ManageEngine, a division of Zoho Corporation, the expansion of its cloud cost management platform, CloudSpend, by unveiling a multi-portal architecture for managed service providers (MSPs), cloud service providers (CSPs), and multi-tenant enterprises.
This latest capability delivers a comprehensive multi-portal experience, enabling organizations to address the burgeoning challenge of cloud cost management and scale their FinOps practices by bringing together unified cost visibility, secured segmentation, and contextual governance on a single, centralized interface.
As cloud adoption continues to surge, financial and operational complexity has grown exponentially for service providers and large enterprises. Gartner® projected worldwide end-user spending on public cloud services to top $723.4 billion in 2025, underscoring not only the continued acceleration of cloud adoption, but also calling to attention the urgent need for effective financial management.
"Service providers have long struggled to strike the right balance between visibility and isolation," said Srinivasa Raghavan, director of product management at ManageEngine. "Each client needs full transparency into their cloud spending, while MSPs and CSPs must uphold strict separation, governance, and compliance. Similarly, large multi-tenant enterprises face the same challenge of managing multiple business units or projects securely under one environment."
"CloudSpend's new multi-portal architecture bridges this gap by unifying visibility across tenants while enforcing data isolation and automated cost policies," he added. "It enables service providers and enterprises alike to manage cloud costs securely and efficiently at scale, helping them maximize profitability, ensure compliance, and deliver the transparency every stakeholder expects."
Solving the Multi-Tenant Challenge
Managing hundreds of clients or business units across multiple clouds often leads to fragmented reports, manual billing, and compliance risks. To address these challenges, CloudSpend delivers a unified set of new features built for secure, scalable, and efficient multi-tenant cost management.
Key capabilities include:
- Dedicated portals with unified governance: Each portal runs in a secure, independent environment with its own budgets, alerts, thresholds, and workflows, while admins retain centralized visibility and policy control through granular roles and access rules that uphold data boundaries.
- Intelligent cost saving recommendations: The platform automatically identifies underused resources, recommends rightsizing actions, and pinpoints savings opportunities—reducing cloud waste, improving efficiency, and maximizing ROI without manual audits.
- Simplified management and white labeling: Users can manage all portals from a single command center, with automated billing, chargeback, and branded reporting. White labeling ensures the entire experience—from dashboards to reports—reflects the customer's brand identity.
- AI-driven anomaly detection and forecasting: Users can leverage historical data to predict future spending trends, detect anomalies in real time, and gain actionable insights that enhance budgeting accuracy, improve cost control, and optimize cloud spend across all portals.
This enhancement marks a major milestone in CloudSpend’s product maturity and progression as a next-generation FinOps solution. As cloud ecosystems grow more distributed and financially complex, CloudSpend will continue to evolve with a focus on helping customers enhance governance, improve operational efficiency, and build long-term resilience, equipping service providers and enterprises to strengthen their FinOps and develop more data-driven practices for the future.
CloudSpend supports businesses of all sizes including MSPs, CSPs, and multi-tenant enterprises, offering free tracking of costs up to $3,000 per month of spending on GCP, AWS, and Azure. Paid plans start at 1% of monthly spend above $3,000, and organizations spending over $100,000 can request a custom quote.
The Latest
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.
The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...
The gap is widening between what teams spend on observability tools and the value they receive amid surging data volumes and budget pressures, according to The Breaking Point for Observability Leaders, a report from Imply ...
Seamless shopping is a basic demand of today's boundaryless consumer — one with little patience for friction, limited tolerance for disconnected experiences and minimal hesitation in switching brands. Customers expect intuitive, highly personalized experiences and the ability to move effortlessly across physical and digital channels within the same journey. Failure to deliver can cost dearly ...
If your best engineers spend their days sorting tickets and resetting access, you are wasting talent. New global data shows that employees in the IT sector rank among the least motivated across industries. They're under a lot of pressure from many angles. Pressure to upskill and uncertainty around what agentic AI means for job security is creating anxiety. Meanwhile, these roles often function like an on-call job and require many repetitive tasks ...