new use cases for data and services at all levels in the stack, from infrastructure to middleware and front-end applications. As companies add AI features, often on top of OpenAI or other models, they need to fundamentally rethink pricing to ensure that margins are maintained, and that value can be extracted from “power-users” (or machines) scaling usage disproportionately.

and the role that it plays in AI, data security and Web3 

 Tokenization is used to chunk data to facilitate easier pattern detection in AI, to obfuscate identity and limit cyber threats as part of data security efforts, or to digitize assets to make them more accessible in Web3 blockchain or digital asset applications. In all cases, processing efficiency increases and handling the smaller data packages lends itself to being valued by usage and impact. It also makes the process of managing user usage data more complex and the need for clean, reliable billing data even more important

, with its focus on profitable growth rather than growth at any cost, aligns interests and reduces barriers to purchase if you get paid as your customers use your product. To facilitate the frictionless upsell that characterizes many PLG initiatives, UBB and clean data is essential.

The economic environment is likely also playing a role and in higher interest rate environments there is always a premium on limiting borrowing, reducing burn, preventing churn, and encouraging growth through net revenue retention and acquisition. Usage, not users, is simply a better way to price under these conditions.

Just as there are a myriad of different pricing models, so too is there a range of different usage-based options. Some of the better-known usage-based models include:

, where prices are set in increments with clear thresholds.

, where prices are adjusted based on total system demand and personal consumption.

, based on specific tools and features used.

There is seldom a perfect solution and one size doesn’t fit all. As always, the answer is “it depends”!

UBB has a lot of advantages but is not always a good thing and there are a few disadvantages that must be considered alongside the advantages.

. Users are only charged for what they consume and are never billed for services they don’t use. Customers don’t overpay for services they never access and can align costs with revenues as they grow their businesses. In this way, UBB 

. There should be no surprises (except for situations where you use more than you had anticipated) and, when coupled with self-monitoring tools, it is 

 can access the same account and billing is consolidated. For infrastructure providers, UBB allows them to 

charge for the demand put on platforms and systems

 by heavy users rather than having these customers subsidized by average and light users of the infrastructure.

Perhaps the most significant advantages, however, are those that can be quantified and support the growth of both your business and that of your customer. 

 has done some terrific research on the subject and reported 9% 

 for usage-based SaaS companies compared to the broader SaaS 

 claim that UBB reduces customer and logo churn because of the control it gives them, the alignment of objectives, and higher customer satisfaction. The estimates of reductions in churn rates vary widely (from ~10%-30%) and, while we could not independently verify any of the data, this aligns with the perspectives we gained from market experts.

 (TAMs) by making products more accessible and lowering barriers to trial and usage. Similarly, 

 of services encourages ongoing usage and expands TAMs. Many expect that net promoter scores (NPS) and customer usage will more closely align over time, allowing superior products and offers to rise to the top.

increased customer satisfaction and loyalty

 reported that 80% of customers surveyed for their 2022 Technology Report were satisfied with the better value they received from consumption 

 The same report stated that nearly half of software companies using UBB say it helped them acquire more customers, and two-thirds said it helped them increase revenues with existing customers. This is powerful data and supports the fact that most of the fastest growing SaaS companies employ 

There are naturally some disadvantages associated with UBB and it is not always the best option to pursue. It goes without saying that to use UBB one needs to be able to 

, which is not always straightforward. It sometimes takes 

time to find appropriate metrics to monitor and charge for

, or to build a baseline. Having accurate, reliable usage data is essential and this can be an obstacle or take a while to line up. We have heard from finance teams that older systems sometimes can’t identify and reconcile usage data accurately, leading to 

and the associated complaints and hassles to address

 Many of the newer UBB providers are using this “disadvantage” as the reason why teams need them!

We mentioned above that customers can sometimes lose track of their usage if there are no monitoring tools in place, which could lead to 

. For UBB service providers, the opposite can be an issue and 

 as customers can throttle back usage to meet budgets or handle cash crunches. It is also 

 if there are no long-term contracts or subscription agreements in place.

We believe that these disadvantages are relatively minor when compared to the significant benefits that UBB offers and, in most cases, they can be managed. One way to manage pricing plans is not to stick religiously with one model or another but instead to use a hybrid pricing model.

SaaS companies are increasingly turning to hybrid pricing models. Openview’s 2022 Financial & Operating SaaS Benchmarks Survey highlighted that 41% of SaaS companies surveyed either offer usage-based subscription plans or were testing UBB alongside traditional 

 Only 15% of companies largely used UBB, while 61% used some form of UBB. Like others, we believe that hybrid pricing models are the future of SaaS.

One of the reasons we believe that hybrid pricing models are the future of SaaS is the change in needs and usage along the customer journey. Customers often need incentives to try new products and vendors should do everything in their power to lower the barriers to trial and usage. At this stage it makes sense to offer UBB terms over per-seat subscription fees as the value typically accrues to new users. Once customers are comfortable with the product and see the value, it is easier to have the conversation around subscription contracts, which provide cost certainty for the users and revenue predictability for the vendors. These contracts are, on average, fair to both parties but the equation breaks down as the ratio of power users increases. This is particularly true if the vendor must absorb usage-based costs such as compute or third-party fees and royalties for embedded software or models. In these instances, UBB again makes more sense to recover some costs from power users (or machines) and protect margins. Hybrid pricing models address this challenge and align the interests of both parties along the customer journey.

Suitability of Pricing Along the Customer Journey

As pricing gets more complex, we are seeing the rise of the modern pricing tech stack and ecosystem of solutions.

The Rise of the Modern Pricing Systems Ecosystem

Rather than describing the various elements of the modern pricing tech stack — from data warehouses or lakes to the data cleaning and pre-processing tools required, data enrichment applications, analytics and modeling tools, enterprise platforms, execution engines, security solutions, and audit and control systems — we found it more helpful to think about the systems ecosystem that the specific pricing applications need to operate in. Most companies have the tech stack needed to implement UBB and other related pricing applications, but these solutions need to exist and operate within the broader context of the enterprise. Our Activant view on the modern pricing systems ecosystem, depicted in the diagram below, is somewhat stylistic but we think it includes all the critical elements.

Our View on Modern Pricing Systems Ecosystem

Large enterprise solutions — like ERP systems, CRM applications, and core finance systems — are the central nervous system of any organization and UBB and other related applications require bi-lateral integration to draw from and update these systems. Some organizations have “quote-to-cash” (QTC) solutions that they use, while others leverage pricing operations platforms to capture usage data, process it against pricing and contracts data, and send it out to billing and payments systems. While there are a handful of exceptional point solutions in the market, most solutions that we looked at have functionality and modules addressing several core tasks. Pricing engines, for example, are likely to include some form of configure-price-quote (CPQ) functionality along with subscription and UBB modules. Analytics and optimization functionality is often contained within larger enterprise, finance or pricing platforms but there are also a few very good stand-alone solutions that integrate seamlessly into the enterprise suite.

most successful modern pricing systems embrace the notion that pricing is fundamentally a data problem

. The drivers of the rise in UBB are all fundamental shifts and in each case, they imply a significant increase in the amount of data that needs to be processed. Data infrastructure and architecture plays a role, and the fidelity of data is key. Some players choose to aggregate data while others have decided to pre-aggregate. This naturally impacts some elements of performance and should be considered when choosing the solution and provider best suited to particular needs. The systems are becoming so complex that users can no longer intuitively understand where they are, and they need dashboards and tools to understand usage and the associated risks and benefits to the business. 

The platforms that will win are likely to be both data- and customer-centric

The point solutions that we like in the market are very good at the core task that they were designed for, and they typically integrate easily and seamlessly into other systems. Dunning providers like 

, for example, address failed subscription payments and integrate directly with a company’s payment processor and subscription management platform. They work with commercial tech stacks like 

, or your homegrown platform. Similarly, 

 offers excellent subscription analytics and integrates with many of the best-known payments platforms and providers, including 

A characteristic of the market over the past few years has been the acquisition of point solutions by larger platform players or those looking to build integrated suites. Stripe, for example, acquired UBB platform Octane. 

, a cloud-based finance platform that provides services to launch and manage subscription businesses, acquired 

, a no-code platform that simplifies and accelerates pricing and billing automation (including usage-based), was acquired by Ericsson. 

The integrated suites all partner with the leading ERP, CRM and other enterprise solutions to offer simple integration. 

, an enterprise billing and revenue management platform, highlights its partnerships with 

, amongst others. These integrated suites can replace the functionality of core enterprise system modules or complement them selectively as the large vendors all have modules addressing core pricing functionality, including UBB.

Range of Pricing Solutions

Collectively these solutions make up the pricing market landscape.

We think of the pricing market landscape as a series of layers that build upon one another as required. At the base there are the core enterprise systems that often have much of the pricing functionality required, and above this are the core finance systems that organizations may use. This will include (QTC systems, finance and planning tools, as well as pricing and billing applications. Beyond this, we consider core pricing applications — subscription management systems, UBB applications, and pricing automation solutions — and finally, the overlay services that add incremental value.

Pricing Market Landscape

Our core belief that UBB, while not new, likely has never been more important than it currently is naturally shaped our work. The logos presented on the exhibit above are representative and not an exhaustive mapping of all players. We have focused on the players specifically offering UBB functionality.

As alluded to above, the segments that we defined, and the players shown within each, are not “mutually exclusive and collectively exhaustive” (MECE). We attempted to place companies in segments aligned with their core functionality and what they are best known for, but we readily admit the limitations of our approach and categorization. 

, for example, is an enterprise billing platform that has excellent CPQ functionality that one could argue belongs in the QTC segment. A similar argument could be made that 

Not new, but likely never more important

Usage-Based Billing

Trusted advisor to founders. Passionate ecosystem builder. Recognized fintech expert.

Office

Hometown

Residence

Andrew
Steele

Partner

Office

Hometown

Residence

Role

How Andrew supports portfolio companies

Partnerships & BD intros

Product narrative and strategy

Industry perspectives - particularly in payments and fintech

Research

Relevant Experiences

Office

Hometown

Residence

Andrew Steele

Partner

Office

Hometown

Residence

Role

How Andrew supports portfolio companies

Partnerships & BD intros

Product narrative and strategy

Industry perspectives - particularly in payments and fintech

Research

Relevant Experiences

Andrew
Steele