DoiT just acquired its fifth company in 18 months, launched a new AI solution and became one of the first parters to earn AWS’ new Business Value Realization Competency. Here’s what you need to know about DoiT’s massive AI push and why it’s working.
DoiT has acquired five companies over the past 18 months as the channel star dives deep into AI cost management innovation and drives billions in AWS sales as it achieves AWS’ new Business Value Realization (BVR) Competency.
The Santa Clara, Calif.-based company just acquired AI cost management startup Attribute, has launched a new AI solution, and became one of the first parters to achieve AWS new and important BVR Competency.
“We’re applying the same approach that made us a leader in cloud cost management to AI tokenomics, and establishing ourselves as an innovative player in what we call automated AI cost attribution—where we can tie every token of spend to the work and the outcome it produced,” said Amit Kinha, DoiT’s field chief technology officer.
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“This makes us a more complete partner to the large enterprise and upper mid-market companies we already serve, and it gives prospects a reason to switch that the rest of the market can’t match,” Kinha said.
The AWS BVR Competency marks DoiT’s twelfth AWS specialization.
In 2023, DoiT signed a five-year strategic collaboration agreement (SCA) with AWS to drive $5 billion in business.
“Our BVR Competency sits on top of a five-year SCA to drive $5 billion in business, our AWS Managed Services Provider designation, and more than 500 AWS certifications across our cloud architects,” Kinha said. “So, this isn’t a one-off badge.”
In an interview with CRN, DoiT’s Kinha talks about acquiring startups, the importance of AWS’ new BVR Competency, the new Attribute solution for AI and DoiT’s tokenomics future.
What market shift occurred to make you buy a startup like Attribute?
What’s happening here is larger than one acquisition or product enhancement.
The unit of spend has moved all the way down to the token, and that breaks the assumptions cloud cost management was built on.
We’re building the measurement layer for that new world, which is why we like the concept of “tokenomics,” which is all about understanding what each token is actually worth to the business, who consumed it, what it produced, and whether the outcome justified the spend.
Why is tokenomics so important in today’s AI era?
Tokenomics is where the industry is heading.
The Linux Foundation and the FinOps Foundation announced their intent to form the Tokenomics Foundation, which will set open standards for AI token economics.
We think that’s exactly the right frame and we’re aligning DoiT squarely with that mission, more closely than anyone else in this space.
Our automated AI cost attribution is the measurement layer that makes tokenomics operational, and we intend to be the company most closely identified with it.
Click through to read Kinha’s thoughts on acquisitions, AWS and Attribute.
DoiT has bought several startups over the past 18 months including PerfectScale and LiveDiagrams. Talk about the need to acquire a startup like Attribute for AI cost management?
AI doesn’t operate like traditional cloud products when it comes to how cost is allocated, spent or overseen. Every other vendor looked at this problem and came up with the same answer: connect even more data sources, build bigger and more streamlined dashboards, and generally make AI spend a complementary piece of information to the cloud costs they’ve always helped customers manage.
We determined that while those kinds of changes would certainly be helpful, they don’t really solve the problem.
AI spend is more volatile, more decentralized, less responsive to good governance, and costs can explode in real time, especially when agents and sub-agents get involved and take independent action with shared GPUs, shared model accounts, and LLM gateways that older tools were never designed to see into.
We needed to help customers tell the difference between human-triggered spend and agent/sub-agent spend, attribute those workflows and costs to the proper individual and project that triggered them, and give both finance and practitioners teams that information in real time.
We weren’t content to just help them better understand their AI bills after the fact, when the damage was done.
Our own research into enterprise AI spend found that nearly 80 percent of enterprises had AI cost overruns in the past year, and the organizations with the most sophisticated governance actually had the highest overrun rates and the highest mean overspend.
Ultimately, we decided this problem requires a distinct approach and technology architecture built for the realities of AI, not just an iterative extension of our cloud cost platform.
We identified the startup Attribute that had built exactly the capability we needed to achieve our goals and moved quickly to acquire it and build it into our platform.
DoiT is one of the first partners to achieve the new AWS Business Value Realization Competency. Talk about the importance of the BVR Competency and your thoughts on AWS’ BVR partner strategy in general?
Being named one of the inaugural launch partners for the AWS Business Value Realization Competency matters because it recognizes the thing we care about most: whether a customer’s investment actually produces a result they can measure.
The competency identifies partners who go past technical delivery into proving outcomes, setting success metrics with the customer up front, tracking against them, and showing evidence at each stage.
As more budget shifts into generative AI the question of, “Did this AI pay off?” has become a priority question for every enterprise board, and AWS built the designation to help customers find partners who can answer it.
DoiT was part of the AWS Partner-Led Customer Success pilot that laid the groundwork for the program, working alongside the AWS team to test and refine the framework before it went public.
Our delivery model moves a customer from a stated business case to a production-grade AWS solution—usually in four to eight weeks—and every engagement is handed off with infrastructure-as-code, runbooks, and enablement so the team can run it on their own.
The results are immediate and impressive: 40 percent of code written by Amazon Q Developer at Promptly; and 70 percent faster transaction processing at Reimagination Technologies on a platform serving more than 80 million users.
Our BVR Competency sits on top of a five-year Strategic Collaboration Agreement to drive $5 billion in business, our AWS Managed Services Provider designation, and more than 500 AWS certifications across our cloud architects.
So, this isn’t a one-off badge. It’s another layer on a relationship that keeps deepening.
DoiT already integrated Attribute technology into your portfolio and you’ve just launched a new AI cost management solution—called the same name—Attribute. What does your new Attribute solution provide to customers?
Rather than aggregating what the AI providers report after the fact, our platform uses a lightweight kernel-level sensor to observe exactly what runs, in real time, at the kernel level, with no instrumentation, code changes, SDKs, or tagging.
Every token, model call, and GPU cycle traces back to the workload, the feature, the agent, and the individual that triggered it.
For the first time, real-time AI spend attribution gets the right information to the right people fast enough to do something about it before the next bill arrives.
Ultimately, this is a deliberate, strategic shift for DoiT.
We’re applying the same approach that made us a leader in cloud cost management to AI tokenomics, and establishing ourselves as an innovative player in what we call automated AI cost attribution, where we can tie every token of spend to the work and the outcome it produced.
What new clients, capabilities or markets do you hope to gain from Attribute?
Two things change fairly substantially with this development: the AI cost capability itself, and the way our technology is leveraged within our customer organizations.
In terms of platform capability, we are now the only vendor who can provide real-time, kernel-level, cloud-shared cost attribution that doesn’t depend on anyone reporting anything.
We are the only one that can observe AI cost from the source that triggered it, be it a person or agent. With this, our customers are able to map spend to outcomes with confidence.
This naturally shifts AI cost from exclusively a finance question to primarily a practitioner one. Considering that most AI spend originates in engineering, it’s natural that engineers are the people best placed to look at attributed spend and judge whether it was worth it, because they understand what the workload was actually doing within the broader business context. Finance can see a spend number, but they can’t contextualize the work behind it inside a spreadsheet.
The platform now becomes an enablement tool for practitioners who can finally connect spend to the project outcomes it produced, making it far more valuable for evaluating the real ROI of AI.
With Attribute, you can attribute every single token to the customer, feature, product or team that drove it, with zero instrumentation.
This makes us a more complete partner to the large enterprise and upper mid-market companies we already serve, and it gives prospects a reason to switch that the rest of the market can’t match.
