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VMware Avi Load Balancer Introduces New Features

VMware's Avi Load Balancer, a software-defined load balancer (LB) for hybrid clouds that delivers a distributed architecture with built-in automation and deep application visibility, has added the following new features:

VCF Private Cloud Applications: Plug-and-play operating model for VCF, delivering LB self-service through integration with Aria Automation, formerly vRealize Automation (vRA).

Kubernetes Applications: As an ingress LB, Avi’s software defined and elastic architecture is suited for the distributed and dynamic nature of container workloads. Avi’s advantages include:

- Gateway API is a next gen Kubernetes ingress, significantly reducing the need for customizations via annotations and custom resource definitions (CRDs), and also future proofs customers for Kubernetes and serverless workloads.

Mobile and 5G Applications: Avi delivers a flexible and consistent load balancing solution for a broad set of telecom and mobile use cases:

- Natively multi-tenant, now delivering ~3X higher multi-tenancy with VCF and Telco Cloud Platforms (TCP).

- Full support for recently shipping TCP 4.0.

- Further enhancements to software-defined IPv6 load balancer (with decoupled IPv6 control and distributed data planes) for on-prem and cloud.

Legacy LB to Avi Conversion Tool: To simplify and speed-up Avi deployments in brownfield environments, we are announcing the Avi Conversion Tool. The tool:
- discovers legacy LBs
- extract configurations
- converts legacy LB configurations and custom rules (e.g. iRules) to Avi
- deploys configurations on AviThis tool will accelerate legacy-to-Avi migrations delivered by our professional services team.

Self-Service Load Balancing for VCF Private Cloud: Avi Load Balancer is a load balancer for VCF, fully integrated and supported with plug-and-play ease of use. Avi provides application visibility to help a VCF admin rapidly troubleshoot and root cause application performance issues within minutes. Customers who want to enable self-service capabilities to DevOps teams find legacy load balancers unbearable as it takes weeks to provision, requiring many manual steps and multiple tickets. Avi’s integration with Aria Automation offers application teams self-service access to L4-L7 load balancing services (see blog: Enabling Load Balancing as a Service for VCF-based Private Cloud). This enables application and infrastructure teams to immediately deploy load balancing at the time of application provisioning, with minimal know-how of load balancing technology or the need to create manual tickets. The integration helps customers lower operational cost, simplify and automate the provisioning and capacity management of load balancing. Watch this short demo video to see how easy it is to enable load balancing as a service using Aria Automation.

Boost Avi Load Balancer Multi-tenancy by ~3X: Avi Load Balancer has made significant improvements on performance and scale, boosting multi-tenancy support by nearly 3X. Customers can now manage more Avi Service Engines (load balancers) for every Avi Controller deployed. Each controller can support up to 800 Tier-1 routers for VMware NSX-T Cloud. Customers not only have fewer controllers to manage, but save significant operational costs compared to legacy load balancers. As a result, the same team can manage more tenants, so productivity is enhanced. VMware Cloud Service Providers, formerly VMware Cloud Provider Program (VCPP), leverage Avi Load Balancer to streamline cloud operations and elevate service offerings.

Avi for Ingress Load Balancing of Kubernetes and Serverless Workloads: To keep pace with the speed of application deployment and deliver enterprise-grade load balancing and ingress solutions, Avi Load Balancer offers the ideal architecture for container workloads with built-in ingress security.

Avi offers integrated ingress services which include ingress controller, load balancing, multi-cluster global server load balancing (GSLB), WAF, application analytics in a single platform. For customers who have deployed Avi for their virtualized workloads, it’s an easy extension onto the Kubernetes workloads with the same user interface, consistent workflows and policies. Avi is agnostic to container platforms and supports VMware Tanzu, RedHat OpenShift, Tanzu Application Service (TAS, previously Pivotal Cloud Foundry) and more. Avi Load Balancer is introducing general availability (GA) support for Gateway API in the AKO 1.12.1 release. Avi adds advanced L7 routing functionalities including serverless Kubernetes support, header modification, cookie insertion, and a key set of HTTProute functionalities. Avi offers advanced traffic routing, better scale and observability on a per route basis, without the need for CustomResourceDefinition (CRDs) or annotations.

Extending Software-Defined Approach to Mobile and 5G Applications: With the distributed IPv6 support enabled by Avi’s unique software-defined architecture and Telco Cloud Platform 4.0 support, VMware Avi Load Balancer continues to offer a broad set of use cases from 4G VM workloads nad Virtual Network Functions (VNFs) to 5G container workloads and container network functions (CNFs). A large telco customer first deployed Avi in their corporate IT network for its cloud-like experience. Impressed by its ease of use and application analytics, the network team introduced Avi to its core telco team, who decided to extend Avi into the Telco Cloud Platform that enables 5G network for Kubernetes workloads. From an operations perspective, it’s exactly the same platform for both environments, making it extremely easy to train staff and collaborate on resolving application issues that can involve multiple teams. Multi-tenancy has been a key requirement for telco use cases and the scale has been greatly enhanced with the release. Avi provides granular rule-based access control (RBAC) to different tenants and teams.

Legacy Load Balancer to Avi Conversion Tool: To make it easier to migrate off legacy load balancer hardware appliances and accelerate the journey onto VCF private cloud, Avi Load Balancer is announcing the initial availability (IA) of an UI-based conversion tool that takes legacy policy rules, converts configurations into built-in Avi features as well as into Avi DataScript (a Lua-based scripting language). Customers break the chain off legacy load balancers, from convoluted box-by-box configurations to simple policy management from a centralized dashboard.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

VMware Avi Load Balancer Introduces New Features

VMware's Avi Load Balancer, a software-defined load balancer (LB) for hybrid clouds that delivers a distributed architecture with built-in automation and deep application visibility, has added the following new features:

VCF Private Cloud Applications: Plug-and-play operating model for VCF, delivering LB self-service through integration with Aria Automation, formerly vRealize Automation (vRA).

Kubernetes Applications: As an ingress LB, Avi’s software defined and elastic architecture is suited for the distributed and dynamic nature of container workloads. Avi’s advantages include:

- Gateway API is a next gen Kubernetes ingress, significantly reducing the need for customizations via annotations and custom resource definitions (CRDs), and also future proofs customers for Kubernetes and serverless workloads.

Mobile and 5G Applications: Avi delivers a flexible and consistent load balancing solution for a broad set of telecom and mobile use cases:

- Natively multi-tenant, now delivering ~3X higher multi-tenancy with VCF and Telco Cloud Platforms (TCP).

- Full support for recently shipping TCP 4.0.

- Further enhancements to software-defined IPv6 load balancer (with decoupled IPv6 control and distributed data planes) for on-prem and cloud.

Legacy LB to Avi Conversion Tool: To simplify and speed-up Avi deployments in brownfield environments, we are announcing the Avi Conversion Tool. The tool:
- discovers legacy LBs
- extract configurations
- converts legacy LB configurations and custom rules (e.g. iRules) to Avi
- deploys configurations on AviThis tool will accelerate legacy-to-Avi migrations delivered by our professional services team.

Self-Service Load Balancing for VCF Private Cloud: Avi Load Balancer is a load balancer for VCF, fully integrated and supported with plug-and-play ease of use. Avi provides application visibility to help a VCF admin rapidly troubleshoot and root cause application performance issues within minutes. Customers who want to enable self-service capabilities to DevOps teams find legacy load balancers unbearable as it takes weeks to provision, requiring many manual steps and multiple tickets. Avi’s integration with Aria Automation offers application teams self-service access to L4-L7 load balancing services (see blog: Enabling Load Balancing as a Service for VCF-based Private Cloud). This enables application and infrastructure teams to immediately deploy load balancing at the time of application provisioning, with minimal know-how of load balancing technology or the need to create manual tickets. The integration helps customers lower operational cost, simplify and automate the provisioning and capacity management of load balancing. Watch this short demo video to see how easy it is to enable load balancing as a service using Aria Automation.

Boost Avi Load Balancer Multi-tenancy by ~3X: Avi Load Balancer has made significant improvements on performance and scale, boosting multi-tenancy support by nearly 3X. Customers can now manage more Avi Service Engines (load balancers) for every Avi Controller deployed. Each controller can support up to 800 Tier-1 routers for VMware NSX-T Cloud. Customers not only have fewer controllers to manage, but save significant operational costs compared to legacy load balancers. As a result, the same team can manage more tenants, so productivity is enhanced. VMware Cloud Service Providers, formerly VMware Cloud Provider Program (VCPP), leverage Avi Load Balancer to streamline cloud operations and elevate service offerings.

Avi for Ingress Load Balancing of Kubernetes and Serverless Workloads: To keep pace with the speed of application deployment and deliver enterprise-grade load balancing and ingress solutions, Avi Load Balancer offers the ideal architecture for container workloads with built-in ingress security.

Avi offers integrated ingress services which include ingress controller, load balancing, multi-cluster global server load balancing (GSLB), WAF, application analytics in a single platform. For customers who have deployed Avi for their virtualized workloads, it’s an easy extension onto the Kubernetes workloads with the same user interface, consistent workflows and policies. Avi is agnostic to container platforms and supports VMware Tanzu, RedHat OpenShift, Tanzu Application Service (TAS, previously Pivotal Cloud Foundry) and more. Avi Load Balancer is introducing general availability (GA) support for Gateway API in the AKO 1.12.1 release. Avi adds advanced L7 routing functionalities including serverless Kubernetes support, header modification, cookie insertion, and a key set of HTTProute functionalities. Avi offers advanced traffic routing, better scale and observability on a per route basis, without the need for CustomResourceDefinition (CRDs) or annotations.

Extending Software-Defined Approach to Mobile and 5G Applications: With the distributed IPv6 support enabled by Avi’s unique software-defined architecture and Telco Cloud Platform 4.0 support, VMware Avi Load Balancer continues to offer a broad set of use cases from 4G VM workloads nad Virtual Network Functions (VNFs) to 5G container workloads and container network functions (CNFs). A large telco customer first deployed Avi in their corporate IT network for its cloud-like experience. Impressed by its ease of use and application analytics, the network team introduced Avi to its core telco team, who decided to extend Avi into the Telco Cloud Platform that enables 5G network for Kubernetes workloads. From an operations perspective, it’s exactly the same platform for both environments, making it extremely easy to train staff and collaborate on resolving application issues that can involve multiple teams. Multi-tenancy has been a key requirement for telco use cases and the scale has been greatly enhanced with the release. Avi provides granular rule-based access control (RBAC) to different tenants and teams.

Legacy Load Balancer to Avi Conversion Tool: To make it easier to migrate off legacy load balancer hardware appliances and accelerate the journey onto VCF private cloud, Avi Load Balancer is announcing the initial availability (IA) of an UI-based conversion tool that takes legacy policy rules, converts configurations into built-in Avi features as well as into Avi DataScript (a Lua-based scripting language). Customers break the chain off legacy load balancers, from convoluted box-by-box configurations to simple policy management from a centralized dashboard.

The Latest

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.