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Tercera 30

Tercera introduces the 2025-2026 Tercera 30

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Today, Tercera officially launched its 2025-2026 Tercera 30 research, highlighting the 30 software ecosystems we believe hold significant potential for technology services firms.

We develop this annual research – now in its fourth year – by gathering and analyzing third-party data across more than 200 enterprise software companies. We combine this data with primary research that we collect in a number of different ways, including a survey of 300+ service providers and interviews with channel leaders, service leaders, bankers, analysts and enterprise buyers.

These quantitative and qualitative insights are then weighted based on what we believe creates a thriving environment for services firms. The end result is a short list of vendors that offer the greatest opportunities for services firms looking to build, scale or diversify their business, as well as insights into how these ecosystems are evolving in the AI era.

To access the full report, including vendor profiles and key themes from the research, visit our Tercera 30 page.

Changes This Year

This year, we replaced our “Market Challengers” category, which had focused solely on privately held companies, with a new one named “Market Builders”. This decision allows us to focus more on ecosystem maturity than ownership structure, and reflects the growing number of large companies that are choosing to remain private by choice.

Six companies fell off this year’s list, including one that has played an over-sized role in the global economy (Nvidia) and one that has been a long-time Anchor for thousands of partners (Adobe). These, and the other exits from last year’s list, were less about company performance, and more about where we see gravitational pull – namely in data, cybersecurity and AI.

This year’s exits were less about company performance, and more about where we see gravitational pull – namely in data, cybersecurity and AI.

We have five brand new vendors on our list this year (IBM, Palantir, Rubrik, Saviynt, ThoughtSpot), and one returning vendor that had dropped off last year (Crowdstrike). We also saw a number of players move between categories based on our change in methodology mentioned above. This led to our first new Anchor (Databricks) since the inaugural Tercera 30.

This year, we also supplemented our insights with interviews from a wider range of enterprise IT buyers and ISV channel leaders than we had in the past. To help with this and to surface deeper insights, we leveraged Bridgetown Research’s AI-native research platform, which uses voice-enabled AI agents to conduct qualitative interviews and apply analytics on structured and unstructured data. We also filtered our inputs into a 2×2 grid to visually graph vendors relative to each other based on growth rates and services potential.

Who is on the Tercera 30 for 2025-2026?

The report includes a mix of large vendors with more mature partner ecosystems (our Market Anchors), along with fast-growing platforms and specialized vendors that have more nascent partner ecosystems (our Market Movers and Builders).

Market Anchors:
Large software vendors with more mature partner ecosystems
AWS
Databricks
Google Cloud
Microsoft Azure
Oracle
Salesforce
SAP
ServiceNow
Snowflake
Workday

Market Movers
Large and mid-size software vendors with growth potential and evolving partner ecosystems
Atlassian
Dynatrace
Contentstack
Crowdstrike
IBM
Okta
OneStream
Palo Alto Networks
Shopify
Stripe

Market Builders
High-growth, high-potential software companies with more nascent partner ecosystems
Anthropic
Celonis
commercetools
MongoDB
OpenAI
Palantir
Rubrik
Saviynt
ThoughtSpot
Zscaler

Key Themes Highlighted in the Report

The research we conducted for the Tercera 30 and our recent AI playbook for services uncovered insights into how the enterprise software market is shifting and partner strategies alongside it. Below are six themes highlighted and analyzed in the Tercera 30 report.

1. The Great AI Washing
AI washing is rampant and every Tercera 30 vendor is racing to build agents. This is creating confusion but also opportunity for partners who can separate hype from reality.

2. Data is the New Cloud
Data platforms are the new power players in enterprise tech, with platforms like Databricks, Oracle and Snowflake poised to become the next hyperscalers.

3. Systems of Record Defend Their Turf
Enterprise giants are under siege from AI-native challengers. To stay entrenched, incumbents like Salesforce, SAP, Workday and IBM are fighting back by ramping up R&D and M&A.

4. Services and Software Collide
The wall between software and consulting is disappearing, with AI driving a new category of services-as-software. This convergence is causing a shift in partner playbooks.

5. Vertical Focus = Revenue Advantage
Vertically oriented services firms are winning more AI-related revenue, while vertical AI vendors present new partner opportunities for those who can navigate these tricky waters.

6. Expect Even More M&A
Tech M&A is heating up again, with more than 40 acquisitions across the Tercera 30. Dealmaking is redrawing the partner map, and services firms could be the next targets.

Download the full report and vendor profiles here.

Building the Tercera 30: Where AI Helped and Hindered

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At Tercera, we spend considerable time advising business leaders on how AI is impacting the way work is delivered. So we are always looking for ways to take our own medicine and responsibly weave AI into our own processes.

When it came time to develop this year’s annual Tercera 30 report, we knew this was the perfect opportunity to leverage AI in a purposeful way. Over the last four years, we’ve built up a significant amount of data on software vendor ecosystems. This, along with our deep connections with software channel partners, bankers, analysts, and service providers in these ecosystems, gives us a lot to work with. However, we are still a small team without a dedicated research arm. 

We started by looking at our existing processes and where we could leverage the foundational enterprise AI tools we already use (e.g., ChatGPT, Claude) and the AI capabilities embedded within our existing productivity tools (e.g., Google, Box, HubSpot, and Microsoft). These processes include: data collection, data validation, report development, and marketing and promotions.

From there, we looked at how we could use fit-for-purpose AI platforms and agents to do things that would’ve been difficult or impossible to do without giant budgets, or an army of analysts. This included interviewing or doing deep research on smaller, less established software vendors where we may not yet have direct relationships, or getting direct insights from senior enterprise IT leaders to understand how they’re buying, building or adopting AI platforms. 

The two tools we ended up leaning on the most were ChatGPT Deep Research and Bridgetown Research’s agentic research platform. 

ChatGPT: The Swiss Army Knife for Research 

ChatGPT proved most helpful at filling in basic data in our repository of company information. In batches, we were able to create tables including information such as website, company description, public/private, etc. formatted in a way we could easily import into our existing data set. Of course, there were more than one instances of hallucination or incorrect information, so it still required a fair amount of double-checking data against past data we had collected and other sources. This should require less training in the future based on this year’s usage. We found consistent value in ChatGPT’s Deep Research function to develop a base-level understanding of corners of the market that we were unsure about. 

Bridgetown Research: Agents to Go Wider, Deeper, Faster

We were first introduced to Bridgetown Research through one of our Advisors, and were immediately impressed by their vision to transform how primary and secondary data are gathered and analyzed, achieving scale, speed, and cost efficiencies that were previously difficult or nearly impossible. At its core, the Bridgetown platform uses autonomous AI agents to conduct expert interviews, collect data, and run custom analyses. It’s still early days, and we only scratched the surface of what Bridgetown’s platform can do, but we were impressed enough that we are considering incorporating it deeper into our AI-stack for value creation advisory work. 

Lessons Learned for Using AI in Research

When it comes to incorporating AI deeper into our processes, we learned first-hand just how important it is to experiment. Some of the tools and ways we leveraged AI in this process exceeded expectations. At times, it was more trouble than it was worth. 

It reinforced to us that AI can be extremely powerful as part of the research process. However, its impact is best felt when applied not generally, but purposefully. 

Here are 5 other lessons we learned along the way.

1. ChatGPT is a utility player, but not the best tool for everything

General-purpose GPTs are table stakes for the modern worker. Like Word or Excel, they are a base tool you should reach for by default to draft, summarize, run first-pass research, and structure messy information. In our process, ChatGPT sped up recurring tasks like populating company metadata into import-ready tables and spinning up quick briefs so we could engage experts faster. But just as a growing company eventually moves from tracking pipeline in a spreadsheet to implementing a CRM, you’ll eventually outgrow what a generalist GPT can do on its own. 

The real transformational change in our research process came from pairing a generalist GPT with a purpose-built tool.

Bridgetown Research’s platform expanded our reach by using agents to coordinate expert and buyer interviews and generating targeted insights we could not reliably get from a generalist model alone. Its “Bridget” assistant answered research questions more effectively because it was tuned to market-research workflows and grounded in the data we supplied. 

As scope and complexity increase, you need tools that are designed for the specific job.The model you choose matters: different GPTs handle retrieval, structure, and citation differently, and task–tool fit drives quality. 

2. There is still a role for traditional research tools

AI helped us move faster and cover more ground, but it did not replace the basics. We still leaned on the traditional research and analysis tools. For example, we commissioned a third-party research firm to survey 321 current services leaders to gauge how teams were actually implementing AI and thinking of partnership strategies. 

We also built a straightforward scoring model to evaluate ISVs on service potential and growth. The weightings and KPIs came from proven ways of consolidating and analyzing data, which kept the results anchored in reality rather than a black box. Then came the hands-on work that turns data into insight. We used Excel to clean, weight, and combine variables, and to turn the results into a clear 2×2 grid that made the tradeoffs easy to see. 

Just as important, we talked to people. Interviews and live conversations with services leaders, ISV channel leaders, and analysts gave us context, helped validate surprising findings, and turned the numbers into practical guidance. AI gave us speed and reach. Traditional research tools and real conversations made the output trustworthy and useful. 

3. Challenge the output at each step 

Reviewing any AI output is essential, but it’s just as important to understand the inputs and sources from which the AI tools are pulling. In more than a few instances, we found data was wrong because it was pulled from a source that was wrong and not reliable. A prime example of this was a higher-than-expected rate of misidentifying whether or not companies were publicly traded. Double-checking outputs like this is a given for anyone using AI to produce deliverables. And while this may seem like a no-brainer, Deloitte’s recent blunder shows how easy it is to skip this step in the name of efficiency and speed. 

Another problem we encountered was a feedback loop involving our own data from the report. As we began to query in more specific areas, multiple different AI research platforms began to feed us back data from our past Tercera 30 reports. While upon first glance this was incredibly self-validating, the danger is exactly that. We ran the risk of blindly validating ourselves with stale data and commentary without challenging our narrative based on other resources and data from the past year. Luckily, we were able to identify this and use our previous thought leadership as contextual input, while ensuring we incorporated third-party resources. GPTs and agents are designed to interact with data that already exists and consolidate. Even then, the outputs are not consistently reliable (although accuracy continues to increase). 

4. Agents are powerful, but they can’t replicate a human experience

While agents can do many things, the power of a human relationship remains one thing that they can’t replace. We found great value in using the Bridgetown platform agents to conduct interviews with key market stakeholders, but we also found tremendous value in calling upon our professional relationships to validate (and debate) the selections we made for this list. 

Not only do we know where these individuals stand and what might be influencing their opinions, but through these conversations, we garnered insights that likely would not be shared with an agent that people don’t have a personal relationship with.

Exposure to expert and on-the-ground opinions is not only crucial to our process but the secret sauce to the Tercera 30.

While anyone with enough time and resources can replicate the base process that we have built, it’s tough to replicate relationships and discernment.

In a similar vein, it is dubious to expect agents and GPTs to automatically reflect the decades of institutional knowledge and context that we bring to the analysis and scrutiny of the data we gather for the Tercera 30. Unlike relationships, this can be trained into models over time, but it requires the foresight and structure to do so. Even then, these models will need to be updated by the rapid changes and learnings that we gain over time. 

5. AI cannot run the whole project (yet)

AI gave us speed and reach, but it couldn’t (and we didn’t expect it to) fill all of the gaps across an end-to-end project like this. At this point, AI wasn’t able to design the right models for our goals, drive the sequencing of work across teams, or keep all the moving pieces on track. We still needed people to frame the problem, pick the methods, set quality thresholds, and decide when to pivot. Beyond basic, repetitive tasks, AI still isn’t reliable at carrying that context through the full lifecycle.

The creative side is another limit. While AI can draft copy, suggest layouts, and generate visuals, the final deliverables still required human taste and judgment to tune the voice, ideate and contextualize data visuals, and make choices that fit our brand and audience. The tools are getting better every day, and we will keep experimenting, but they are not ready to own creative direction or produce truly differentiated deliverables by themselves. AI is a strong contributor. It is not the project owner.

The Future

AI tools are moving fast. What was unreliable this cycle could be ready by our next round of research. We will keep testing and adopting the best options for our work, and we will fold standout platforms into the ever-growing list of companies we consider for the Tercera 30.

Commerce in chaos: The shift from architecture to answers

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Why the winners in commerce services won’t sell architecture — they’ll sell clarity.

Earlier this month, the Tercera team attended ShopTalk to get a pulse on what’s happening in retail and commerce technology. Across dozens of conversations with service providers, software vendors and investment bankers one theme stood out loud and clear: everyone is chasing simplicity and quick ROI.

The promise of ease-of-use and fast results have always been appealing to tech buyers. But in today’s commerce space, it’s non-negotiable. A mix of tariffs and AI mania have created a level of uncertainty and volatility that vendors and customers are scrambling to navigate, and during times of chaos, buyers look for certainty and stability. They hoard budgets, protect teams and slow roll major modernization efforts until they are absolutely necessary.

It’s why partners and vendors are seeing large programs like commerce, order and warehouse management replatforms slow to a trickle, while narrower initiatives like content management, AI search and product discovery get prioritized. They’re simpler, faster to deploy, and easier to prove ROI.

The Lure of Shopify

Every few years, a new platform rises on the promise of making commerce easy: ATG, Hybris, Magento, Demandware. Today, it’s Shopify’s moment in the sun.

The company’s trajectory over the last few years has been impressive to watch. In the last five years, the total merchandise value (GMV) sold through Shopify has grown nearly 380%! In 2022, Shopify’s stock may have been on a downward spiral, but by 2025 they were a part of nearly every conversation we had at Shoptalk.

We spoke with many Salesforce and Adobe partners diversifying into Shopify. With the thousands of partners already in the Shopify ecosystem, this isn’t an easy one to break into, but word on the street is that Shopify is now in enough buyer conversations to make it worth the investment.

Shopify is winning in today’s market because it hides the complexity. It’s fast to deploy, intuitive, and feels safe. In a down market, that’s priceless. Not just for the small and medium-size businesses where Shopify has traditionally dominated, but also for enterprise buyers.

While enterprise customers are still a small portion of Shopify’s customer base, the company has been making slow but steady inroads into larger and larger customers with its Shopify Plus and headless offerings that decouple the front-end presentation layer with the back-end infrastructure.

Composable returns not as hype, but as a necessity

Meanwhile, composable vendors are finding it hard to compete with Shopify’s lure of simplicity and fast ROI.

Two years ago, composable commerce was at the peak of the hype cycle, selling on the promise of a future proof architecture and infinite customization. Today some of the luster of composable has worn away. A handful of failed implementations — often led by underqualified integrators or DIY efforts — have left some buyers wary.

But while the buzz may have faded— we’d argue composable is moving into the slope of enlightenment, where real value starts to emerge. Those customers who have made the move, sing the praises of composable, and many of the principles behind MACH (microservices, API-first, cloud-native, headless) have become the norm for how modern web applications are developed.

While buyers crave simplicity, enterprise complexity and the underlying need for flexibility and optionality still exists. Especially with enterprise buyers who live in a world of complexity — regulatory hurdles, global operations and bespoke workflows — and for those that want to take full advantage of the AI-native applications coming to market.

Monolithic platforms might promise to serve these needs, but composable architectures are often better equipped to deliver. It’s why composable, even without the buzz, is quietly becoming the enterprise default. It’s also why so many of the large platform providers, even the monoliths like Salesforce and Shopify, have started offering headless / composable options.

If you want to service B2B customers, regulated industries, or support advanced enterprise workflows, composability is the key.

The opportunity for commerce service providers

Here’s the bottom line: simple always wins – until it doesn’t.

Shopify will continue to shine in certain user cases and we believe has a very bright future ahead. But commerce is complex. It’s messy. Simple solutions eventually hit a limit, and when it does, the cycle is predictable: platforms start lean, gain adoption, add features to address edge cases — and eventually, complexity creeps back in.

It’s why composable is so powerful as a concept. Every architecture gets messy at a certain scale.

This is where services firms can shine — not just as builders of tech stacks, but as trusted brokers who can navigate the noise, deliver clarity and lead with results. It’s why the best service partners today lead with:

  • Proven case studies that illustrate results and what to expect
  • Opinionated stacks with pre-vetted technology combinations
  • Trusted ISV ecosystems that the partner can easily navigate
  • Pre-built integration and accelerators to speed results

What buyers really want isn’t a perfect stack. They want a recommendation, not a decision tree. They want progress, not paralysis. The faster the better.

Whether monolith or composable under the hood, the services partner that makes everything feel simple — that hides the seams and reduces the noise — is the one that earns trust and repeat business

It’s the partner that brings clarity in a complicated world that will win the long game.

5 takeaways from Nvidia GTC for IT services firms

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The next chapter of AI will be written in systems, not silicon

That was a clear message from Nvidia’s GTC conference last week, the formerly academic conference that has now become a mix of technical sessions and world-class trade show. As one of the newer entrants to our Tercera 30, the Tercera team attended the conference to better understand how Nvidia and others are pushing the boundaries of what’s possible with AI, robotics and accelerated computing. Beyond sessions, we also met with analysts, channel and services leaders to gain insights into the partner ecosystem and the implications for IT services firms looking to navigate the evolving landscape of enterprise AI adoption.

Here are five high-level takeaways that stood out to us — and why they matter for service providers of all sizes:

1. AI Isn’t Just a Tool — It’s a Flywheel for Your Business

One of the most compelling themes across multiple sessions was the concept of “agent-powered flywheels” within organizations. These aren’t just AI tools for productivity—they’re autonomous or semi-autonomous agents that learn, act and improve processes over time. When implemented effectively, they can kickstart a compounding cycle of efficiency, insight and innovation.

For IT services firms, this means rethinking how you structure internal operations and customer-facing processes alongside agents. Could you automate onboarding, and let an AI agent handle repetitive security checks or internal documentation updates? Could you leverage agents to smooth out and accelerate the sales to delivery handoff, or identify areas for future work? The internal use of AI isn’t just cost-saving—it becomes a demonstration of what you can do for your clients.

2. From Generative AI to Agentic AI — and Beyond

The next wave of AI is already here. We’re quickly moving from passive generative models to active, agentic AI — systems that can autonomously take action based on context and goals. Nvidia emphasized this shift as a foundational change, with implications ranging from enterprise automation to real-world robotics and digital twins.

For IT services firms, this represents a major expansion of scope. Projects will no longer just focus on integrating LLMs but designing workflows where AI agents can make decisions, interact with environments (both digital and physical), and continuously improve outcomes.

3. AI Adoption is Inevitable — Control the Chaos Before It Controls You

Another major takeaway: your employees are already using AI, whether sanctioned or not. Shadow AI—tools like ChatGPT, Gemini, or Copilot used without IT oversight—is everywhere. Rather than resist, the recommendation from Nvidia’s Chief Security Officer, David Reber, was to lean in and establish centralized AI governance frameworks.

This is a golden opportunity for services firms to help customers architect secure, scalable and compliant AI strategies. Internally, invest in your own AI policy and infrastructure, then use it as a blueprint for client engagements.

4. The Big Opportunities Aren’t Just for the Big Players

While the Global Systems Integrators are clearly well-positioned to lead in this space, Nvidia’s ecosystem still leaves room for nimble, specialized players — especially those who bring deep vertical or domain-specific expertise.

However, the ecosystem around Nvidia and other major AI players is still relatively closed and extremely difficult to break into, especially if you don’t bring a brand or customer weight to the table. Smaller firms can still play here, but those looking to break in here will need to be proactive, build their own pipeline and give it time. Embedding AI into your core offerings — rather than spinning up standalone AI practices — will be key. Co-innovation, proof-of-concept development, and managed AI services are all emerging as viable paths forward.

5. Nvidia is Evolving—And So Should You

Perhaps the most striking signal at GTC was Nvidia’s own transformation. While GPUs are still foundational, the company is clearly leaning into software, robotics, and full-stack AI platforms like Nvidia Omniverse and CUDA-X. Part of this is due to supply chain constraints (they can only make so many chips), but it’s also a recognition that the next chapter of AI will be written in systems, not silicon.

It will be interesting to see how the tight partnerships between Nvidia and the Cloud Service Providers (CSPs) evolve over the next few years, as Nvidia moves up the stack and the CSPs look to develop their own chips.

The good news is that Nvidia understands that their evolution and the future of AI will require a robust ecosystem of software partners, systems integrators, and service providers. They were vocal that they can’t do this alone. For IT services firms, this is an open invitation to innovate, collaborate, and co-build with one of the world’s most forward-thinking tech companies.

Final Thoughts

AI is no longer a future consideration—it’s a present-day imperative. At GTC, it was clear that Nvidia is not just building the infrastructure of AI, but also rallying an ecosystem to shape its application across industries. For IT services companies that can bring real expertise and differentiated offerings to the table, it could be a good time to step into that ecosystem but don’t expect it to be easy. However, the opportunity s enormous.

Anaplan vs. OneStream: Duking It Out in CPM

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Coke vs. Pepsi. Nike vs. Adidas. Tesla vs. Edison. Packers vs. Bears. There’s nothing like a rivalry to focus attention and spark emotions.

Last year we wrote about the rivalry between Databricks and Snowflake in the data wars. You, dear readers, loved it, making it one of our top posts of the year. As an encore performance, we are tackling another tech battle brewing – the matchup between Anaplan and OneStream in corporate performance management (CPM).

Tercera has been tracking the Anaplan and OneStream ecosystems as part of Tercera 30 research for the last three years. Only one side has made the cut. Was it the feisty upstart (OneStream), or the somewhat more established player (Anaplan)? In this post, we take a closer look, deciphering key advantages and drawbacks of both solutions. We discuss the ecosystems for IT services firms considering partnering with one of these firms, and make a case for the one we’re confident will prevail.

What are Corporate Performance Management (CPM) Solutions?

First, some background. Corporate performance management (CPM) solutions (also known as enterprise performance management or EPM solutions) help organizations plan, budget, analyze and optimize their financial and operational performance. They sit on top of systems of record, such as ERPs, HRISs or CRMs, with data flowing in from those and other sources. Their functionality is twofold: consolidating and reporting historical financials, and supporting planning and forecasting.

The right CPM platform is like Mission Control for finance. It provides a single source of the truth so CFOs don’t lose sleep worrying about the integrity of their data. In its ideal state, a CPM should ensure a company won’t have conflicting numbers in different systems for the same data point—an all-too-frequent occurrence that drives CFOs bonkers, especially now that their responsibilities include other tasks such as allocating resources and optimizing supply chains. A holistic CPM that solves the full range of a company’s data needs can provide much needed organizational efficiencies and support better decision making.

Both Anaplan and OneStream play in this market segment, and are used by the Office of the CFO. However, they aren’t exactly the same.

OneStream vs. Anaplan: How are They Different?

The main differences between the two solutions is in the range of needs that they address, their ability to ensure a single source of the truth, and their scalability/extensibility.

Founded in 2012, OneStream supports the full spectrum of CFO data needs. It is a single, extensible platform that seamlessly integrates financial close, budgeting, forecasting, and scenario planning. As an enterprise platform, it has more power and can handle very large scale organizations.

OneStream excels at providing a true single source of the truth. It features a unified data model, where all departments work off the same numbers, preventing discrepancies. Its single-security model and reporting engine ensure real-time data consistency, and it has integrated financial data quality controls, where data is validated at ingestion to reduce errors and ensure accuracy.

OneStream is a versatile, powerful tool, a reality that carries its own burden. As one Tercera adviser put it, “OneStream’s biggest advantage is that it can do anything; Its biggest challenge is that it can do anything.” With this power comes responsibility. You have to architect the system correctly from the start, which is why guidance from an IT services partner is so important. The fact that OneStream is laser-focused on customer success helps.

In contrast, Anaplan, founded in 2006, today is more of a point solution. It is flexible, and fulfills a department’s forecasting and planning needs with relative ease. Smaller and lower-end commercial companies may opt for Anaplan because it can be deployed more quickly. However, it isn’t as effective at handling the broad range of a CFO’s requirements, such as reporting, consolidation and compliance. Customers will likely need to bring on and stitch together other tools to modernize the finance function, resulting in a spider web of applications rather than an integrated solution.

Below are two recent Gartner Magic Quadrants that highlight Anaplan as a leader in financial planning, but its absence in financial close and consolidation.

OneStream in Gartner Magic Quadrants

Anaplan’s decentralized model-based architecture also does not ensure a single source of the truth. Anaplan allows different departments to create independent models. While some customers may initially like this flexibility, it can lead to data fragmentation and inefficiencies. Teams can have their own assumptions, drivers, and calculations, causing inconsistencies. Users must check data accuracy and integrity using manual reviews, external databases or third-party validation tools. This decentralized approach, and the lack of built-in financial data quality controls, can also pose scaling challenges for large enterprises. More models intensify reconciliation demands, increasing the risk of data inconsistency.

Even though Anaplan is a forecasting tool, OneStream offers more robust forecasting for a couple of reasons. First, it is built around financial consolidation, so forecasts are based on accurate historical financials—that essential single source of the truth. Second, OneStream has a sophisticated logarithmic AI model, with external factors and economic indicators built in. Logarithmic AI is a powerful type of AI that improves forecast accuracy. Other platforms that incorporate generative AI are not as powerful. With logarithmic AI, you can input fewer data points and dirtier data and still have a good predictive model. OneStream’s AI can be intimidating because it’s so powerful, but we believe it’s the future.

Finally, there are key technical differentiators. OneStream’s founders had extensive experience at Oracle and elsewhere, and they created the platform to solve difficult problems that companies faced with earlier CPMs. For instance, OneStream features “extensible dimensionality,” which means that it can easily add new categories of data without breaking the database’s structure. It also offers “relational blend,” a data management approach enabling the integration of financial and operational data from relational databases. This allows users to bring together structured relational data (like transaction records, detailed operations metrics, or ERP system data) with financial planning and reporting in a unified platform. This lets OneStream handle a volume of data that is difficult for other solutions.

How are the Two Solutions Evolving?

Both platforms are innovating to broaden their markets. OneStream is investing in simplifying its platform to increase adoption among mid-market customers. In 2024, it launched CPM Express, a solution packaging “decades of experience and best-practices” and reducing implementation time to 6-8 weeks. To simplify customer onboarding and boost adoption, in early 2025 it rolled out solution-based pricing. Separately, to reinforce its position in highly regulated industries, it has achieved FedRAMP High authorization, a top-level federal security certification.

Meanwhile, Anaplan is moving upmarket, transforming what was traditionally a planning tool into a more unified platform, with native tools that can help address more complex use cases. It acquired Fluence Technologies in 2024 to add financial consolidation capabilities. It is focusing on real-time, user-friendly modeling, to support enterprise-wide decision-making, positioning itself as a flexible alternative to traditional CPM solutions.

Both platforms are also extending the machine learning (ML) capabilities that they’ve built over the last few years. OneStream has introduced Sensible ML, a machine-learning-driven solution designed to enhance financial planning, forecasting, and decision-making. Meanwhile, Anaplan is investing heavily in AI-driven scenario planning to match OneStream’s advancements in predictive modeling. While mainstream adoption of these AI capabilities may be some years away, it’s clear that embedding them in these platforms is becoming a higher priority.

How are the Two Ecosystems Oriented?

OneStream’s ecosystem is tiered based on experience, into Diamond, Gold, Platinum, and Silver tiers. OneStream also offers a GSI tier, and a Development designation for partners building solutions in the OneStream Solution Exchange. The firm is committed to 100% customer success, so its partner model is highly-controlled and high-touch. As of December 2024, OneStream had more than 300 partners, annual recurring revenue of $568 million, 35% year-over-year subscription growth, and 1,600 customers with a 98% retention rate. You can learn more about the OneStream partner program here.

Anaplan is stratified by industry (Consumer, Manufacturing, Financial & Business Services, Technology, and Media & Telecom) and function (Finance, Supply Chain, HR & Workforce, and Sales & Marketing). Anaplan has a more flexible, open partner ecosystem, and offers opportunities to generate resale revenue. Anaplan, which was taken private in 2022 by software investment firm Thoma Bravo, announced in February 2025 that in the previous two and a half years it had grown annual recurring revenue from approximately $600 million to over $1 billion, added nearly 300 new logo customers, and expanded the number of customers with annual subscriptions greater than $1 million to over 200. In 2022, nearly 200 partners were listed on the company’s website. However, a recent analysis shows closer to 75 pure consulting partners.

Who Will Win?

Both platforms have the potential to continue gaining market traction, but OneStream has the pole position, especially when it comes to its services partners.

As a newer and broader platform, OneStream has a bigger role to play across the office of the CFO, especially within large enterprises. It is far more customizable for the inevitable evolution of use cases. We believe the company’s expansion into function-specific planning, AI-driven forecasting, and its strong financial data control framework make it the preferred solution for companies needing a true single source of truth. Additionally, OneStream’s recent IPO means the company is well capitalized to invest in growing its share.

Anaplan, while strong in flexibility and multi-scenario planning, has more limited upside for partners in our opinion. The data fragmentation inherent in its model makes enterprise-wide financial accuracy challenging, which will limit the company’s ability to move up-market. We also believe that it’s easier to expand downmarket rather than upmarket, giving OneStream an edge. While Anaplan’s business performance has done well under Thoma Bravo’s guidance, the company has been flat to down in both employee growth and number of ecosystem partners.

As many CFOs look to consolidate their tech stack to streamline operations, reduce cost and improve data quality, we believe OneStream is positioned to win. It’s why in 2022 Tercera invested in Black Diamond Advisory, a Diamond partner for OneStream that was recently named Partner of the Year for OneStream. So, to be fair, we might be a little biased here.

commercetools

by

Last updated: January 2025

About the Company:

commercetools is a global leader and a go-to player in the growing composable commerce segment. As the company that originated the term “headless”, it has shaped the movement to a more modern, modular architecture that decouples technology components within a broader commerce system so businesses can adapt quickly to market changes and shifting customer demands.

Founded in 2006 in Munich, Germany, commercetools now has more than 600 employees across 12 international offices. While its roots are in traditional retail segments, commercetools has seen growing traction in healthcare, automotive, telecom and other B2B segments that have complex business relationships and procurement processes.

Company Stats:

CEO:

Andrew Burton

Market Segment:

Commerce

Number of employees:

600+

HOW TO BECOME A PARTNER:

commercetools Partner Program

About the Partner Ecosystem:

commercetools is a partner-first business that relies on software and service partners to fully deliver on the promise of composability and a modern user experience. The company’s culture recognizes the critical role partners play in helping each customer create highly customized, contextualized commerce experiences that serve as extensions of their brands.

The number of partners in the commercetools ecosystem has grown significantly year over year, and its ecosystem remains in growth mode as the company deepens relationships with existing partners, expands its GSI relationships, and onboards new specialist partners. commercetools keeps the bar high for all its partners knowing that the shift to MACH (Microservices, API-first, Cloud-native, and Headless) systems requires exceptional customer support.

PARTNER PROGRAM HIGHLIGHTS:

  • Global partner-first business model, with solutions that are fairly services intensive 
  • Little to no competition from in-house professional services 
  • Shared focus on customer outcomes and empowering individual partners to demonstrate a unique value proposition in crafting world-class commerce experiences
  • A tiered system for service partners that rewards high engagement and CSAT
  • Formal partner steering committee offering cross-functional engagement opportunities
  • Market development funds available for co-marketing and campaign opportunities
  • Client success organization offering design, code and go-live readiness reviews

“Helping customers get to the ideal commerce experience requires a set of people who understand and have different perspectives they can bring to that problem. The world’s leading brands need flexible solutions to meet their specific needs and to meet them without boundaries.”

– Andrew Burton, CEO

Keys to Service Partner Success:

Partner success in the commercetools ecosystem starts with having an intimate understanding of a customer’s specific challenges, and a point of view that commerce is not an area for cookie cutter solutions. Because customer solutions often include multiple components, partners must be able to collaborate alongside other software and service partners to deliver commerce experiences within the context of each brand.

For partners to succeed in this environment, having deep expertise in commercetools is table stakes. Those looking to truly stand out must be able to serve as advisors across all the different customer touchpoints – to help inspire and define what great looks like in commerce and to point customers toward the solutions that will bring those exceptional commerce experiences to life.

“If you view technology as an enabler to your business outcome, then you need that technology engine to be highly performant, highly scalable, highly flexible. Because if that slows down, if your engine is slow, your business is slow.”

– Andrew Burton, CEO

Near-term Opportunities for Service Partners: 

commercetools is looking for consulting partners that are willing to collaborate and innovate alongside the company and its customers to reimagine best practices and deliver world-class commerce experiences across the customer journey.

It is looking for partners that can augment and extend a customer’s internal expertise both vertically or horizontally in key areas related to commerce. Those that are business advisors and thought partners on how commerce and the commerce stack is evolving, as well as technologists that can implement highly performant, agile systems.

commercetool’s Approach to AI: 

commercetools is leveraging AI within its platform to allow businesses to deliver hyper-personalized shopping experiences through real-time recommendations, dynamic pricing, and predictive analytics. Its API-first, headless architecture offers the flexibility for customers to incorporate their own AI models or third-party AI solutions.

“commercetools is the commerce platform for the AI-enabled world.”

– Andrew Burton, CEO

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