Attending AWS re:Invent 2024 was like watching a forest grow and decay at 10,000-times time-lapse speed. With each major breakthrough release falling, Amazon Web Services Inc. might crush a wide swath of products, including a few of its own, while sprouting even more new startups in its wake.
Just pick out any one announcement such as AWS Bedrock Data Automation — which could overshadow a few dozen intelligent document processing and business workflow solutions, at least in the view of AWS customers that aren’t already automating that kind of work.
Indeed, while AWS Bedrock was being infused as the new substrate for multiple artificial intelligence models, large language model, retrieval-augmented generation and so on that will someday run everything, the real progress will still be among vendors and enterprises making cloud infrastructure better support application innovation and operations. AI is just today’s bleeding edge, driving the advancement of scalable and interoperable cloud architectures, massive resilient and secure data infrastructures, and development tools and technologies that are ready for change.
“The ability to scale down cloud infrastructure is just as powerful as the ability to scale up, so we can focus our optimization where it needs to be,” said Ed Peters, vice president and distinguished engineer at Capital One Financial Corp., a company that famously started its journey to cloud with AWS way back in 2016.
Here are just a few of those disruptive AWS projects, partner vendors and startups I managed to catch up in Las Vegas:
Scalability and interoperability
A key takeaway — beyond the advances of speeds and feeds in silicon technology, clustering, storage tiers — was how AWS is striving for interoperability these days, in terms of more open-source involvement, integrations with other vendors, and tools to help companies get their arms around cost and complexity.
For me the newly announced “Buy with AWS” marketplace release represents a nice externalization of this massive ecosystem very well. Just by embedding a widget, vendors no longer need to send their customers over to the main AWS Marketplace to consolidate purchases.
There’s no reason why vendors can’t curate their own marketplaces, and offer some tools that compete with some AWS offerings, and leverage others. If chief information officers and architects are working on their shopping lists here, AWS would always get a cut of that IT budget anyway, even if customers choose some alternative management, observability and deployment tooling.
Sumo Logic Inc. presented its new “Mo” generative AI assistant based on AWS Bedrock as a foundational model. With a natural language prompt, an engineer, developer or business process owner kicks off a “conversation” of multiple virtual site reliability engineering agents behind the scenes, scanning telemetry data and presenting anomalies with a complete service map of dependencies and root cause analysis. An automation service can then suggest the resulting agentic AI consensus of remediation steps that combine security information and event management or SIEM, security orchestration, automation and response or SOAR, and observability workflows.
Vega Cloud Inc. was in Vegas hunting for “cloud whales” — enterprises and managed services providers that might spend millions of dollars annually on cloud and data infrastructure services. After aggregating a custom FinOps data lake with usage and payment data, their service recommends alternative rate options and deployment footprints, including multi-vendor negotiations, availability scheduling actions and alternative solution packaging to meet scale requirements and service level agreements at lower costs.
“AWS announcements on Bedrock, Q and infrastructure to support training and inference for other models have really pulled them into the generative AI conversation,” said Puneet Gupta, chief executive of Amberflo.io, Inc, which introduced new interdepartmental IT chargebacks into its application programming interface and AI metering and monetization solution here. “We’re seeing huge tailwinds for usage-based pricing and metering. As customers incorporate gen AI into their application stack, they have to fundamentally rethink how they sell their products and services to their customers because of variable cost vectors.”
Massive data infrastructure
Remember when S3 buckets used to be considered sort of a catch-all “junk drawer” for cloud object storage? Now we are seeing huge advances from AWS through announcements such as S3 tables and metadata with chipsets and networking to transform those buckets into high-performance, queryable data lakes.
Observability and security vendors are reacting with their own offerings, provisioning Iceberg-style data lakes, lower-cost/faster object stores in S3, and resilient storage and backups to power new AI-augmented search and telemetry.
“The overall scale of this event is impressive, and we’re having lots of conversations with folks trying to build AI-powered applications on top of existing data stores, or support new use cases,” said Steve Kearns, general manager of search at Elastic N.V. “It’s been interesting to see where people are in their adoption curve, their understanding, and their learning on this journey. The further they get down the path to production for building generative AI applications, the more nuanced their understanding of the capabilities they are going to need.”
ScyllaDB Inc. was there demonstrating its unique method of dynamically scaling their NoSQL-style database by sharding data “closer to the metal” across distributed instances for lower latency and cloud scaling costs, and quicker release of unused resources.
With streaming event data volumes sometimes nearing daily petabyte scales, operators and security analysts now want to keep everything without bleeding their entire budget. ChaosSearch Inc. delivers a combined analytics, search and SQL query database platform atop low-priced S3 tiers geared toward optimizing ingest costs and real-time search cost-to-performance ratios when compared with leading SIEM and observability vendor data lake offerings.
Backups can comprise as much as 10-to-20% of a company’s total IT spend for management and storage. Eon Inc.’s immersive “time travel” expo booth demonstrated how its cloud backup management platform tags and retains records according to declarative content policies, retrieving snapshots or individual records with global search and disaster recovery capabilities across AWS and other cloud storage resources.
“We’re seeing a lot of interest here in moving enterprise data to hybrid cloud, with on-premise systems working alongside private and public cloud infrastructure,” said Karthik Ranganathan, CEO of Yugabyte Inc. “Here at re:Invent, AWS just announced Aurora DSQL — a PostgreSQL-compatible, multi-region, horizontally scalable database which validates our core thesis and the movement of bringing distributed data capabilities into the future.”
Development and integration help on the journey
Never has the maxim of enabling people, process and technology, in that order, been more true than today. Developers and engineers need help mastering the many details of building and securing new cloud services with AI futures in mind without breaking their existing critical applications, or breaking the IT budget.
Integrail Corp. offers a drag-and-drop platform for designing and delivering agentic AI workflows. Its low-code process oriented orchestration layer combines multiple AI agents, which may come from different sources, to complete complicated tasks. For instance, you might have different agents recognizing the real-world context and source of a product image, writing a text description of it, and tagging and dropping it in an online catalog, so a natural-language LLM prompt can answer user questions about it.
I’ve definitely heard of synthetic data for software testing and observability, but Gretel Labs Inc. was there with a novel approach for AI builders, creating synthetic datasets for training and fine tuning AI models and LLMs. The product leverages different inference models to generate anonymized real-world data, or create datasets from scratch.
I didn’t expect to see a new rapid application development or RAD tool enter the market, but I visited Retool Inc. and found its modern approach to low-code React component app building and snap-together integrations with back-end Git-style source control, permissions, co-pilots, and deployment workflow capabilities might just fill the bill. Even skilled developers appreciate spending less time coding internal and external apps that are portable to cloud or local infrastructure.
In a cloud development world where permissions are often unused and identities can lie dormant, Apono Inc. offers DevOps teams and engineers a consumer identity and access management platform that allows them to embed “access flow” permissions with just-in-time policy monitoring that dynamically validates least-privilege user access in the workflow context of the application.
WorkOS Inc. offers an access management platform with self-service user activation, role-based access control and identity that seems specifically useful for business-to-business or software-as-a-service startups, with single sign-on, authentication and directory sync based on connections and usage, instead of unpredictable per-user costs.
Since cloud-native development patterns must include a strategy for dealing with legacy technologies and code, I really enjoyed a conversation about modernization with Bill Platt, a general manager at AWS. It has a pragmatic approach to enterprise cloud enablement, including free advisory, training and an expansive system integrator partner program. Some customers are already using its Amazon Q agentic AI development assistant to document codebases and map out a plan for which dependencies to start on.
“Development teams seek out the best tools, and whether they are choosing a language, or using an AI model to help them do their job, they want standardization and repeatability,” said Sunil Mallya, co-founder and chief technology officer of Flip AI, formally Flip Technology Corp. “All LLMs have slightly different prompts and interpret instructions differently,” he said. “The idea of developers freely choosing between AI models and orchestrating them is an illusion. Why create five different codebases to maintain across the team, when the models could be updated at any time?”
The Intellyx take
My last AWS re:Invent coverage in 2019 was a tough slog — I felt overwhelmed with features and overcrowded by every vendor doing anything related to software and infrastructure in general. Perhaps at the time, being surrounded by more than a hundred thousand people with an oncoming pandemic exacerbated my agoraphobia, but I swore never to return.
Now, five years later, I approached with an open mind and found the environment offered much easier routes to understanding and collaboration.
If AWS demonstrated one thing really well here, it’s that it cares about the partners who are their channel to the world, and even more so, the developers and operators who are their end customers. With so many engineers being forced to return to the office or transform their own skill sets, let’s hope that commitment to human ingenuity augmented by AI potential continues.
Or, maybe my bio-brain was digitized upon event entry, and this article is just another product of multimodal agentic AI trained on a RAG of my collected writings and the commentary of other autonomous agents having conversations with each other in a simulated re:Invent environment. Only only time will tell tell tell [glitch].…
Jason English is a partner and principal analyst at Intellyx B.V., an analyst firm that advises enterprises on their digital transformation initiatives, and publishes the weekly Cortex and BrainCandy newsletters. He wrote this article for SiliconANGLE.
©2024 Intellyx B.V. Disclosure: No AI chatbots were used to write this article. At the time of writing, Elastic is an Intellyx customer, and Amberflo and Sumo Logic are former Intellyx customers. None of the other persons or vendors mentioned here is an Intellyx client. AWS covered Jason’s attendance costs for re:Invent, a standard industry practice.
Photo: Jason English
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