Year: 2022

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December 16: From BI to AI (Databricks, Dataiku, insightsoftware, MarkLogic, MathWorks, Microsoft, SingleStore, SQream, Syniti)

Funding and Finance

$200M F Round for Dataiku from Wellington Management
This week, Wellington Management led the latest round of funding for AI stalwart Dataiku, a Series F round of $200M. DAtaiku will use the funding for continued growth and expansion of its platform capabilities.

Launches and Updates

Data Lineage Now Generally Available in Databricks Unity Catalog
On December 12, Databricks announced the general availability of data lineage in their Unity Catalog governance solution, after six months of being in preview. Customers at the Databricks Premium and Enterprise tiers now have access to automatically captured data lineage at no extra cost; they will need to restart their clusters or SQL Warehouses that were last started prior to December 7.

insightsoftware Launches Jet Analytics Cloud
On Monday, insightsoftware released Jet Analytics Cloud, a managed services offering of the Jet Analytics data prep and analytics tool. Jet Analytics Cloud is now available on the Azure public cloud.

MarkLogic Modernizes with Version 11
Veteran data platform MarkLogic announced MarkLogic 11 earlier this week. Key modernization features include improved support for GraphQL, OpenGIS and GeoSPARQL, and OAuth; extended capabilities for the MarkLogic Optic API; more flexible deployment and management options, including support for Docker and Kubernetes; and improved observability, auditability, and manageability capabilities.

MathWorks Debuts Modelscape for Model Management in Regulated Industries
On December 15, MathWorks released Modelscape, a suite of products designed to help primarily financial institutions reduce risk during the model lifecycle while complying with regulatory requirements. The products include Modelscape Governance, which provides centralized access to models, dependencies, metadata, lineage, audit trail, risk scoring, and overall model risk reporting; Modelscape Develop, to develop models with automated documentation and reproducible processes; Modelscape Validate, to validate models; Modelscape Test, to automatically test models before putting them into production; Modelscape Deploy, to deploy models into production without recoding, with both on-prem and cloud options; and Modelscape Monitor, to monitor, analyze, and report on model performance in a dashboard scenario.

SingleStore Announces Version 8.0
SingleStore released version 8.0 of their cloud-native database this week. New capabilities include better query performance with semi-structured data such as JSON data, dynamic workspace scaling, new realtime and historical monitoring capabilities, and OAuth support.

Partnerships

Microsoft and London Stock Exchange Group Announce Long-Term Strategic Partnership
Microsoft and the London Stock Exchange Group have announced a decade-long strategic partnership. LSEG’s data infrastructure will be architected on the Microsoft cloud, and both companies will co-develop new data and analytics products and services. In addition, Microsoft has agreed to purchase a 4% equity stake in LSEG by acquiring shares from the Blackstone/Thomson Reuters Consortium.

Panoply by SQream Now In the Google Cloud Marketplace
Data analytics acceleration platform SQream is now available in the Google Cloud Marketplace. Panoply users will be able to purchase Panoply within GCM, then set up a Google BigQuery instance within Panoply and connect their data.

Syniti Match and Syniti Replicate Available on SAP® Store
Enterprise data management company Syniti has made Syniti Match and Syniti Replicate available in the SAP Store. Syniti Match, integrated with SAP HANA, is data matching software, while Syniti Replicate provides data integration and realtime data streaming. Replicate can also integrate with both SAP HANA and SAP S/4HANA, with change data capture capability and data lake operations.

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December 2: From BI to AI, Part 1 (AtScale, Dremio, Matillion, Informatica, Starburst)

Launches and Updates

AtScale’s Semantic Layer Launches on Google Cloud Marketplace

Semantic layer platform AtScale announced Thursday that it was now available on Google Cloud Marketplace. Mutual customers will be able to use AtScale on Google Cloud with services such as Google BigQuery, where they can run BI and OLAP workloads without needing to extract or move data. 

Dremio Announces Updates to Lakehouse Capabilities

Open data lakehouse Dremio announced a number of improvements this week. Among the updates: new SQL functionality, including support for the MAP data type so users can query map data from Parquet, Iceberg, and Delta Lake; security enhancements such as row and column-level policy-defined access control for users; support for INSERT, DELETE, and UPDATE on Iceberg tables, and for “time travel” to query historical data in place; as well as usability and performance improvements. Dremio also added a number of connectors, including dbt, Snowflake, MongoDB, DB2, OpenSearch, and Azure Data Explorer. 

Informatica Reveals AWS-Specific Cloud Data Management Services

At AWS re:Invent 2022, Informatica announced three new capabilities for Informatica within AWS. Informatica Data Loader is embedded within Amazon Redshift so that mutual customers will be able to ingest data from a wide variety of systems, including AWS. The Informatica Data Marketplace now supports AWS Data Exchange, allowing customers to access and use third-party data hosted on the Data Exchange. And Informatica INFACore, INFA’s new development and data science framework, simplifies the process of developing  and maintaining complex data pipelines, which can be shunted over to Amazon SageMaker Studio as a simple function, allowing users to pull prepared data from INFACore into SageMaker Studio for further use in building, training, and deploying machine learning models on SageMaker.

Matillion Accelerates Productivity for Data Teams with Key Ecosystem Integrations | Matillion 

Data productivity platform Matillion announced a number of integrations with technical partners. Most of these integrations are accelerators that speed up some aspect of data processing between Matillion and its partners. FHIR Data, built by Matillion and Hakkoda, is a Snowflake healthcare data integrator that simplifies the process of loading FHIR data in Snowflake, then transforming it into a structured format for analytics processing. AWS Redshift Serverless Scale will let mutual Matillion-AWS customers run analytics without needing to manually provision or manage data warehouse clusters. Matillion One Click within AllCloud automates the setup and maintenance of data pipelines. Finally, the Matillion-Collibra integration creates data lineage, mapping inbound and outbound data flows, and attaches data objects to assets in the Collibra data catalog.

Starburst Grows Galaxy with Data Products Capabilities

Analytics engine Starburst launched new capabilities for Starburst Galaxy, the managed service version of its primary Starburst Enterprise offering.  The new Data Products capabilities include a catalog explorer for users to search through their data more easily and understand what they have; schema discovery, which can help users find new datasets regardless of their storage format; and enhanced security and access controls. 

Starburst Enterprise Now Supports AWS Lake Formation and Further Data Federation

Starburst also announced support for AWS Lake Formation via Starburst Enterprise, which will allow joint customers to more easily implement a data mesh framework across all of an organization’s data sources.

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Quick Take: IBM Sues Micro Focus for Software Theft

Yesterday, IBM filed suit against Micro Focus for claims of copying part of the z/OS for data mapping in the web services implementation of Micro Focus Enterprise Suite. To understand this suit, I think the most relevant excerpts of claims in the suit are:

26. CICS® TS (Customer Information Control System Transaction Server) Web Services uses a “web service binding file,” known as a WSBIND file, to expose CICS® TS programs as web services and maps data received.

40. Micro Focus’s Enterprise Suite offers a web services implementation (“Micro Focus Web Services”) that includes a WSBIND file for mapping data
• Micro Focus’s WSBIND file uses IBM internal structures that are not available outside of IBM.
• The Micro Focus utility processing reflected in the log file exhibits the same configuration, program sequence, program elements, program optimizations, defects, and missing features as the corresponding CICS® TS utility programs.
• Micro Focus’s WSBIND file is encoded in EBCDIC—like IBM’s—yet, Micro Focus has no need for using that encoding as it uses an ASCII environment.

(Analyst’s note: I think this is probably going to be one of the key hinges of the lawsuit. EBCDIC is really an IBM-specific format at this point while ASCII is everywhere. A bit weird to use IBM’s specific encoding for characters.)

42. …no legitimate reason for Micro Focus to have copied IBM’s computer program. Without copying from IBM, Micro Focus had a broad range of design and architectural choices that would have allowed it to create software that offers the same features as the Micro Focus Enterprise Suite.

It’s no secret that IBM has bet the farm on modernization and digital transformation (see Red Hat). The ability to manage IBM customer technology evolution is core to the future of the business. If nothing else, this suit sends a strong message: Don’t Mess with the zSeries. I’m interested to see how this suit will reference Google vs. Oracle: this isn’t the same, but I’d imagine Micro Focus will try to make it sound that way.

Copy of complaint if you want to read the entire IBM legal complaint for yourself : https://filecache.mediaroom.com/mr5mr_ibmnewsroom/194536/Micro%20Focus%20Complaint.pdf

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November 11: From BI to AI (Anaconda, Coefficient, Databricks, dbt, Domino Data Lab, Matillion, Neo4J, PwC, Sagence, Snowflake, Tellius)

Editorial note: While Twitter and Meta aren’t precisely on the BI to AI spectrum per se, given the sheer prevalence and use of these massive data sources and the fast-moving news around layoffs and security issues for these properties lately, Amalgam Insights would recommend confirming that you are performing backups of relevant data on a regular schedule; that you are taking available security precautions including the use of two-factor authentication where possible; and that your communications strategies are nimble enough to respond to issues of impersonation. 

Funding and Finances

Coefficient Secures $18M Series A Funding Round

Spreadsheet data automation company Coefficient has raised an $18M A round. Battery Ventures led the funding round, with participation from existing investors Foundation Capital and S28 Capital. Coefficient will use the money to scale up global operations and expand its offerings.

Databricks Ventures Invests in Matillion

Strategic investment firm Databricks Ventures has taken an equity stake for an undisclosed sum in Databricks partner Matillion, a data integration solution. In doing so, Databricks extends their existing partnership with Matillion, providing financial support for Matillion’s Data Productivity cloud and how it works with Databricks’ Lakehouse Platform.

Updates and Launches

Tellius Improves User Experience and Ease of Visual Analysis with Version 4.0

Decision intelligence platform Tellius announced version 4.0 on Wednesday. Key new features include Multi-Business View Vizpads, allowing users to view and analyze across multiple data sources without needing to constantly switch between individual dashboards for each; an enhanced onboarding and ongoing user experience with walkthroughs and in-app chat; and more robust search functionality.

Neo4j Announces General Availability of its Next-Generation Graph Database Neo4j 5

Graph data platform Neo4J made Neo4J 5, the next version of its cloud-ready graph database, generally available earlier this week. Among the notable improvements: new syntax making complex queries easier to write; query performance improvements by up to 1000x; automatic scaleout to handle sudden massive bursts of query activity; and the debut of Neo4J Ops Manager to monitor and manage continuous updates across global deployments. 

Partnerships

Anaconda Will Integrate with Domino’s Enterprise MLOps Platform

Anaconda in Snowpark for Python Enters Public Preview

Anaconda made two partnership announcements this week. First, Anaconda is collaborating with Domino Data Lab to incorporate the Anaconda repository into Domino’s Enterprise MLOps Platform. Domino users will be able to access Anaconda’s Python and R packages without requiring a separate Anaconda enterprise license.

Second, Snowpark for Python, the Anaconda repository and package manager within Snowflake Data Cloud, has entered public preview. Snowflake users will be able to use Python to build data science workflows and data pipelines within Snowpark.  

On a related note, dbt Labs also announced support for data transformation in Python to dbt, allowing dbt customers to take advantage of Python capabilities on major cloud data platforms such as Snowflake. Joint dbt and Snowflake customers will be able to use Python capabilities for both analytics and data science projects on Snowpark.

Acquisitions

PwC Acquires Data Strategy Consulting Firm Sagence 

Data management and analytics consulting firm Sagence has been acquired by PwC, adding to PwC’s existing data strategy and digital transformation capabilities. Sagence provides additional expertise and experience in creating action plans for proposed data strategies.


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November 4: From BI to AI (Alation, Cloudera, Collibra, IBM, Informatica, Qlik)

Funding

Alation Raises $123M Series E

Enterprise data intelligence platform Alation announced a $123M Series E round of funding this week. Thoma Bravo, Sanabil Investments, and Costanoa Ventures led the round, with additional participation from new investor Databricks Ventures and existing investors Dell Technologies Capital, Hewlett Packard Enterprise, Icon Ventures, Queensland Investment Corporation, Riverwood Capital, Salesforce Ventures, Sapphire Ventures, and Union Grove. Alation will use the capital to continue accelerating product innovation and global expansion.

Partnerships

Cloudera Expands Partner Opportunities, Accelerates Go to Market

On November 2, Cloudera debuted the Cloudera Partner Network, redesigning their existing partner program’s approach. CPN members will see improved tools for supporting go-to-market initiatives, a FastTrack Onboarding Program to shorten time-to-market capabilities, programs for rebates and market development funds to demonstrate financial commitment, a Partner Success Team to improve training, and additional benefits to support the new CDP One SaaS solution.

Launches and Updates

Collibra Announces Updates to Collibra Data Intelligence Cloud

At Data Citizens ’22, Collibra announced new capabilities for Collibra Data Intelligence Cloud. A new data marketplace will make it faster and easier to find curated and approved data, speeding up decision making and action. The Workflow Designer, now in beta, will help teams automate business processes in creating new workflows, and usage analytics will show which assets are most widely and frequently used. On the compliance side, Collibra also released Collibra Protect, available through their Snowflake partnership, to provide greater insight into how protected and sensitive data is being used, as well as protect said data and maintain compliance.   Collibra Data Quality and Observability, when deployed in an organization’s cloud, will help organizations scale and secure their data quality operations.

IBM Announces IBM Business Analytics Enterprise  

On November 3, IBM launched IBM Business Analytics Enterprise, a new suite that includes business intelligence planning, budgeting, reporting, forecasting, and dashboarding capabilities. Among the key new features is the IBM Analytics Content Hub, which will let users assemble planning and analytics dashboards from a number of vendor sources. In addition, the Hub tracks and analyzing usage patterns to recommend role-based content to users.

Up Next for Informatica: Intelligent Data Management Cloud for State and Local Government 

Informatica released the State and Local Government version of their Intelligent Data Management Cloud this week. This continued expansion of vertical-specific IDMCs demonstrates Informatica’s commitment to serving a variety of government clients beyond just the federal.

Qlik Launches Real-Time Enterprise Data Fabric Qlik Cloud Data Integration

Qlik released Qlik Cloud Data Integration, a set of SaaS services that form a data fabric. Among the major cloud platforms QCDI will integrate with are AWS, Databricks, Google Cloud, Microsoft Azure Synapse, and Snowflake. Data from these sources will be sent through QCDI, where it can be transformed from its raw state to analytics-ready, allowing for automated workflows for apps and APIs while accounting for metadata management and lineage.

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October 28: From BI to AI (Amazon Web Services, Axelera AI, Bloomberg, data.world, Informatica, IBM, LatticeFlow, Microsoft, OpenAI, Shutterstock, ThoughtSpot)

Funding and Finance

Edge AI Company Axelera AI Announces $27M Series A

Edge AI startup Axelera AI closed a $27M Series A funding round this week. Innovation Industries led the round, with participation from imec.xpand and SFPI-FPIM. Axelera AI will use the funding for launching and producing its AI acceleration platform, as well as hiring.

LatticeFlow Claims $12M in Series A Funding

AI platform LatticeFlow secured $12M in Series A funding. Atlantic Bridge and OpenOcean led the funding round, with participation from new investors FPV Ventures and existing investors btov Partners and Global Founders Capital. The funding will go towards expanding LatticeFlow’s capacity to diagnose and repair errors in AI data and models,

OpenAI Invests Tens of Millions of Dollars in Audio and Video Editing App Descript

AI company OpenAI, most recently in the news for its AI text and image generators, is investing tens of millions of dollars in audio and video editing app Descript at a valuation of $550M. Between this, the recent expansion of its partnership with content platform Shutterstock, and more money potentially flowing OpenAI’s way from Microsoft, OpenAI is making moves to secure its position in modern AI-based content creation.

Launches and Updates

AWS Releases Amazon Neptune Serverless 

AWS announced Amazon Neptune Serverless, a serverless version of their graph database service which automatically provisions and scales resources for unpredictable graph database workloads. Amazon Neptune Serverless is available today to AWS customers running Neptune in specific regions; availability in other regions is coming soon.

Bloomberg Carbon Emissions Dataset Now Covers 100,000 Companies

Bloomberg announced that it had enlarged its carbon emissions dataset to cover 100,000 companies. The dataset includes company-reported carbon data, as well as Bloomberg-generated estimates of carbon data for companies that do not have or provide carbon emissions data, and accompanying data reliability scores.

Informatica Launches Intelligent Data Management Cloud for Higher Education

Adding to their collection of vertical-specific releases of its Intelligent Data Management Cloud, Informatica launched their Intelligent Data Management Cloud for higher education this week. IDMC helps integrate educational data from a wide variety of decentralized sources while ensuring data remains secure and compliance and privacy standards are respected.

IBM Adds Natural Language Processing, Text to Speech, Speech to Text Libraries to Embeddable AI Portfolio

IBM released three new libraries this week, expanding their embeddable AI portfolio. These libraries include IBM Watson Natural Language Processing Library, IBM Watson Text to Speech Library, and IBM Watson Text to Speech Library. IBM Ecosystem partners will be able to use these libraries to develop and scale AI apps more quickly. 

ThoughtSpot for Sheets Brings Self-Service Analytics to Google Sheets

Analytics company ThoughtSpot debuted ThoughtSpot for Sheets, a web plugin for Google Sheets. Users will be able to install and run ThoughtSpot for Sheets directly in their web browser, capable of analyzing the data available in their Google Sheets spreadsheets while minimizing the technical knowledge necessary to do so. ThoughtSpot will be compatible with additional partners in the near future.

Hiring

data.world Names New CMO, SVP of Sales, VP of Finance

Enterprise data catalog company data.world made several hiring announcements this week. New Chief Marketing Officer Stephanie McReynolds joined data.world from Ambient.ai, where she served as head of marketing. Prior to that, McReynolds was the SVP of Marketing and first marketing executive at fellow data catalog company Alation. New SVP of Sales Richard Yonkers came to data.world from Knoema, an enterprise data hub provider, where he served as the senior vice president of sales. Mineo Sakan was the VP of Global Finance at data protection firm HYCU prior to joining data.world as their new VP of Finance.

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October 21: From BI to AI (Alteryx, Bloomberg, Dataiku, Google Cloud, Oracle, Stability AI, Tableau, Tellius, TigerGraph)

Oracle CloudWorld Announcements

Oracle made a number of announcements at Oracle CloudWorld this week.

Oracle Database 23c Now in Beta
Version 23c of Oracle Database, code named “App Simple,” is now available in beta. As highlighted by the code name, improvements focused on simplifying application development, particularly for apps written using JSON, Graph, or microservices. JSON Relational Duality is Oracle’s new approach to allow data to be simultaneously used and understood as both app-friendly JSON documents and as database-friendly relational tables, allowing JSON app data to be directly queried.

Oracle Launches MySQL HeatWave Lakehouse
Oracle expanded the MySQL HeatWave portfolio with the addition of MySQL HeatWave Lakehouse, which will allow customers to process and query data in object store at multi-terabyte scale. This will directly compete with Redshift and Snowflake in providing a cost-efficient, performant lakehouse offering.

Oracle Innovates Across Data and Analytics Portfolio
Oracle also announced product innovations across its data and analytics portfolio. Oracle Analytics Cloud added a semantic modeler to present the semantic model to business users in an appropriate manner; advanced composite visualizations to organize content and present data patterns and signals in a more easily understood manner; one-click automated insights that provide recommendations for visualizations; and AI and ML enhancements to connect Oracle Cloud Infrastructure cognitive services to Oracle Analytics Cloud. Oracle Fusion Analytics improved their existing ERP, SCM, and HCM analytics solutions with vertical-specific enhancements, and added Oracle Fusion CX Analytics to the OFA portfolio to give sales, marketing, service, and finance users KPIs and dashboards.

Oracle and NVIDIA Team Up to Accelerate Enterprise AI Adoption
Finally, Oracle and NVIDIA announced a partnership that will bring NVIDIA’s complete accelerated computing stack, including tens of thousands of GPUs, to Oracle Cloud Infrastructure to permit AI training and deep learning inference at scale.

Funding

Stability AI Announces $101M Seed Round
Open source AI company Stability AI announced a $101M seed round at a nearly $900M valuation. Coatue, Lightspeed Venture Partners, and O’Shaughnessy Ventures LLC led the round. Stability AI will use the funding to speed up the development of open AI models across a wide variety of use cases, consumer and enterprise alike.

Tellius Raises $16M B Round
AI decision intelligence platform Tellius announced a $16M B round of funding, announced October 20. Baird Capital led the round, and all existing investors also participated: Grotech Ventures, Sands Capital Ventures, and Veraz Investments. Tellius will use the funding to expand go-to-market, hire across sales, marketing, and product engineering, and R+D for their platform.

Launches and Updates

Alteryx Announces New Analytics Cloud Capabilities, Version 22.3
At Inspire EMEA 2022, Alteryx announced the 22.3 Alteryx product release, and additional enhancements to Alteryx Analytics Cloud. Alteryx Machine Learning is now running on Alteryx Analytics Cloud, and Designer Cloud has improved Snowflake data processing performance when moving from AWS. As for Alteryx 22.3, Alteryx’s Data Connection Manager supports Azure Active Directory group authentication for Databricks and Snowflake, and integrations with third-party vaults like CyberArk and HashiCorp. 22.3 also features enhanced Google BigQuery connectivity and performance improvements.

Bloomberg Data License Content Now Available on Google Cloud
Bloomberg made its Data License content available on Google Cloud. Clients will be able to integrate this cloud data into their Google Cloud-specific workloads directly.

Tableau Launches Version 2022.3
Tableau announced version 2022.3 this week. Key new features include a Data Guide guided experience to Tableau, Table Extensions that will let customers enrich their data with advanced analytics and predictions, and adding “dynamic zone visibility” functionality to dashboards, allowing users to see only the dashboard contents that are relevant without needing to manually design multiple individual dashboards.

TigerGraph Will Support openCypher in GSQL.
TigerGraph announced this week that they would support the openCypher query language in TigerGraph’s own graph query language. Providing this support will make it easier for developers to build or migrate graph applications to TigerGraph databases.

Partnerships

Dataiku and Slalom Team Up on AI Strategy
Dataiku announced a partnership with Slalom, a global consulting firm. Slalom will provide strategic guidance to Dataiku enterprise customers on implementing AI and MLOps projects.

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Cloud Cost Management Vendor Profile: IBM Turbonomic

Amalgam Insights continues to present its list of Distinguished Vendors for Cloud Cost and Optimization Management. This matters because analysts assessed nearly 30 providers for this effort; only a third were able to demonstrate genuine differentiators and approaches that satisfied Amalgam Insights’ requirements for achieving Distinguished Vendor status. To that point, we already have posted profiles on SADA, Spot by NetApp, Apptio Cloudability, Yotascale, Kion, and CAST AI . We next discuss IBM Turbonomic.

WHY IBM TURBONOMIC FOR CLOUD COST AND OPTIMIZATION MANAGEMENT

  • Focus on application performance, which leads to savings
  • Platform configuration is automated, saving IT time and effort during deployment
  • Software learns from organizations’ actions, so recommendations improve over time

ABOUT IBM TURBONOMIC

IBM Turbonomic is an Amalgam Insights Distinguished Vendor for Cloud Cost and Optimization Management. Founded in 2009, Turbonomic was acquired by IBM in 2021. IBM Turbonomic now acts as Big Blue’s solution to ensure application performance and governance across cloud environments, including public and private. Turbonomic has two offices in the United States — its headquarters in Boston and a satellite location in Newark, Delaware — as well as one in the UK and another in Canada. IBM does not publicly disclose how many Turbonomic employees it has, nor does it break out Turbonomic annual revenue or provide customer retention rates.

In terms of cloud spend under management, Turbonomic states that it does not track the amount of money its clients spend on cloud computing. Turbonomic serves Fortune 2000 customers across industries including finance, insurance, and healthcare. Turbonomic is typically considered by organizations that have at least 1,000 cloud instances or virtual machines; many support tens of thousands.

IBM TURBONOMIC’S OFFERING

IBM Turbonomic Application Resource Management targets application performance and governance throughout an organization’s cloud environment, which can include public cloud (Amazon Web Services, Microsoft Azure, Google Cloud), private cloud (IBM, VMware), and multi-cloud environments.

The platform optimizes cloud computing, storage, database as a service, reserved instances, and Kubernetes, but does not currently address spot instances). Furthermore, it optimizes and scales based on IOPs (input/output), reservations, and discounts. Overall, IBM Turbonomic aims to ensure spend aligns to applications, preventing cost overruns and keeping applications performing optimally. While Turbonomic mainly serves IT users, Turbonomic recently teamed with Flexera to add a detailed cost-reporting module that appeals to Financial Operations (FinOps) experts.

IBM Turbonomic charges for its cloud application optimization software based on the number of resources under management. Rather than offering individual add-on capabilities, IBM Turbonomic lets clients choose more advanced capabilities by buying different licensing tiers associated with integrations to other software and processes such as IT service management, orchestrators, and application performance management. IBM Turbonomic includes technical support with all tiers. IBM Turbonomic and its third-party channel partners offer professional services as needed.

IBM Turbonomic states that its top differentiator originates from artificial intelligence that matches application demand to underlying infrastructure supply at every layer of the stack continuously in real-time with automatable resourcing decisions. As more organizations use IBM Turbonomic, the automated recommendations provided to all of its customers improve. Cloud administrators gain insight into suggested actions, such as investments to enhance performance and save money.

IBM Turbonomic Application Resource Management is delivered as software-as-a-service. It works across public, private, containerized, and bare metal cloud environments. IBM Turbonomic’s reference customers include Providence Health, which has 120,000 employees; Litehouse Foods, which makes salad dressing, cheese, and other foods; and apparel maker Carhartt.

COMPETITION AND COMPETITIVE POSITIONING

IBM Turbonomic mainly competes against organizations’ in-house spreadsheets and mix of tools that are specific to the technologies in use. In these cases, IBM Turbonomic finds that organizations are over-provisioning cloud computing resources in the hopes of mitigating risk. Therefore, they are spending too much and only addressing application performance when something goes wrong.

IBM Turbonomic also often faces VMware CloudHealth in its prospective deals.

IBM Turbonomic states that it draws customers because of automation and recommendations that tend to result in the following business outcomes:

  • Reduction of public cloud spend by 30%
  • Increase in team productivity by 35%
  • Improvement of application performance by 20%
  • Increase in speed to market by 40%

IBM TURBONOMIC’S PLANS FOR THE FUTURE

IBM Turbonomic keeps its roadmap private, so details about upcoming enhancements are not public. However, Amalgam Insights believes that IBM Turbonomic will pursue improvements in sustainability reporting and GitOps resizing in the near future, and may soon pursue a deeper relationship with Microsoft Azure, given that three of these areas are of interest to IBM Turbonomic’s current client base.

AMALGAM INSIGHTS RECOMMENDATIONS

Amalgam Insights recommends that organizations with a minimum of 1,000 cloud instances or virtual machines, and residing within the Fortune 2000, consider IBM Turbonomic Application Resource Management.

Because the platform automatically configures during deployment, provides ongoing recommendations for application and cloud-configuration improvement, and continues to learn from users’ actions, organizations can observe how cloud environments are continuously optimized. This allows IT teams to support cloud consumption needs while also ensuring the organization does not overpay or underresource. In addition, FinOps professionals gain the information they need to track and budget digital transformation efforts without burdening their IT counterparts.

Combined, these capabilities are critical to organizations’ goals of delivering stewardship over their cloud environments while maintaining fiscal responsibility that best serves shareholders, investors, and staff.

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Cloud Cost Management Vendor Profile: CAST AI

Cast AI - Amalgam Insights' 2022 Distinguished Vendor for Cloud Cost Management

Managing cloud infrastructure is no easy task, especially when containers such as Kubernetes come into play. In our ongoing effort to help organizations understand what they need to do to make the most of their cloud environments, Amalgam Insights this year briefed with a number of management and optimization vendors. We continue to publish our findings, which include analyst guidance complete with a series of vendor profiles. This installment focuses on CAST AI, a company that takes a different approach to cloud cost and optimization management by homing in on containers. Read on to learn why that is so important and to understand Amalgam Insights’ resulting recommendations for enterprises.

WHY CAST AI FOR COST CLOUD COST AND OPTIMIZATION MANAGEMENT

  • Optimizes Kubernetes containers on a continuous basis
  • Company claims to save users an average of 63% on cloud bills
  • Cost reporting and cluster analysis provided as a free service

ABOUT CAST AI

CAST AI is an Amalgam Insights Distinguished Vendor for Cloud Cost and Optimization Management. Founded in 2019, Miami-headquartered CAST AI employs 60 people in Florida and Lithuania. It raised $10 million in Series A funding in fall of 2021, following its $7.7 million seed round in late 2020. CAST AI does not look for a specific customer size; some of its users have fewer than two dozen virtual machines, while others run thousands. The privately held firm does not disclose annual revenue or how much cloud spend it manages.

CAST AI’S OFFERING

CAST AI automates and optimizes Kubernetes environments on Amazon Web Services (AWS) Elastic Kubernetes Service, kOps running on AWS, Microsoft Azure Kubernetes Service, and Google Cloud Platform Google Kubernetes Service as well as Kubernetes clusters running directly on CAST AI.

Cast AI users — who typically are DevOps (Development Operations) experts — may run cost reporting that includes cluster analysis and recommendations. FinOps (Financial Operations) professionals can take the reporting results and incorporate them into their practices.

The CAST AI engine goes beyond cost reporting to rearrange Kubernetes environments for the most effective outcomes. To do this, CAST AI connects to a specified app, then runs a script that installs agents to collect information about the app. After that, a report pops up that can provide recommendations for reducing the number of Kubernetes machines or changing to a different compute platform with less memory, all to cut down on cost.

If a user accepts CAST AI’s recommendations, he or she can click a button to optimize the environment in real time. This button sets off a continuous optimization function to give orders to Amazon Elastic Kubernetes Service (EKS), Google Kubernetes Engine (GKE), or Azure Kubernetes Service (AKS) to rearrange itself, such as autoscaling in real time and rebalancing clusters. Users set their desired automation and alerting thresholds. CAST AI pings the app every 15 seconds and produces an hourly graph. CAST AI claims its users save an average of 63% on their cloud bills.

Pricing for CAST AI varies. CAST AI does not enforce a minimum spend requirement. Rather, it charges by the number of active, optimized CPUs. That starts at $5 per CPU per month and there are tiered discounts from 1-5,000 CPUs, then 5,001-15,000, and so on. Base subscriptions start at $200 per month and go up to $5,000 per month or more, depending on volume discounts. CAST AI provides cost reporting and cluster analysis for free, with no time limits. Users also can buy cost management as a standalone service.

COMPETITION AND COMPETITIVE POSITIONING

CAST AI competes most frequently against the Ocean platform from Spot by NetApp in competitive deals. For the most part, though, CAST AI “competes” against DevOps professionals trying to reduce cloud costs manually — a difficult and time-consuming effort.

CAST AI finds that it gains customers because of its engine’s ease of use and ability to make changes in real-time. This further frees DevOps experts to focus on innovative projects.

CAST AI goes to market via its website and, in Europe, Asia, and the United States, also through third-party partners.

CAST AI’s reference customers including La Fourche, a French online retailer of organic products, and ecommerce consultancy Snow Commerce.

CAST AI’S PLANS FOR THE FUTURE

CAST AI plans to build an air-gapped version of its engine disconnected from the Internet and fully supported within the customer’s internal environment for private cloud users in vertical markets including government and banking. Because CAST AI collects metadata to optimize Kubernetes environments, CAST AI is working on this capability to support more governed industries and organizations.

AMALGAM INSIGHTS’ RECOMMENDATIONS

Amalgam Insights recommends that organizations with Kubernetes containers try CAST AI’s free trial to understand how the platform might help save money and optimize resources. Although Kubernetes has largely won as the software container of choice in DevOps environments, businesses still have not standardized on strategies to optimize the compute and storage associated with containerized workloads and services. Amalgam Insights believes that Kubernetes optimization should not be a long-term direct responsibility for developers and architects as tools emerge to define the resources that are most appropriate for running containerized applications at any given time.

Organizations worldwide are struggling to control cloud costs, especially as they pursue containerization and cloud refactorization projects associated with digital transformation. Organizations also are cleaning up pandemic-spurred cloud deployments that quickly got out of hand and have proven difficult to keep in line since then. CAST AI’s technology provides an option that DevOps engineers should consider as they seek to tighten and optimize the spend tied to applications containerized in the cloud.

Need More Guidance Now?

Check out Amalgam Insights’ new Vendor SmartList report, Control Your Cloud: Selecting Cloud Cost Management in the Face of Recession, available for purchase. If you want to discuss your Cloud Cost Management challenges, please feel free to schedule time with us.

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Cloud Cost Management Vendor Profile: Kion

Organizations juggling services from the major public cloud providers — Amazon Web Services, Microsoft Azure, and Google Cloud Platform — often struggle to streamline the disparate data that emerge. For businesses and government agencies with a deliberate focus on financial management, Kion can prove a management option to consider.

In Amalgam Insights’ latest profile featuring cloud cost management and optimization vendors, we discuss what Kion does and how the company differs from its competition.

WHY KION FOR CLOUD COST AND OPTIMIZATION MANAGEMENT

  • Single-platform approach for identity and access management, financial control, and security configuration across the top three public cloud platforms
  • Deep emphasis on compliance checks, including United States federal government compliance
  • Insight into financial management beyond spend savings to include budgeting, forecasting, and compliance

ABOUT KION

Kion is an Amalgam Insights Distinguished Vendor for Cloud Cost and Optimization Management. Formerly known as cloudtamer.io, Kion was founded in 2018. While it is headquartered in Fulton, Maryland, Kion takes a remote-first approach to employment, so it has staff across the United States — more than 50 people as of May 2022. Kion reports annual revenue of between $5 million and $10 million and holds a Net Promoter Score of 81. The company serves about 45 clients collectively spending more than $500 million across IaaS and PaaS.

Most of Kion’s customers fall into three segments: government, higher education, and commercial enterprise. For the government and higher education markets, Kion helps agencies manage funding allocations, align spend with appropriations, meet standards, and ensure compliance (e.g. FedRAMP, NIST SP 800-171, CMMC). Enterprises tend to look to Kion for help in improving the engineer experience to accelerate cloud adoption.

KION’S OFFERING

Kion calls its approach to cloud governance and management “cloud enablement.” This term is intended to describe a combination of automation and orchestration, financial management, and ongoing compliance checks to gain visibility into and control over multiple clouds. 

Kion installs in the customer’s cloud account rather than as a software-as-a-service solution. Kion supports management across cloud providers and lets users integrate with identity access management tools including Okta, native Active Directory, and OneLogin; with IT service management platforms including ServiceNow, Jira, and Splunk; and with host-level vulnerability management technologies including Tenable.

Kion’s platform provides capabilities for automation and orchestration, financial management (FinOps), and continuous compliance, including self-service provisioning for use governance. For financial management, Kion users can allocate funds, receive alerts on potential budget overruns, and proactively remediate cost issues. Kion’s compliance measures contain auto-remediation for governance policy issues, as well. Kion assesses factors such as budget and funding sources to show what remains in a budget, and where that money came from to align cost management, budgeting, and forecasting. Kion’s compliance capabilities enforce policies across clouds at project and resource levels. Kion supports more than 4,500 compliance checks and provides a security control matrix that displays how cloud layers are meeting requirements.

Admins can enforce budget actions that prevent overspending in DevOps and sandbox environments. Kion pulls in data from multiple sources to auto-populate fields.

Standard support for Kion includes email with a two-business-day response time, and access to the Kion Support Center. Customers may procure annual premium support to have a dedicated technical account manager.

Kion’s pricing depends on cloud spend under management. The company sells directly through its in-house sales teams, third-party partners, and on cloud marketplaces. Kion also provides a back-end platform which can be white-labeled to support managed service providers, resellers, and system integrators.

COMPETITION AND COMPETITIVE POSITIONING

Kion wins customers based on several operational challenges. First, it attracts users who struggle to move into the cloud quickly. Second, Kion appeals to organizations struggling with cloud spend excesses, wasted resources, and security concerns. Third, Kion lands users lacking preventive controls. Finally, the company gets new business from companies seeking to remove complexity as they provision new cloud accounts and projects.

Kion competes most often against internal processes or homegrown tools. It also competes against Spot by NetApp and CloudHealth by VMware in the financial reporting realm. On the security end, Kion faces Fugue and Turbot in competitive deals. Some organizations view Kion as a platform that integrates those products (and others), or as a tool that will eventually replace those other brands. Rather than bifurcate personas into specific operational responsibilities — say, by FinOps, SecOps, and DevOps — Kion aligns with business personas and role-based governance across the platform based on functional need and to control spend.

Kion states that customers see savings of at least 30 percent through savings opportunities and cost optimizations. With automation, Kion claims soft-dollar savings can exceed 60 percent if organizations are willing to also review staffing, process, and policy decisions. Kion’s reference customers include NASA, Indeed, the Centers for Medicare & Medicaid Services, and Encamp.

KION’S PLANS FOR THE FUTURE

Kion plans to build more contextual insight to display cloud environment behaviors that are interrelated so users can make more informed decisions. Kion also intends to add more compliance policies as well as cloud platforms beyond the Big Three of Amazon Web Services, Microsoft Azure, and Google Cloud Platform.

AMALGAM INSIGHTS’ RECOMMENDATIONS

Amalgam Insights recommends that organizations — particularly in government, higher education, and commercial enterprise — trying to manage one or more of the top three public cloud providers consider Kion. Kion focuses on ease-of-use and cross-departmental visibility to avoid overspending and ensure security and compliance. That streamlined approach provides a single view for cloud cost and optimization management, budgeting, and technology implementation. Amalgam Insights recommends Kion for companies that seek to cut cloud costs and consider financial management to be part of a greater effort of improving operational performance associated with cloud services.

Need More Guidance Now?

Check out Amalgam Insights’ new Vendor SmartList report, Control Your Cloud: Selecting Cloud Cost Management in the Face of Recession, available for purchase. If you want to discuss your Cloud Cost Management challenges, please feel free to schedule time with us.