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But it could soon turn deflationary. If every vendor includes 100 AI Agents for the same price as before. That do 10x as much. The post One Thing is Clear: AI Makes a Lot of Business Software Look Awfully Expensive Today. Is Deflation Coming? appeared first on SaaStr.
commercial revenue surged 71% as ArtificialIntelligence Platform (AIP) gains enterprise traction Deep government/defense roots benefit from heightened geopolitical tensions worldwide Clear AI differentiation has made it indispensable for data-driven decision making How AI Led Palantir From Slow Growth (13%) to Hypergrowth (49%!)
Every major tech company now depends on NVIDIA: OpenAI training GPT models Google powering Bard and Search AI Microsoft Azure AI services Meta’s LLaMA development Tesla’s self-driving technology The B2B Parallel : AWS becoming the infrastructure layer for the internet, or Salesforce becoming the system of record for sales teams globally.
What This Means for the Market For Competitors : Snowflake, AWS, Microsoft, and Google are all scrambling to respond. Platform Effects Are Stronger Than Ever When customers use 6+ Databricks products, they’re not just buying software—they’re adopting an entire data architecture. The switching costs become astronomical.
For example, machinelearningmodels can forecast sales, optimize pricing, and evaluate investment scenarios in real time. Key benefits of AI-driven decision support include: Predictive Insights: Machinelearning forecasts customer demand and market shifts by analyzing historical and real-time data.
Pricing: Keep It Simple (At First) Databricks started with a simple, consumption-based pricing model. Because thats how their customerswho were used to AWS, Azure, and GCP pricingexpected to buy. Enterprise sales require a field presence, strategic account management, and a drive to go where your customers are.
FastSpring continuously monitors transaction flow via machinelearningmodels that are under the oversight of an enterprise-grade team of infrastructure and payments experts. Moreover, we have an enterprise-grade engineering team that has built and maintains a scalable and resilient cloud-native stack using AWS.
This fuels a robust ecosystem of AI chips (Nvidia, AMD), cloud AI services (AWS Sagemaker, Azure AI, Google Cloud AI), and SaaS integration (Salesforce Einstein, Microsoft 365 Copilot, Adobe Firefly). AWS Trainium2 & Ultracluster supercomputer details unveiled; embeds 64 Trainium chips, used by Anthropic + Apple trials.
These seem like perfect fits for LLM based applicatiosn. Perfect for a LLM! They each have some of the largest cloud businesses in the world in AWS, Azure and Google Cloud respectively. There are so many of these workflows out there today, and many of them are quite manual. What do all of these have in common?
The Infrastructure Math Is Unprecedented The Capital Intensity Is Off The Charts: Big Six tech CapEx: $212B annually (63% YoY growth) Microsoft AI business: $13B run-rate (175% YoY growth) NVIDIA data center revenue: $39B quarterly (78% YoY growth) Amazon AWS CapEx as % of revenue: 49% (vs. 80% cheaper than predecessor, costs 0.2%
4 Learnings on Snowflake’s Future Direction The CFO Test is Coming: As Snowflake bills become the “second largest after AWS” for many enterprises, the pressure to prove ROI intensifies dramatically. Success is measured not just by data storage and processing, but by demonstrable business value creation from that data.
It’s the same psychological principle that made AWS dominant. Customers trusted AWS partly because they knew she could leave if needed. When they can easily export their data, they’re more confident investing deeply in your platform. That trust led to deeper, stickier relationships.
2025 in LLMs so far Speaker: Simon Willison ( SimonWillison.net ) – Session video I’m a huge fan of Simon’s, and here he’s in fine form—fine enough that this presentation won best-of-conference.
With a background that includes leadership roles at AWS, Microsoft, and Lenovo, Fred brings a wealth of experience in building high-performing teams and driving revenue growth. Startup to watch DocUnlock – launched publicly with incredible traction, over 100% NRR through zero churn and multiple upgrades and expansions.
LLMs Transform the Stack : Largelanguagemodels transform data in many ways. If you’re curious about the evolution of the LLM stack or the requirements to build a product with LLMs, please see Theory’s series on the topic here called From Model to Machine.
Within the next 12 months, Adam Seligman, VP of Generative Builders at AWS, believes there will be an inversion of SaaS. Adam came up with the wildest idea he could think of for an app and used Anthropc, a largelanguagemodel company, to help develop the idea. What does that mean?
Then we began to add routers, mixtures of experts, & small languagemodels. Now we’re realizing the LLM architecture isn’t the best at planning work : reinforcement learning is better & must be integrated. AWS & others have stopped charging to move data. Both have decreased switching costs.
Culture Structure You want a culture of checking results and having metrics to evaluate those results from the LLM or a more traditional model. Historically, Cloud platforms like AWS and Azure help with the sporadic needs of renting a GPU for a few hours for training vs. long-term use, which would cost thousands of dollars.
Perhaps not coincidentally, Snowflake announced a deepened partnership with Nvidia to offer customers models & training on Nvidia’s Nemo platform. Most major cloud players have picked an LLM partner & perhaps will choose multiple. Clouds are picking teams in one of the most important dislocations in software.
Drift brings Conversational Marketing, Conversational Sales and Conversational Service into a single platform that integrates chat, email and video and powers personalized experiences with artificialintelligence (AI) at all stages of the customer journey.
This modern architecture for data analysis, operational metrics, and machinelearning enables companies to process data in new ways. The conference features talks from practitioners and open-source leaders from the ecosystem from Netflix, Microsoft, Expedia, AWS, and Preset.
Cloud Data Lakes are the future of large scale data analysis , and the more than 5000 registrants to the first conference substantiate this massive wave. Mai-Lan Tomsen Bukovec, Global Vice President for AWS Storage will deliver one of the keynotes. This time, the conference will build on the foundation from last year’s event.
The number of patents filed in 2021 in ArtificialIntelligence was 30x the number published six years earlier. We’re on the cusp of a golden age in AI, and the lesson learned from Cloud was that Cloud sped up the pace of development by a lot. Thinking back through Cloud and mobile, what can you learn from them?
Currently, there are 3 primary options available to implement AI in a company: Cloud or LLM providers: Large cloud providers, like AWS, Google, or Microsoft, all provide services to implement generative AI in a secure way in the cloud.
Artificialintelligence is revolutionizing our everyday lives, and marketing is no different, with several examples of AI in marketing today. This article examines what artificialintelligence in marketing looks like today. This article examines what artificialintelligence in marketing looks like today.
The era of largelanguagemodels (LLMs) is booming. In 2025, foundation models or generative AIs like GPT-4, Claude, Gemini, and open-source LLaMA are reshaping AI research, software development, and SaaS products. 405B) ranks 2nd in math/reasoning, 4th in coding, 1st in instruction-following among open models.
Raw silicon (chips like Nvidia bought in large quantities to build out infra to service upcoming demand). Model providers (OpenAI, Anthropic, etc as companies start building out AI). When they started using largelanguagemodels from OpenAI, the gross margin on the same product went to -100%!
The generative AI platform comes with a suite of tools for tuning largelanguagemodels, a data store built on lakehouse architecture, and an AI governance toolkit.
Official Unintelligence – While artificialintelligence has huge potential for improving sales planning, forecasting accuracy, and new rep ramp time, we’re not there yet. At the US Open, IBM promoted to use of artificialintelligence to find match highlights based on audience noise levels, but that’s only one data point.
Amazon Web Services has expanded the capabilities of its Amazon SageMaker machinelearning toolkit to address a number of challenges that enterprises confront when trying to operationalize machinelearning, from model organization, training, and optimization to monitoring the performance of models in production.
AWS has decreased prices for EC2, elastic compute cloud, and S3, simple storage service, 42 times in eight years. The first manifestation of large scale, near-free compute I’ve seen is in machinelearning. When I worked at Google in 2005, we would test individual machinelearningmodels one or two at a time.
And if you think about AWS, if you think about the rise of cloud data warehousing, that is a big technology change and a big game changer for a lot of companies. Suddenly, Alex had gained access to a quantitative look at who might poach his leads, which in turn now allows him to prioritize his competitive intelligence and messaging.
I asked ChatGPT how many price changes AWS has made to S3 since it’s inception in 2006, and the answer it gave me was 65. Here’s what they said “F or Llama 2 and Llama 3, it's correct that the license restricts using any part of the Llama models, including the response outputs to train another AI model (LLM or otherwise).
Machinelearning can get the right message or recommendation out in a responsive way – not just from the customer’s next best action, but from the sales perspective, too. Just from a platform perspective, we can look at it: everybody knows from the AWS perspective how inexpensive it is to go to market.
And so, what happened is I was working on this program on artificialintelligence in medicine that had originated at Stanford under Ted Shortliffe, who was extremely well known, even back then, for building one of the first expert systems to diagnose blood-bacterial infections. Like, this is not anything like artificialintelligence.
As you advance to this position, you can also choose to transition into a data analyst or BI consultant role depending on your interest: Data Scientist : If you’re passionate about statistics, machinelearning, and predictive modeling, you may transition into a data scientist role.
Examples of cost management software include in-platform cost optimization modules like GCP Billing and AWS Cost Explorer. Financial Modeling Tools As well as dedicated cost management software, another key component of the FinOps toolkit is a powerful financial modeling tool like Flightpath Finance.
Most interestingly, we’ll discuss how artificialintelligence has improved the operation of SaaS businesses over the years and what to expect next. Moreover, there were slower innovation cycles compared to the cloud-based SaaS model that we know now, thanks to the advent of smart neural networks. Mobile-friendly design.
At its AWS re:Invent 2019 event, the company acknowledged that computing power can be more useful when it’s closer to home, announcing three new services for reducing latency, including a mini-cloud you can house in your own data center. Get the latest cloud computing insights by signing up for our newsletter. ]
Suddenly, the LLM is spitting out your code or your source. You want to build your own LLM from scratch? They can do it; they’ve done it for large customers. It will be like AWS, GCP, and Azure. Who has the largest LLM? There’s a place for the Cisco routers and for LLM and so on.
It was around that time about 12 years ago that Jeff Bezos launched AWS, and some of you may remember that, when he did this, Wall Street analysts were looking at him and saying, “Why would you take what’s already a very unprofitable business and drive it further into the red by investing in this AWS initiative?”
Products like Amazon Web Services (AWS) and the rise of engineering talent globally have reduced the barrier of entry for software startups in recent years. Buyers aren’t locked into long-term purchases the same way they were with on-premises solutions. There’s more competition and more choices for software buyers.
However, with the rise of cloud storage and machinelearning trends, you may need to handle tasks specific to certain tools, such as: Apply machinelearning algorithms to develop predictive models, automate data analysis tasks, and gain deeper insights from complex datasets.
Author: Avi Sanadhya, ReSci Platform Engineering Team At Retention Science we deliver personalized marketing campaigns powered by machinelearning to drive a deeper level of customer engagement. The post Serverless with AWS Lambda: Reducing metrics reporting lag from hours to seconds at ReSci appeared first on ReSci.
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