Customers

Trusted by data and AI teams.
Across enterprises and startups.

Highlights

Unify cloud cost monitoring across AWS, Azure, Google Cloud, and Oracle Cloud accounts.

Multi-CloudCostSummary

Drill down to cloud costs by operations, resources, regions, instances, teams, and more.

GranularCostDrillDown

Improve tag compliance to improve cloud cost management and get help in implementing chargeback.

TagCompliance&CostAnalysis

Get alerts on Slack, email (or any other preferred tool), and recommendations to optimize cloud costs.

Alerts&Recommendations

Advantages

100%

accurate cost center mapping

50%

reduction in monthly cloud spend

40%

cloud resource reduction

99.95%

Uptime

Gathr Optics

The Cloud Cost Puzzle

While the cloud offers significant advantages in terms of speed, scalability, and reduced total cost of ownership, organizations often face multiple challenges in monitoring and optimizing cloud resources and budgets.

White Paper

Cloud Cost Optimization

Automate Insights & Actions to Boost Cloud ROI

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Solution Details

Unified Cloud Cost Optimization

Optimize hybrid and multi-cloud resources with unified cloud monitoring.

  • Limited Cloud Cost Visibility

    It leads to provisioning gaps, unused/orphaned VMs, resource sprawl, & budget violations.

  • Lack of Predictability

    Teams fail to gauge cyclical trends and patterns or plan for any spikes in usage.

  • Lack of Actionable Insights

    Teams don’t know which instances, teams, and regions need optimization.

CloudCostMonitoringChallenges
  • Improve Integration

    Smart connectors simplify the collection and analysis of hybrid & multi-cloud costs.

  • Drill Down in Minutes

    Get cloud cost breakdown by tags, accounts, regions, usage types, workload, & more.

  • Gain Predictability

    Leverage Python & machine learning to detect anomalies, predict cost spikes and get cloud cost optimization recommendations.

SimplifyCloudCostManagement
  • Multi-cloud Management

    Improve tag compliance and resource management across AWS, Azure, GCP, & Oracle Cloud accounts.

  • Advanced Chargeback

    Assess cloud cost distribution across enterprise teams & implement chargeback.

  • Alerts & Actions

    Get advanced alerts (via Slack, email, etc.) & auto-shutdown unused/idle resources.

MaximizeCloudROI

Integrations

Expert Opinion

Approved by the
industry experts

Gathr has the stamp of approval from leading analysts and market experts.

MEET GATHR

Gen AI powered, no-code, unified
data-to-outcome platform

  • No-code for data at scale, batch and streaming
  • Gen AI help to search, understand, query, and build easily
  • 250+ connectors,
    200+ operators,
    50+ apps and
    Solution blueprints
  • Unified collaborative experience
  • Best of open source and enterprise grade
  • Production ready output from day 1

Capabilities

Why Gathr

Ten Reasons to upgrade to Gathr

No code for data at scale

When it comes to data processing, data analysis, and data management, there are many no-code tools; however, their capabilities only address fragments of the data journey, and cannot deal with the complexities of data at scale. On the other hand, there are tools that support data at scale, but they are often complex, demand a steep learning curve, and require significant coding efforts. Gathr stands alone as the only platform that supports no-code application development for data at scale.

All-in-one unified platform

Say goodbye to the challenges of managing multiple tools and product suites. Gathr unifies data integration, data quality, machine learning, action-based visualization, and process automation into a seamless, all-in-one platform. A unified user experience allows you to effortlessly stitch end-to-end ‘data to outcome’ business apps across these capabilities. Moreover, a host of ready-to-use business apps for DevOps, FinOps, CloudOps, SecOps, and more, are available out of the box.

ML at the front and center

Gathr offers enterprise-grade ML and MLOps capabilities such as drag-and-drop ML, model training and tuning, drift detection, model management, and more. The platform streamlines the entire ML lifecycle for teams to either build or bring in their own model and operationalize the models quickly, at scale. Seamlessly use ML in-line within your pipelines and apps to improve data quality and power advanced analytics. Moreover, several out-of-the-box apps come with built-in ML capabilities for anomaly detection, predictions, recommendations, etc.

Industry-leading real-time capabilities

Gathr stands at the forefront of streaming analytics, enabling seamless processing, analysis, and instant action on streaming data. With unmatched scalability, integration with diverse streaming sources, and robust complex event processing, Gathr sets the standard. Low-latency data transformation, ML integration, alerts, and visualization tools, make it the go-to solution for real-time enterprise use cases. Year after year recognition by Gartner and Forrester reaffirms Gathr's leadership in this space.

Self-service fueled productivity

Teams with varying skills can effortlessly build, deploy, and manage data pipelines and applications in minutes with Gathr's intuitive self-service drag-and-drop UI. Without the code-level complexities and lesser maintenance hassles with point-and-click troubleshooting, Gathr significantly boosts your teams' productivity. With over 250 connectors, 200 transformations, and 100 ML algorithms, available out of the box, Gathr turbocharges your application development. Plus, our library of 100+ ready-to-use business apps propels your data-driven journey into overdrive.

Collaborative innovation unleashed

Break silos and foster real-time collaboration between data stewards, data engineers, data scientists, DevOps engineers, business analysts, and business users. Not only Gathr ensures smooth handoff between data stewards and data engineering teams, but also streamlines version control, movement of pipelines from dev to prod, end-to-end monitoring, debugging, and more to simplify DevOps. Though the platform is designed for unhindered collaboration and productivity, its role-based access control, encryption, and other enterprise-grade security features ensure data integrity and security at all times.

Automation baked throughout

Simplify and speed up pipeline creation with no-code application development, and features like auto schema detection, auto pipeline inspection, auto data validation, and more. Employ fully automated data pipelines for ongoing database replication, data lake hydration, data cleansing, ML model tuning, etc. Gathr extends its automation power to your business processes, automating workflows, repetitive tasks and actions - freeing your team from mundane routines.

Enterprise-grade reliability & extensibility

While Gathr is built over an open architecture, it offers the quality, performance, stability, reliability, security, and support that enterprises require for their mission-critical operations. It means you get the power of the community with all its upgrades without the hassle of going through numerous forums to find answers for any technical issue. At the same time, Gathr's patented extensibility allows you to reuse pre-built and custom components across use cases and quickly extend your solutions. With these capabilities, you can ensure your enterprise remains nimble and future ready.

Interoperable across cloud vendors

Gathr supports key functionalities such as portability, interoperability, and platform-independency. With Gathr you can build applications once and deploy them on any other cloud in the future. No more cloud vendor lock-ins! You can easily move to a different cloud vendor in the future without experiencing issues such as application downtime, high costs, or technical incompatibilities.

Lowest TCO

Gathr offers more cost advantages than any other solution in its class. For enterprise users with On-Prem/VPC deployments, it costs less than the TCO of a skilled engineer. With its SaaS plans, you can create apps and experiment at no cost and pay only when you execute the app. You can get started quickly with the forever-free plan. As you grow and scale, enjoy the flexibility of pay-as-you-go pricing, with as little as $0.25/credit, that too after utilization of free credits. Moreover, you get 30% additional cost savings with the option to bring your own cloud (BYOC).

Learning and Insights

Stay ahead of the curve

FAQS

Find Your Answers

What are some common mistakes that lead to cost overruns in the cloud?
While hybrid and multi-cloud operations can in theory allow organizations to improve resource allocation and efficiency, many times they lead to cost overruns. Resources aren’t set up properly (sizing issues) and are left to run indefinitely as there aren’t any decommissioning dates. Due to a lack of visibility into virtual sprawl, costs can easily spiral out of control. Further, organizations lack early warnings or budget consumption alerts. It is also seen that DevOps teams often subscribe to cloud services for monitoring and backend connectivity, which aren’t accounted for in the cloud cost calculations. Such oversights prevent organizations from assessing the true cost and ROI of their cloud.
What are some simple ways to control cloud costs?
Most organizations that adopted cloud with a lift-and-shit approach have realized that it’s not the best way to optimize cloud costs. That’s why organizations need a cloud-first mindset, which means they need to stop provisioning for the peak loads and leverage the benefits of the cloud with right-sized, just-in-time resources. As not all workloads are equal, organizations need to match their demand with on-demand auto-scaling instances, reserved instances, spot instances, and discounted instances. They also need to adopt cloud cost management tools that automate consumption forecasting and simplify capacity management (e.g., power scheduling, removing unused instances, etc.).
How to implement continuous cost monitoring and optimization in multi-cloud environments?
Organizations realize the importance of continuous cost optimization at every stage of cloud consumption and why it needs to be a part of their operating model. To this end, they need to embrace automation and self-service analytics tools that offer better visibility and control over cloud spends and their baselines. To optimize cloud costs, organizations need to implement tools that simplify cost allocation, resource tagging, and showback/chargeback models. With automated multi cloud cost dashboards, organizations can easily understand what they are paying for and quickly determine if they have overprovisioned or heading towards some cost spike.
What’s the difference between a FinOps and cloud cost management solution?
While FinOps is an evolving field, most FinOps solutions are only an extension of cloud cost management. However, there’s a significant difference in the approach; cloud cost management tools are aimed at IT or operations teams, while FinOps tools are built to engage executives (project managers, product owners, etc.) and DevOps engineers, providing them improved visibility into their infrastructure costs. Earlier, only a few people in the operations team used to be responsible for controlling cloud costs. However, a majority of cost optimizations weren’t directly in their control as it required sensitizing and educating everyone about cloud cost management. This is where FinOps solutions make the difference by including every resource consumer into the cost-saving process.
How to select a multi-cloud cost management solution?
While most vendors claim to offer interactive dashboards and granular visibility, organizations should evaluate their ease of usage and customization. You may want to spin up a custom dashboard for better visibility or define some metric to benchmark your cloud efficiency. A multi-cloud cost-management solution should also offer seamless data collection as leading cloud services providers now charge by the second, more data is generated than ever before. The solution should be capable enough to not only ingest all this cost data from different cloud vendors in near real-time but should also simplify its analysis. Waiting for someone to report until the end of the month can be too late to take remedial actions. Further, organizations should prioritize solutions that offer AI or machine learning capabilities for anomaly detection and cost predictions. Such cloud cost insights can help organizations adopt a more proactive approach to managing their cloud spending.