We are often asked what’s the difference between Anodot and CloudHealth. Since both platforms offer cloud cost management solutions, the differentiation might be unclear. In this article, we’ll quickly clarify what each platform is built for, and why — despite some overlaps in features — these are two fundamentally different creatures.
You probably know that CloudHealth is a great observability and BI tool that collects and consolidates all of your cloud environment data in a single platform so you can optimize and govern your cloud environment. CloudHealth does this by gathering data and metadata related to your cloud-based services use into a centralized data analytics platform, and provides you with analysis, recommendations, and trending reporting on cost, usage, performance, and security.
What CloudHealth does not do is detect and report cloud cost incidents in near real-time, get engineers to take immediate actions, or provide personalized recommendations to continuously eliminate cloud waste.
CloudHealth users report the following challenges:
CloudHealth’s UI is slow, clunky, and difficult to use. Multidimensional reports are hard to create with current reporting and APIs. Even advanced users will find the UI difficult to use, resulting in long adoption times and long time to value.
Limited Kubernetes visibility
While CloudHealth provides visibility into your on-demand Kubernetes costs (requires installation of CloudHealth Agent), it does not provide actual costs or a breakdown of utilized, idle, and unallocated costs within Kubernetes.
Few savings recommendations
While CloudHealth does well with rightsizing and SP/RI recommendations, there is so much more to cloud waste elimination. CloudHealth only offers recommendations for database and compute services: AWS (EBS and EC2), Azure (SQL and VMs), and GCP (GCE).
With CloudHealth, users need to manually define granular policies including static thresholds, and identify anomalous costs on dashboards and reports. Manual monitoring using static thresholds will inevitably produce alert storms (too many false positives) — or you could miss key events (false negatives), opening the door to preventable runaway costs.
Lack of business context
An increase in cloud costs might be a result of business growth – but not always. Understanding whether a cost increase is proportionately tied to revenue growth requires that you know and track your unit costs over time, something that CloudHealth does not offer.
In the context of modern cloud cost monitoring and management solutions, CloudHealth has the same limitations as other 1st generation cloud cost management platforms.
Anodot for Cloud Costs
While we respect CloudHealth’s leadership as a first-gen cloud financial management platform, Anodot represents a next-gen approach to help you understand and control multi-cloud spending in one easy-to-use platform in which cloud spend is continuously monitored and correlated with business KPIs.
Anodot’s key pillars are:
- Visibility. Visualize your entire infrastructure, deep dive into your data details, correlate cloud spend with business KPIs and get a clear picture of how your infrastructure and economies are changing.
- Saving insights. Continuously eliminate waste and optimize your infrastructure with personalized savings recommendations for unknown saving opportunities that can be implemented in a few steps.
- Monitoring. Easily connect to AWS, GCP, and Azure to monitor and manage your multicloud and Kubernetes spend in real-time.
With Anodot, companies can achieve unit economics and better understand how specific units influence cloud costs, usage, and performance. Cloud and FinOps teams can see how business decisions impact cloud metrics, get a contextual understanding of cloud costs, and continuously optimize their cloud investments to drive strategic business initiatives.
While CloudHealth and Anodot do share some functionality, Anodot outperforms CloudHealth and other 1st gen cloud financial management platforms in these critical parameters:
Easy to use
Anodot offers completely autonomous anomaly detection, forecasting, correlation, and recommendations — out of the box. It has a slick and simple UI that’s accessible to anyone. Track and report on unit costs, reallocate costs (without tagging) and monitor increases in spend. Anodot’s continuous monitoring and deep visibility enable FinOps, DevOps, and Finance teams to work together towards cutting cloud spend.
Associate costs with business KPIs
With Anodot, companies can monitor business KPIs, gain visibility into unit economics, and understand how specific units impact cloud costs, usage, and performance. Cloud and FinOps teams can see how business decisions impact cloud metrics, get a contextual understanding of cloud costs, and continuously optimize their cloud investments to drive strategic business initiatives.
Near real-time anomaly detection
Anodot automatically learns each service usage pattern and alerts relevant teams to irregular cloud spend and usage anomalies in near-real-time (up to 8 hours delay). For each anomaly, Anodot also provides the full context of what is happening for the fastest time to resolution. By using proprietary machine learning algorithms, the platform offers deep root-cause analysis and recommended remediation, which significantly reduces resolution time.
Advanced savings recommendations
Anodot helps FinOps teams continuously eliminate cloud waste with personalized cost optimization recommendations, waste trends, and exclusions. Anodot leverages over 40 types of savings recommendations that can be implemented in a few steps and prioritizes optimizations by impact, so you’ll always know what’s your next best move.
Complete Kubernetes cost intelligence
Anodot provides granular insights about your Kubernetes deployment. Easily track your spending and usage across clusters with detailed reports and dashboards. Anodot for Cloud Costs’ powerful algorithms and multi-dimensional filters enable teams to deep dive into performance and identify under-utilization at the node level.
Accurate cloud cost forecasts
Anodot’s AI-powered forecasting leverages deep learning and unlimited historical data to automatically optimize cloud cost forecasts, enabling users to anticipate changing conditions and usage and get a better read on related costs.
Schedule a demo to get a first-hand experience and learn more about what Anodot for Cloud Costs can do for you.