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  • Aditi Consulting Achieves Anthropic Select Tier After Putting Claude Into Production Across Its Business and Client Environments

    Aditi Consulting Achieves Anthropic Select Tier After Putting Claude Into Production Across Its Business and Client Environments

    The milestone follows Aditi’s broader effort to reshape its business, client delivery, and engineering workforce.

    BELLEVUE, Wash., Oct. 6, 2026 — Aditi Consulting has achieved Select Tier status in Anthropic’s Claude Partner Network, Anthropic’s program for consultancies and specialized AI firms that help enterprises adopt and implement Claude.

    The designation follows results from work already underway:

    • Inside Aditi: Claude now supports production workflows across engineering, delivery and business operations. Month-end margin analysis went from hours to under two minutes. Reconciliation, close and payables automations are in production, and an AI-enabled delivery command center catches issues before they create downstream problems.
    • With clients: Aditi and Advarra moved from an initial concept to a working application in six weeks.
    • Across its workforce: 82 engineers in Aditi’s first two cohorts are developing the technical judgment, business context and accountability required to apply AI in real delivery environments.

    That work has shaped how Aditi approaches AI transformation with clients. As AI becomes more capable, the challenge is no longer the technology itself. It is deciding where AI can create value, engineering it into the business and building the capability to sustain it.

    “Access to increasingly capable AI isn’t the hardest part of transformation anymore,” said Carrick Carpenter, chief technology and AI officer at Aditi Consulting. “The harder question is whether the organization around it is ready — the systems, governance, workflows and people. We chose to work through those questions inside our own business first, and that experience now shapes how we help clients put AI into production.”

    Aditi is applying that experience in client environments. At Advarra, a clinical research technology company, Aditi worked alongside Advarra’s teams using Claude and AI-assisted development. Within seven working days, Advarra and its end customer had a working experience they could use to validate the direction.

    “Driving value for our customers is our North Star,” said Brian Hart, chief technology officer at Advarra. “We’re building an approach that brings together Advarra’s unique data and context, an architecture designed to adapt as technology changes, and strategic partners with specialized AI capabilities. That allows us to accelerate our product roadmap and bring meaningful innovation to market faster.”

    For Aditi, the shift also changes what engineering capability looks like. The company is developing engineers not simply to use AI tools, but to combine them with technical fundamentals, business context, judgment and accountability in real delivery environments. Engineers from those cohorts are already working on live client engagements.

    “AI is changing what organizations can build and how they build it, but technology alone doesn’t create an outcome,” said Raja Narayana, CEO of Aditi Consulting. “The opportunity is to use AI to create greater leverage while keeping human judgment and accountability where they matter most. That’s the model we’re building inside Aditi and bringing to our clients.”

    For more information about Aditi Consulting’s work with Anthropic and Claude, visit Aditi’s Anthropic partnership page.

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    About Aditi Consulting

    Aditi Consulting is a global technology partner helping enterprises transform with AI by combining outcome engineering with the capacity to execute. The company brings together AI, engineering expertise and global engineering talent to meet customers where they are—whether they need a partner to engineer the path to an outcome or the specialized skills and scale to execute a strategy already in place. From modernizing what exists to building what’s next, Aditi helps organizations turn what’s possible into measurable impact, while amplifying what people do best. Building a Digital World with Human Spirit.

    Learn more: https://www.aditiconsulting.com/

    Media Contact

    Stevi Rex
    stevi.rex@aditiconsulting.com
    +1 805.368.9024

  • Gremlin Launches Foresight AI to Proactively Fix Reliability Risks

    Gremlin Launches Foresight AI to Proactively Fix Reliability Risks

    The pioneers of Chaos Engineering built Foresight AI on top of a decade of real-world data around how systems fail and recover. Gremlin Foresight AI identifies and fixes reliability concerns before they become incidents, empowering enterprises to move confidently at AI speed.

    DENVER, CO — October 7, 2026 — Gremlin, the company founded by Amazon and Netflix engineers that pioneered Chaos Engineering, today announced the general

    availability of Gremlin Foresight AI. Following a successful beta, Gremlin Foresight AI has proven it can identify and address reliability risks before they become incidents, helping engineering teams move at AI speed without compromising the resilience of mission-critical production systems.

    “AI-driven development means shipping code at 10X velocity; it also means 10x the opportunity for bugs, risks, and failures,” said Kolton Andrus, CEO and Founder of Gremlin. “While the rise of AI SRE tools are great for helping teams respond to incidents faster, it’s still cleanup after something breaks. Gremlin Foresight AI finds risks proactively, delivers the fix, and verifies the fix worked by rerunning the test. This provides teams with the assurances needed to move confidently.”

    Gremlin Foresight AI is built on top of the company’s proprietary Failure Atlas, which incorporates more than a decade of cause-and-effect data around how online systems fail. This specialized foundation means every recommendation is grounded in real-world failure patterns and operational experience, not generic best practices. Every fix is validated against the test that surfaced the risk, closing the loop between identifying a weakness and proving it’s resolved.

    Key features of Gremlin Foresight AI include:

    • Proactive risk detection: Identify potential weaknesses and failure conditions before they become incidents.
    • Guided remediation: Recommend and deliver the specific fix as a configuration patch or infrastructure-as-code change.
    • Continuous validation: Re-run the originating test to confirm each fix performs as intended, and repeat testing as systems change.
    • Measurable resilience: Track reliability scores across services and teams to quantify improvement and prioritize further investment.

    Before founding Gremlin, Kolton Andrus was responsible for the uptime of Amazon’s retail site. He then joined Netflix to build the company’s second generation of fault-injection tooling, following the popularity of their open source project Chaos Monkey. Gremlin has since pushed the discipline of Chaos Engineering beyond one-off fault-injection experiments into a comprehensive approach to world-class reliability management.

    The Gremlin platform enables teams to run planned experiments, control the blast radius, and use continuous automated testing to verify that fixes remain effective over time. Reliability scores make that progress measurable across the whole organization, giving every team a shared standard to work toward, as well as giving leaders the visibility to direct investment and hold teams accountable.

    “Gremlin has always tried to help engineering teams improve the resiliency of their applications by running experiments proactively to identify weaknesses in their distributed systems, but many teams didn’t have the time or expertise to do that consistently,” said Mike Dauber, General Partner at Amplify Partners. “Foresight AI is like a trainer who does the reps for you. Your systems get stronger without your team doing all the manual work.”

    Gremlin Foresight AI extends their proactive philosophy into today’s AI-driven world, delivering agentic resilience by automatically identifying reliability risks earlier and automating fixes where appropriate to avoid outages, degraded services, and negative customer impact. Gremlin Foresight AI is generally available starting today. To learn more or request a demo, visit gremlin.ai.

    About Gremlin

    Gremlin is the reliability management platform trusted by the world’s largest enterprises to build resilience across everything they ship and run. Gremlin combines failure testing, passive risk detection, dependency mapping, and agentic resilience to give engineering teams the predictive data they need to systematically measure, manage, and improve their reliability. Learn more at gremlin.com.

    Media Contact

    Adam LaGreca

    10KMedia

    adam@10kmedia.co

  • Signadot Launches Plans, Bringing Governed, Inner-Loop Validation to Coding Agents Working on Microservices

    New release expands Signadot’s agent-native platform for building and validating microservices on Kubernetes.

    SAN FRANCISCO, June 4, 2026 — Signadot, the platform for building and validating microservices on Kubernetes, today announced the beta release of Signadot Plans, a new product layer that gives coding agents a structured, governed way to validate every code change against real dependencies in the agent’s inner loop.

    As coding agents take on more of the development workflow, the bottleneck has shifted from writing code to verifying it. Agents can produce changes faster than any team can review them, but in distributed systems, bugs live at the seams between services, where mocks and unit tests cannot catch them. Plans closes that gap by giving agents a reusable validation vocabulary that runs against the real cluster, fast enough to fit inside the agent’s iteration loop.

    “Coding agents need to verify their own work before handing it off, and they need to do it against the real system, not a mock,” said Anirudh Ramanathan, CTO of Signadot. “Plans gives them the structured, governed primitives to do that inside their work session, on every change.”

    Plans is built on three layers:

    • Actions are typed, platform-governed primitives that define what kinds of validation are allowed against the cluster. The catalog ships with support for Playwright, k6, and HTTP request checks, and is open to community contributions.
    • Plans wrap actions into small, reusable validation workflows scoped to specific behaviors, tagged with selection hints so coding agents can find and reuse them across sessions.
    • Skills are the agent-facing interface for authoring and running plans, surfaced inside the agent’s session as it iterates.

    The release reinforces the division of responsibility that has defined Signadot’s platform: platform teams own the governed primitives, while developers and their coding agents compose validation workflows on top. This keeps the surface area agents can reach bounded to what the platform team has approved, while still giving agents the autonomy to define the validation flows they need for the change at hand.

    Signadot Plans is available in beta today for all Signadot users. Full details, including a worked example and a quickstart guide, are available in the launch announcement.

    About Signadot

    Signadot is the agent-native platform for building and validating microservices on Kubernetes. Signadot gives engineering teams lightweight, ephemeral environments and the validation primitives coding agents need to verify their work against real dependencies, before code reaches review. Customers include leading engineering organizations like Brex, Miro, Bitso, and Wealthsimple. Learn more at signadot.com.

    Media Contact

    Tristan Willis

    tristan@signadot.com

  • Testkube Redefines Testing for the AI Era with Testkube AI, While Deepening Its Commitment to Open Source and Enterprise Teams

    New AI capabilities at the core, a new viewer for open source users, and a new path for enterprise procurement.

    Boston, MA, May 7, 2026 — Testkube, the open testing platform for AI-driven engineering teams, today announced three simultaneous milestones: the public launch of Testkube AI, the launch of a Test Execution Viewer for open source users, and availability on the AWS Marketplace. Together, the announcements reflect Testkube’s commitment to serving engineering teams at every stage.

    “AI is dramatically accelerating how quickly organizations can ship code, but it’s also raising the stakes on quality”, said Andy Pemberton, President at Testkube. “Teams come to Testkube because they refuse to choose between velocity and confidence – they want both. They also want a path to get there. Today’s announcements are about making sure companies have a path to get the outcomes they’re after regardless of where they are in their testing journey!”

    A new era of AI-native testing

    Testkube AI is deeply woven into the fabric of the Testkube platform, designed to make testing an active, intelligent part of the software development lifecycle rather than a passive step in a CI/CD pipeline. It gives AI agents native access to the test workflows, logs, artifacts, and execution context that shape every testing outcome.

    The distinction matters. Most AI tools that touch testing today live outside of the software testing lifecycle, interpreting results after the fact through whatever partial data they can reach. Testkube AI is built into the testing platform itself, so agents see what the test runner sees, in real time, with the same fidelity as the engineers who built the tests. That changes what AI can do, from summarizing outcomes to actually participating in the work. 

    It also changes what engineers can create. With Testkube AI, anyone can create a test for any framework, and run it immediately in their infrastructure. Most teams today struggle with test coverage, because writing, maintaining, and running tests at scale takes time most teams don’t have. Testkube AI closes that gap on both ends: lowering the barrier to creating tests, and making sure those tests run against real infrastructure, real data, and real applications, where the answers actually live.

    “Testkube is a core part of our testing orchestration. It lets our developers build fast, with powerful checks in CI/CD pipelines”. Said Valerii Timofeev, Staff QA Automation Engineer at AlphaSense. “With Testkube AI Agents, we enable automated failure analysis and troubleshooting, so we spend less time debugging and more time shipping.”

    Inside Testkube AI

    At launch, Testkube AI includes capabilities that show what this foundation makes possible. More will follow.

    AI Authoring — Engineers describe what they need to test in natural language, select any type of testing framework, provide additional context, and Testkube generates the test for immediate execution in their existing infrastructure. AI Authoring is being announced today. Availability details will follow.

    Autonomous Agents — The Testkube platform provides the deep context required for AI agents to analyze executions, open PRs, classify failures, optimize cost, and even generate new Test Workflows – both autonomously or manually when required.Testkube AI Agents plug directly into testing pipelines to eliminate manual triage, flaky-test hunting, and incident correlation. 

    Testkube MCP Server — Testkube now exposes a Model Context Protocol (MCP) server, making test execution and test results — workflows, results, logs, and artifacts — available to any AI tool or agent that supports MCP. Engineering teams can connect Testkube to existing AI-powered development tools and workflows without custom integrations.

    “There’s a difference between adding AI to a product and building a product that AI can fully see,” said Ole Lensmar, CTO at Testkube. “Most AI tools that touch testing today are working with whatever data they can scrape from outside the execution layer. Testkube AI works with the same fidelity our platform has, every workflow, every artifact, every log, in real time. That’s what makes the agents so powerful – they know everything about your tests, tools and results.”

    The Testkube AI Early Access Program is open today. Engineering teams can register now.

    Investments in open source and enterprise teams

    Alongside Testkube AI, Testkube is making two additional moves to support different parts of its user base.

    Open source users have always been at the heart of Testkube, and until today they’ve been running tests without a user interface to see what’s happening. For them, Testkube is now launching the Test Execution Viewer, an on-demand user interface that aggregates logs, results, and artifacts for any test execution, so teams can debug failures faster and stop piecing together context from the CLI. Available today at no cost. No login or account required. Usage instructions are in the Testkube documentation.

    For enterprise teams, Testkube is now available on the AWS Marketplace. Engineering organizations standardized on AWS can procure the platform through the channel they already use, consolidate spend under existing agreements, apply committed spend toward Testkube, and skip new vendor onboarding entirely. Deployment continues to run through Testkube’s standard install path into the customer’s Kubernetes environment.

    Together, these announcements reflect a broader investment in meeting different parts of Testkube’s user base where they are.

    About Testkube

    Testkube is the open testing platform for AI-driven engineering teams. Built for cloud-native teams, Testkube decouples test execution from CI/CD pipelines and runs tests as jobs inside your own infrastructure — with full visibility, any type of testing tool, and now AI built in. Learn more at testkube.io

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    Media Contact:
    Lucio Daza
    Head of Product Marketing
    press@testkube.io
    646-262-6252
    www.testkube.io

  • DBmaestro Introduces Agentic Database DevOps with MCP Server

    Transforming Natural Language into Governed Platform Workflows

    Orlando, FL, April 7, 2026 – DBmaestro, the leading database DevSecOps platform, today announced the launch of its Model Context Protocol (MCP) server, making DBmaestro the first database DevOps platform purpose-built for agentic AI workflows.

    The DBmaestro MCP server exposes the company’s industry-leading database release automation, source control, CI/CD orchestration, and compliance capabilities to AI agents and enterprise copilots. Teams can now use natural language to trigger real DBmaestro workflows – automating project creation, multi-environment deployments, package management, and release orchestration – while preserving enterprise governance, role-based access control, and full auditability.

    “DBmaestro MCP turns our enterprise-grade database DevOps platform into an agentic operational layer for AI,” said Gil Nizri, CEO of DBmaestro. “DBAs and DevOps engineers can now interact in natural language to accelerate repetitive tasks, while AI becomes the interface to deterministic, governed workflows. This is not replacing database expertise – it’s amplifying it with enterprise-grade control.”

    Key capabilities include:

    • Agentic Database DevOps: AI agents execute complex database operations across dev, QA, and production environments
    • Natural Language Workflows: Prompts such as “Create an MsSQL release pipeline with Dev/QA/Prod environments, and update Dev and QA to the latest version” become real platform execution
    • Enterprise Governance: Role-based permissions, compliance tracking, and full audit trails remain intact
    • Deterministic Reliability: AI accelerates setup and self-service while engineers retain control over critical paths

    As IBM’s strategic OEM partner for database release automation, DBmaestro MCP extends this leadership into the agentic era, enabling organizations to scale database delivery while maintaining the security and compliance standards enterprises demand.

    “Every enterprise adopting AI agents needs secure, governed access to their core platforms,” said Yaniv Yehuda, DBmaestro Founder & CPO. “DBmaestro’s MCP implementation shows how database DevOps can evolve from manual processes to agent-driven automation without sacrificing control.”

    The DBmaestro MCP server is available immediately to all customers and partners. For more information, visit https://www.dbmaestro.com/ai/dbmaestro-mcp-server  

    About DBmaestro

    DBmaestro is a leading provider of Agent-Governed database DevOps and release automation, enabling CI/CD for databases with controlled, auditable change management. The platform extends DevOps to the database layer with built-in compliance, observability, and governed AI execution, including MCP-based workflows for natural-language operations and intelligent troubleshooting under enterprise control. As IBM’s strategic OEM partner, DBmaestro powers database release automation in some of the world’s most complex and regulated environments and augments DevOps, automation, and governance solutions. Learn more at www.dbmaestro.com. 

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    Media contact
    Ben Gross
    +972-50-8452086
    beng@dbmaestro.com

  • SerpApi Files Suit Against SearchApi for Stealing Proprietary Technology

    AUSTIN, Texas, Feb. 20, 2026 — SerpApi filed suit in U.S. District Court for the Western District of Texas against Zilvinas Kucinskas and his company, SearchApi, for systematically stealing SerpApi’s proprietary technology and using its trade secrets to build a product that directly copies and competes with SerpApi.

    As detailed in SerpApi’s complaint, Mr. Kucinskas, a former software engineering contractor for SerpApi, exploited his access to SerpApi’s proprietary information to pilfer the company’s intellectual property. The evidence is damning: Mr. Kucinskas improperly retained SerpApi’s source code, and he repeatedly accessed SerpApi servers after stopping work for SerpApi in 2021. Even more egregiously, just 11 days before leaving SerpApi, he downloaded SerpApi’s proprietary customer list. Mr. Kucinskas not only stole all this information, he used it to start a copycat company, SearchApi.

    “Mr. Kucinskas tried to cover his tracks, but we uncovered his scheme. When confronted, he stonewalled our investigation and feigned ignorance. He refused to cooperate despite multiple attempts to resolve this matter directly. His conduct left us no choice but to file suit. We’ve only touched on the facts at a high-level here. Our complaint lays out the evidence in detail, supported by a forensic expert’s sworn declaration,” said Chad Anson, SerpApi’s general counsel.

    SerpApi has invested tens of millions of dollars and nearly a decade of work building a solution that gives its customers structured access to public data at enterprise scale. Mr. Kucinskas spent a few months stealing it. SerpApi filed this suit to protect that investment, to protect its customers, and to hold him accountable. This action is also part of a broader commitment to reinforcing the security and trust SerpApi’s customers rely on.

    In the interest of transparency, SerpApi is making the full complaint, the forensic expert’s declaration, and all supporting exhibits available below:

    About SerpApi

    SerpApi is the leading platform for search data, offering API solutions to parse data from Google, Bing, YouTube, and more. Founded in 2017, SerpApi was among the first platforms to provide developers and enterprises worldwide with access to real-time search data. With more than 100 specialized APIs, SerpApi empowers developers, researchers, innovators, and organizations to harness live search data to support their products and missions. Use cases span AI models, SEO, cybersecurity, E-commerce competitor research, and more.

    Media Contact: press@serpapi.com

  • Signadot Unveils Kubernetes-Native Developer Platform to Scale Agentic Development

    As AI agents generate 10x more code, Signadot eliminates the validation bottleneck with high-concurrency, Kubernetes-native ephemeral environments.

    SAN FRANCISCO — Signadot today announced its evolution into the Kubernetes-native developer platform for the agentic software development lifecycle (SDLC), marking a strategic shift to meet the demands of modern, high-velocity engineering teams. Building on its leadership in high-fidelity local development and ephemeral environments, Signadot is introducing a platform designed to solve the primary challenge of the AI era: the validation bottleneck.

    As engineering teams adopt coding agents to accelerate velocity, the industry is witnessing a fundamental shift in the SDLC. While agents have accelerated code generation, the bottleneck has moved downstream to verification. Traditional staging environments and CI/CD pipelines were not built to handle this level of volume and concurrency, leading to resource contention, long queues, and soaring cloud costs that negate the velocity gains of AI.

    The true potential of AI coding agents isn’t just about writing code faster. It’s about verifying and merging it faster,” said Arjun Iyer, CEO and Co-founder of Signadot. “Most teams have agents perform basic unit or mocked tests, but this isn’t sufficient for complex cloud-native software. Without testing against real cluster dependencies, integration issues surface much later in the cycle and eliminate the productivity gains.”

    Signadot bridges this gap by providing the high-performance infrastructure necessary to scale the agentic SDLC. Using Signadot, engineering teams can spin up lightweight, ephemeral environments at scale. This enables hundreds of concurrent developers and coding agents to write, run, and verify code in parallel loops without contention or the prohibitive costs of duplicating full clusters.

    Powering Agentic Development at Scale

    Signadot is the ephemeral environment platform of choice for high-velocity engineering organizations that demand efficient scaling for their internal agentic development workflows and customer-facing agents. This includes teams at Brex, Wealthsimple, and Bitso. 

    The platform team at DoorDash (DASH) uses Signadot to accelerate productivity for their developers and coding agents by bringing production fidelity into their local environments. 

    “Signadot allows us to fast-track development by working locally rather than waiting for changes to be deployed to our staging environment. This includes our agents, that need the same access our engineers do.” – Adam Rogal, Eng. Director of Developer Platforms @ DoorDash  

    For more information about Signadot’s Kubernetes-native platform for the agentic SDLC, visit www.signadot.com.

    About Signadot

    Signadot is a Kubernetes-native developer platform that accelerates the software development lifecycle for high-velocity teams. With on-demand lightweight ephemeral environments that connect to real cluster dependencies, Signadot enables developers and agents to iterate locally with high fidelity and validate PRs instantly with massive concurrency and zero contention. This eliminates the infrastructure bottlenecks of traditional CI/CD and equips modern engineering teams for agentic development at scale.

  • Techstrong Group Opens Nominations for the DevOps Dozen 2025 Awards

    Techstrong Group Opens Nominations for the DevOps Dozen 2025 Awards

    Nominations are open through October 17 for the DevOps Dozen 2025 Awards, honoring outstanding achievements and contributions in the DevOps community.

    Boca Raton, FL, September 8, 2025 — Techstrong Group, the power source for people and technology, today announced that nominations are open for the DevOps Dozen 2025 Awards, celebrating the individuals, organizations, tools, and services driving excellence in DevOps. The program features 24 categories across Community Awards (9) and Tools & Services Awards (15).

    “The DevOps Dozen are not about trophies or vanity. They are about momentum and validation. These awards honor the people, practices, and products that shaped the community and moved it forward over the past year,” said Alan Shimel, Founder and CEO, Techstrong Group.

    This year, Agentic AI and Generative AI each get their own category, while Platform Engineering has a dedicated slot as the practice matures from trend to table stakes. Supply Chain Security Solution also earns standalone recognition, reflecting how software integrity, attestations and provenance have shifted from ‘nice to have’ to ‘non-negotiable.’

    Important Dates

    • Nominations close: October 17, 2025
    • Finalists announced and public voting opens: November 10, 2025
    • Public voting closes: December 31, 2025
    • Winners announced: January 2026 at the Predict 2026 Virtual Summit

    Awards Process and Eligibility

    There are no nomination fees in the Community Awards category, though commercial vendors are not eligible for the following three: Top DevOps Community Evangelist, DevOps Enterprise Leader of the Year, and Best Application of AI in Enterprise DevOps Practices. Tools & Services Award nominations carry a $300 fee to ensure serious entries and discourage spam. Fees are refunded for those not advancing to the finals. Vendors may nominate themselves.

    Public voting will account for 40% of each winner’s total, while judges’ scores will account for 60%. There is a limit for finalists at 10 per Tools & Services category to keep the field competitive and focused.

    DevOps Dozen 2025 Awards Categories

    Tools & Services Awards

    1. Best DevOps Industry Implementation
      Many organizations have transformed their business by successfully adopting and implementing DevOps principles, practices and solutions. This award recognizes those enterprises that have successfully achieved DevOps success and have worked with their solution partner to do so.
    2. DevOps Industry Leader of the Year
      Tool & Service providers play a critical role in bringing innovative tools, platforms and services to the market. This award recognizes a strong Tools & Services leader who guides their company through the challenges of developing and bringing to market innovative DevOps technologies and services.
    3. Top DevOps Vendor Evangelist
      DevOps solution providers typically are on the leading edge of new DevOps strategies, tools and practices. This award recognizes the individual that does the best job of informing the DevOps community about new and exciting tools and practices to deliver DevOps success and business value.
    4. Best End-to-End DevOps Platform
      Consolidation in the DevOps space has led to a greater selection of platforms offering end-to-end solutions with increasing feature sets. This award honors the platform with the most diverse and robust solution for DevOps teams.
    5. Best application of Generative AI in a DevOps Tool/Platform (Updated for 2025)
      Generative AI is transforming DevOps by automating tasks, improving code quality, and enhancing efficiency. It leverages AI models to generate code, automate deployments, and streamline workflows, ultimately leading to faster development cycles and more robust software. This award honors the best use of Generative AI in a DevOps Tool or Platform.
    6. Best application of Agentic AI in a DevOps Tool/Platform (Added for 2025)
      Agentic AI refers to autonomous AI capabilities in a DevOps tool or service that can plan, reason, and act to complete DevOps tasks with minimal human oversight. This involves perception, decision-making, and execution, creating a value stream lifecycle where the AI agent interacts with the DevOps environment. This award honors the best use of Agentic AI in a DevOps Tool or Platform.
    7. Best DevSecOps Solution
      This award honors the best DevSecOps solution, which enables security to be included earlier and continuously throughout the SDLC.
    8. Best Supply Chain Security Solution (Added for 2025)
      Software supply chain security refers to the measures taken to protect the integrity and security of software throughout its entire development and delivery lifecycle, from initial code to final deployment. This award honors the best solution for ensuring that the software is trustworthy and hasn’t been tampered with at any point in its journey.
    9. Best Observability Solution
      This award recognizes the best observability solution for observing and monitoring to understand the performance of individual components, systems and entire applications.
    10. Best Platform Engineering Solution
      This award recognizes the best platform engineering solution enabling self-service capabilities for software engineering organizations in the cloud-native era, covering the operational necessities of the entire lifecycle of an application.
    11. Best Testing Service/Tool
      Testing is a mainstay in the DevOps toolchain, and this award recognizes the best testing tool for ensuring the quality of code.
    12. Best DevOps for Mainframe Solution
      Mainframes are far from being the dinosaur of the IT world. This award recognizes the best solution for implementing DevOps practices in mainframe environments and hybrid environments.
    13. Best DevOps for DataOps/Database Solution
      Databases often have been left out of the DevOps conversation, but that is changing. This award recognizes the best database related solution for DevOps and DataOps.
    14. Best New DevOps Tool
      This award recognizes the best tool that has brought innovation to the DevOps space in the past year (less than 24 months old).
    15. Best New DevOps Service Provider
      A DevOps services provider helps organizations implement and manage DevOps practices through consulting, implementation, and ongoing support. Typically they offer a range of services, including DevOps consulting, CI/CD pipeline implementation, infrastructure automation, and DevSecOps services, to streamline software development and delivery. This award recognizes a service provider that excels in delivering quality services to the DevOps community.

    Community Awards

    1. Best DevOps-Related Video or Audio Podcast
      Podcasts are popular ways for DevOps leaders and practitioners to keep current. This award recognizes the most educational, informative and inspirational DevOps video or audio podcast series — those with the most engaging and highest-quality content that keeps listeners coming back for more.
    2. Best devopsy.kinsta.cloud Article of the Year
      devopsy.kinsta.cloud publishes multiple new articles every day. This award recognizes the best article from this year. Let us know so we can recognize the author.
    3. Best DevOps Book of the Year
      Each year sees a number of well-crafted DevOps-related books. This category recognizes the most popular and best read of the year.
    4. Top DevOps Community Evangelist (No vendor nominees)
      Every community has those who stand out as leaders in their field who spread the DevOps gospel every day. This award recognizes the individual whom peers in the DevOps community recognize for outstanding leadership, knowledge, and communication.
    5. DevOps Enterprise Leader of the Year (No vendor nominees)
      An organization’s leadership plays a critical role in shepherding its team through innovation and change. This award recognizes a strong DevOps leader who guides their team through the challenges of developing and implementing DevOps strategies, policies, culture, and practices to improve software development and delivery.
    6. Best DevOps Survey/ Research/Report (Updated for 2025)
      Ongoing research in the application of DevOps tracks trends and provides standards that organizations can use to measure themselves. This award recognizes industry research, reporting and analysis that has significantly and positively impacted the DevOps community.
    7. Best DevOps Event of the Year
      Events and conferences are an important part of IT life. This category includes both in-person and virtual events. Which DevOps event stands out from the others and delivers the best content, speakers and attendee experience?
    8. Most Innovative DevOps Open Source Project
      Open source software is an important part of every organization’s set of development assets promoting innovation, velocity, quality and efficiency. This award recognizes the open source project that delivers the greatest value through a community of people who work on and with the open source technology and support each other.
    9. Best Application of AI in Enterprise DevOps Practices (No vendor nominees)
      Leading enterprises are integrating the use of AI within their DevOps practices at an incredible pace. This award recognizes the organization that has applied AI in the most interesting and productive way to accelerate and improve various aspects of software delivery, leading to increased efficiency, higher quality, and faster time-to-market.

    Finalists receive badges and recognition across the Techstrong network; winners receive trophies and the full spotlight they’ve earned.

    For more information about award categories and nomination forms, please visit the DevOps Dozen website. For questions regarding entry process, timing, or technical issues, please contact us at DD@devops.com.

    About Techstrong Group

    Techstrong Group is the power source for people and technology. Techstrong Group accelerates understanding of technologies that drive business. With a broad set of IT-related communities and offerings, Techstrong Group is the only media company serving the needs of IT leaders and practitioners with news, research, events, education, and professional development. Our focus is DevOps, cybersecurity, AI, digital transformation, cloud and cloud native. Techstrong Group business units include: Techstrong Media (devopsy.kinsta.cloud, Security Boulevard, Cloud Native Now, Techstrong.ai, DigitalCxO, PlatformEngineering.com, Techstrong.IT, Techstrong TV and Techstrong Group Podcasts), and Techstrong Learning. For technology providers, Techstrong business units offer many ways to share their stories, expand their audience and grow their business.

    Techstrong is part of Futurum Group, a leading technology research and advisory firm, expanding its capabilities in research, advisory, and media.

    Techstrong Group is based in Boca Raton, Florida. For more information, visit our website and follow us on LinkedIn.

    ###
    Veronica Haggar
    veronica@techstronggroup.com

  • Free Tiers and Open Source LLMs – Mana for Developers, Platform Engineers and QA

    Free Tiers and Open Source LLMs – Mana for Developers, Platform Engineers and QA

    Development rarely follows one straight path. You sketch ideas, prototype, test, swap tools, iterate, and repeat. The increasing availability of free, limited-use AI tiers and locally run open-source AI LLMs is accelerating that loop. These tiers are not marketing fluff. They are practical on-ramps for developers and engineers. They offer the freedom to test, compare, and refine without upfront cost.

    Free Tiers For The Taking

    GitHub provides a free Copilot tier: up to 2,000 code completions per month, plus 50 premium requests (chat / “agent mode”) interactions. Available in VS Code, Visual Studio, JetBrains IDEs, and more. It includes access to Claude 3.5 Sonnet and GPT-4o models. (https://github.com/features/copilot/plans, https://docs.github.com/copilot/concepts/copilot-billing/about-individual-copilot-plans-and-benefits)

    Anthropic’s Claude is free to use on the web, iOS, Android, and desktop with usage limits. Its API is not free. (https://www.anthropic.com/pricing, https://docs.anthropic.com)

    OpenAI’s GPT-5 is available to free ChatGPT users with strict usage caps. Its API requires prepaid credits, minimum $5. (https://www.wired.com/story/openais-gpt-5-is-here, https://help.openai.com/en/articles/8264644-how-can-i-set-up-prepaid-billing)

    New AWS accounts get up to $200 in credits valid for six months. Generative AI services like Bedrock move to pay-as-you-go after that. (https://aws.amazon.com/about-aws/whats-new/2025/07/aws-free-tier-credits-month-free-plan)

    Google Cloud AI Studio and Firebase are free to access. The Gemini API has a free tier with lower rate limits; paid tiers offer higher capacity. (https://ai.google.dev/gemini-api/docs/pricing)

    Cursor offers a free plan with usage limits. Windsurf provides monthly prompt credits across free and paid plans. (https://cursor.com/pricing, https://docs.windsurf.com/windsurf/accounts/usage)

    Why This Matters to Developers

    Flexible Model Switching: You can test multiple models (GPT-4o, Claude) without changing workflow or integration. That removes friction from choosing the best fit for your task.

    Rapid Prototyping: No billing setup. You can try ideas, logic paths, and prompt styles immediately.

    Cost-Effective Scaling: Validate with free tiers. When it works, choose paid tiers intentionally, not by guesswork.

    Hybrid Model Approaches: Use GPT-5 or o1 for reasoning tasks, then cheaper models like Llama for bulk processing. Free access to each lets you refine that pipeline.

    AI in Your DevOps Pipeline

    Free models pair neatly with toolchains:

    • GitHub Actions could use a free model to auto-generate release notes, analyze test failures, or lint code.

    • Microsoft Azure DevOps can use GitHub Copilot Chat and Azure OpenAI Service to assist in pipeline creation, YAML authoring, and automated documentation generation.

    • Google Cloud Build can use Gemini to generate deployment docs or config templates.

    • AWS CodePipeline could incorporate Bedrock calls for compliance checks or IaC validation.

    • CLI tools across platforms can invoke these models for tasks like refactoring YAML, generating manifests, or annotating dashboards.

    These are just a few examples of how these tools can be used with AI models. Running these in staging pipelines gives you low-risk experimentation before you roll out to production.

    Go Local

    Open-source and locally run LLMs such as OpenAI’s GPT-OSS, Meta’s Llama 3, and Mistral’s Mixtral are giving developers new options for building AI-driven applications without relying entirely on cloud APIs. These models can be downloaded, hosted on personal hardware, or deployed within private infrastructure, allowing full control over data, latency, and customization.

    Running models locally means teams can experiment with fine-tuning for domain-specific tasks, integrate AI into environments with limited internet connectivity, and meet strict compliance or privacy requirements.

    While local models may not match the scale or capability of their largest cloud-based counterparts, they offer a powerful balance of cost efficiency, adaptability, and independence that can be especially valuable for development, testing, and edge computing scenarios.

    Who Else Should Care

    It’s not just about developers: Test Engineers can generate synthetic test data, edge-case inputs, and test scripts. DevOps Engineers can automate pipeline scaffolding, log review, and incident summaries. Platform Engineers can prototype developer automation, self-service templates, and internal documentation.

    Getting early hands-on experience helps you understand where AI adds value and where it doesn’t.

    If you code, automate, build pipelines, test, or run platforms, start exploring these free AI tiers today. Plug models into your editor, Actions workflows, test frameworks, and CLI tools. Use them on real tasks, not just sample prompts. Track what works, what stumbles.

    Experimentation costs almost nothing. Delay could cost your team time and innovation.

    Recent articles by Mitch Ashley:
    Kubernetes, AI, APIs, and YAML – A Future 2.0?
    We’re Not Being Replaced. We’re Inventing What Comes Next, Including Ourselves
    AI and You: Don’t Wait… Or Be Weight

     

    Mitch Ashley is VP and Practice Lead of Software Lifecycle Engineering at The Futurum Group. The voice of “AI across the SDLC”, Mitch is a serial-CTO, speaker, advisor, entrepreneur, and product creator. He leads analyst coverage of the Software Development Cycle (SDLC), with emphasis on AI-native and agent development, cloud-native, DevOps, platform engineering, and software security.

    See Mitch’s analyst research on the Futurum website.

    Subscribers can access Mitch’s Software Engineering Lifecycle practice, decision-maker data, insight reports, and advisories through the Futurum Intelligence Platform.