SAN FRANCISCO, June 8, 2021 /PRNewswire/ — Tuesday Capital (formerly Crunchfund) today announces the launch of Fund IV, which will continue Tuesday’s mission of identifying and investing in the next generation of world-changing companies. Fund IV will be utilized for early-stage, industry-agnostic startup investments. The new fund will allow Tuesday to continue broadening its global investment reach into Latin America and Europe, lead a greater number of funding rounds and expand the Tuesday x frog partnership announced last year. Tuesday has a long track record of investing in high growth startups like Uber, Airbnb, Airtable, Gitlab, OpenDoor, DigitalOcean and Cruise Automation at early stages and Fund IV will continue that legacy.

Since launching in 2011, Tuesday Capital has provided funders a unique source of both the capital and innovative services startups need to succeed. Tuesday is committed to providing entrepreneurs with resources that are out of reach for many startups, and Fund IV enables the firm to continue to assist its founders with a diverse array of offerings tailored to help them grow their companies. This thesis is crystallized in Tuesday’s recent partnership with renowned design house, frog, which provides Tuesday’s portfolio companies access to an array of services from branding and design to marketing and public relations. Notable Tuesday x frog collaborative investments include Companion, Kolors and Alto.

“We are very proud to announce Tuesday’s Fund IV. Our mission is to give the best founders tailored support to help their companies take flight and we are excited to bring this new fund to the market to do just that. It’s been an unprecedented year, but founders and investors have found some amazing opportunities despite the uncertainty. This new fund marks a continuation of the legacy we’ve built at Tuesday,” said Founder and Managing Partner Patrick Gallagher.

“It’s an amazing time for venture capital and we are seeing enormous inflows of capital into the market. There’s also a vibrant entrepreneurial and startup ecosystem that has taken root across the country as talent spread out during the pandemic. As a digital nomad myself, I’ve been able to meet with founders in up and coming startup hubs like Roanoke, Tulum, Nosara and Costa Rica. Travelling has helped extend Tuesday’s reach by unlocking new investment opportunities and relationships with amazing founders. I’m excited for the future and this new fund will allow us to help bring the next generation of household names to life,” said Partner Prashant Fonseka.

As Tuesday continues to expand its portfolio in a consistent and scalable way, this newly launched fund will increase the Tuesday global footprint in Latin America and Europe. Under Fund IV, startup founders outside of the traditional VC capital cities will be able to leverage the unparalleled access to capital, services and guidance from Tuesday. With competition increasing rapidly in the VC world, Tuesday’s unique offerings, investment approach and founder-first mindset will continue to guide Fund IV through 2021 and beyond.

Tuesday Capital
Founded in 2011, Tuesday Capital is an early-stage, sector-agnostic venture capital firm based in San Francisco and New York. Tuesday maintains a variety of relationships with other VC firms and works alongside them to help propel the companies of tomorrow. The firm has made hundreds of investments over the last eight years, backing many startups that have gone on to be Silicon Valley standouts and household names, including Airtable, Airbnb, Uber, Cruise, Digital Ocean, GitLab, Rover, Good Eggs, Instawork, Zipline and many others.

Introduction: Global Web Service Cloud Market, 2020-25
The global Web Service Cloud market report is analyzed based on its market share by value and volume. A regional, country, and global level analysis of all the Web Service Cloud segments is done in the report. The study includes all major geographic regions across the world influencing the Web Service Cloud market. Key insights of the Web Service Cloud market are dominating factors, prospect growth opportunities, restraints, and challenges are given in the report. The finding of the report is supported by porter’s five forces analysis, competitiveness analysis, assessment of key characteristics of the competitive landscape, and raw material analysis. The research methodologies and the resultant data provided in the report match your business needs.

Competition Assessment: Global Web Service Cloud Market

Atlantic
Amazon Web Services
Google Cloud Platform
IBM Cloud
Rackspace
VMware
DigitalOcean
Oracle Cloud
CloudSigma

Available Sample Report in PDF Version along with Graphs and Figures@ https://www.orbismarketreports.com/sample-request/132155?utm_source=PujaM

The investment research data offered in the report allows the stakeholders and investors in the Web Service Cloud market to focus on the ongoing and upcoming investment opportunities and makes them aware of the investment scenarios across the Web Service Cloud market. The strategic intelligence capabilities boost your business extension and help better understand the potential of diverse industries in the Web Service Cloud market. Qualitative and quantitative analysis of Web Service Cloud market scenarios as per the geographical regions and performance of its different regions is provided in the report. The research analysis study is customized according to the business requirements of the market participants. Furthermore, the report highlights specifications and challenges, including multiple methodologies to extract accurate data figures and facts, detailed interviews, and studies the competitive landscape of the Web Service Cloud market

Segmentation by Type:

Infrastructure As A Service
Platform As A Service
Software As A Service

Based on application, segmentation is primarily been harnessed to understand the scope of application based on industrial demands and user preferences.

Segmentation by Application:

SMEs
Large Enterprises

Regional Segmentation identifies Americas, European nations, APAC countries and MEA as popular growth hubs.

The report highlights various aspects in the Web Service Cloud market and answers relevant questions on the Web Service Cloud market:
1. What are the best investment options to launch new products and offer advanced services in the Web Service Cloud market?
2. What are the value propositions relevant to the customer or market segment a business should focus on while making new research or investing funds in the Web Service Cloud market?
3. What policy changes will help stakeholders to boost their supply chain and demand network?
4. Which regions would demand more products and services offered in certain segments in the forecast period?
5. What are the strategies that have helped the established players to reduce supplier costs, procurement and, logistics costs?
6.C-suite perspective leveraged to move businesses to new growth trajectory?
7. What are the government interventions boosting the Web Service Cloud market or government regulations that may challenge the status of the regional and global industries in the Web Service Cloud market?
8. How will the political and economic crisis affect opportunities in Web Service Cloud growth areas?

Table of Contents
Chapter One: Report Overview
1.1 Study Scope
1.2 Key Market Segments
1.3 Players Covered: Ranking by Web Service Cloud Revenue
1.4 Market Analysis by Type
1.4.1 Global Web Service Cloud Market Size Growth Rate by Type: 2020 VS 2026
1.5 Market by Application
1.5.1 Global Web Service Cloud Market Share by Application: 2020 VS 2026
1.6 Study Objectives
1.7 Years Considered

Chapter Two: Global Growth Trends by Regions
2.1 Web Service Cloud Market Perspective (2015-2026)
2.2 Web Service Cloud Growth Trends by Regions
2.2.1 Web Service Cloud Market Size by Regions: 2015 VS 2020 VS 2026
2.2.2 Web Service Cloud Historic Market Share by Regions (2015-2020)
2.2.3 Web Service Cloud Forecasted Market Size by Regions (2021-2026)
2.3 Industry Trends and Growth Strategy
2.3.1 Market Top Trends
2.3.2 Market Drivers
2.3.3 Market Challenges
2.3.4 Porter’s Five Forces Analysis
2.3.5 Web Service Cloud Market Growth Strategy
2.3.6 Primary Interviews with Key Web Service Cloud Players (Opinion Leaders)

Chapter Three: Competition Landscape by Key Players
3.1 Global Top Web Service Cloud Players by Market Size
3.1.1 Global Top Web Service Cloud Players by Revenue (2015-2020)
3.1.2 Global Web Service Cloud Revenue Market Share by Players (2015-2020)
3.1.3 Global Web Service Cloud Market Share by Company Type (Tier 1, Tier Chapter Two: and Tier 3)
3.2 Global Web Service Cloud Market Concentration Ratio
3.2.1 Global Web Service Cloud Market Concentration Ratio (CRChapter Five: and HHI)
3.2.2 Global Top Chapter Ten: and Top 5 Companies by Web Service Cloud Revenue in 2020
3.3 Web Service Cloud Key Players Head office and Area Served
3.4 Key Players Web Service Cloud Product Solution and Service
3.5 Date of Enter into Web Service Cloud Market
3.6 Mergers & Acquisitions, Expansion Plans

With unfailing market gauging skills, has been excelling in curating tailored business intelligence data across industry verticals. Constantly thriving to expand our skill development, our strength lies in dedicated intellectuals with dynamic problem solving intent, ever willing to mold boundaries to scale heights in market interpretation.

Overview

Cloud computing has moved from an IT convenience to a core operational infrastructure for businesses of all sizes. As organisations accelerate digital transformation, understanding the market structure — which service models are growing, which regions lead adoption, and which vendors compete for share — helps engineering and operations teams make better platform decisions.

The following breakdown draws on the structure of DataIntelo’s Global Cloud Computing for Business Operations Market report (2020 edition), which covers historical data from 2018 and projections through 2026, including early analysis of COVID-19’s effect on enterprise cloud adoption.

Market Segmentation

The market is analysed across three primary dimensions: service (product) type, deployment model, and geography.

By Service Type

• Infrastructure as a Service (IaaS) — Virtualised compute, storage, and networking resources delivered on demand. Examples include AWS EC2, Azure Virtual Machines, and Google Compute Engine.
• Platform as a Service (PaaS) — Managed runtimes, databases, and middleware that let development teams deploy applications without managing the underlying infrastructure. Examples include AWS Elastic Beanstalk, Azure App Service, and Google App Engine.
• Software as a Service (SaaS) — Fully managed applications delivered over the internet, billed by subscription. Examples include Salesforce CRM, Microsoft 365, and Google Workspace.
• Recovery as a Service (RaaS) — Cloud-based disaster recovery and backup capabilities that replace on-premises DR infrastructure, enabling rapid failover and data restoration.

By Deployment Model

• Private Cloud — Infrastructure dedicated to a single organisation, hosted on-premises or in a colocation facility. Preferred by regulated industries (finance, healthcare, government) with strict data-sovereignty requirements.
• Hybrid Cloud — A combination of private and public cloud environments connected by orchestration and networking. Allows workloads to move between environments based on cost, compliance, or latency requirements.
• Other / Multi-Cloud — Architectures that span multiple public cloud providers simultaneously, reducing vendor lock-in and improving resilience.

By Region

The report segments the market into the following geographies:

• North America — United States, Canada, Mexico
• Europe — Germany, United Kingdom, France, and the rest of Europe
• Asia Pacific — China, Japan, India, and the rest of Asia Pacific
• Latin America — Brazil and the rest of Latin America
• Middle East & Africa — GCC countries and the rest of the region

North America has historically led cloud adoption, driven by mature enterprise IT budgets and the headquarters presence of the largest providers. Asia Pacific — particularly China and India — represents the fastest-growing region, fuelled by rapid digitisation of SMEs and government-backed cloud initiatives.

COVID-19 Impact on Enterprise Cloud Adoption

The 2020 edition of this report was published during the early stages of the COVID-19 pandemic. The crisis accelerated several pre-existing cloud trends:

• Remote work mandates sharply increased demand for SaaS collaboration and VDI (virtual desktop infrastructure) workloads.
• Organisations with existing hybrid or public cloud infrastructure were better positioned to scale capacity on short notice than those running purely on-premises environments.
• Cloud-based disaster recovery (RaaS) gained renewed attention as business continuity planning moved from a checkbox exercise to an operational priority.
• Budget pressures caused some enterprises to accelerate cloud migrations to reduce capital expenditure on hardware refresh cycles.

Major Market Players

The competitive landscape for cloud business operations platforms is broad, ranging from hyperscale infrastructure providers to specialist SaaS vendors. The DataIntelo report identifies the following as significant participants:

AWS, Microsoft Azure, and Google Cloud Platform collectively hold the largest share of global IaaS and PaaS revenue. The remaining vendors compete through specialisation: Red Hat and VMware focus on hybrid infrastructure, Salesforce and ServiceNow on business-process SaaS, and DigitalOcean and Kamatera on developer-friendly, cost-efficient compute for SMEs.

Key Considerations for DevOps and Cloud Engineering Teams

Market reports like this one are useful for contextualising vendor investment and roadmap stability. For practitioners evaluating cloud platforms, the following considerations remain relevant regardless of market share rankings:

• Service model fit — IaaS gives the most control but the most operational responsibility. PaaS reduces toil. SaaS eliminates infrastructure management entirely. Choose based on your team’s capacity, not on what is most popular.
• Hybrid readiness — Organisations with on-premises regulatory workloads should evaluate vendors with strong hybrid connectivity: Azure Arc, AWS Outposts, Google Distributed Cloud, and VMware Cloud on AWS are common options.
• Vendor concentration risk — Relying entirely on a single provider creates lock-in and single-point-of-failure risk. Multi-cloud strategies using Kubernetes and infrastructure-as-code reduce coupling.
• Regional data residency — Verify that your chosen provider offers data centre regions that satisfy your compliance obligations (GDPR in Europe, sector-specific rules in healthcare and finance).
• Total cost of ownership — Egress fees, reserved instance commitments, and support tier pricing often determine real-world cost more than headline compute rates.

Summary

The cloud computing market for business operations spans a wide range of service models (IaaS, PaaS, SaaS, RaaS), deployment architectures (private, hybrid, multi-cloud), and geographic markets. The 2020 DataIntelo report captures a market in transition: COVID-19 accelerated adoption timelines, and hyperscale providers strengthened their leads while specialist vendors competed through focus and price. For DevOps teams, understanding this landscape informs platform selection, vendor negotiation, and long-term architecture decisions.

When working with a Kubernetes-based application, you will occasionally need to schedule additional pods to handle peak traffic or bursts of compute load. By default, scaling is a manual operation: a developer changes the replica count in the Deployment object, then reduces it again when load subsides. That manual dependency can become a liability. Traffic can spike at 3 AM, or a sudden viral event can overwhelm a cluster before anyone has a chance to respond.

The solution is the Horizontal Pod Autoscaler (HPA). The HPA reads resource-usage data from the Metrics Server and automatically adjusts the number of running pods to keep per-pod utilization within a target threshold. Combined with DigitalOcean Kubernetes (DOKS), a managed Kubernetes service, HPA lets you build infrastructure that adapts to load without human intervention.

When autoscaling is a good fit: HPA works best for stateless workloads that can run as multiple parallel instances — web servers, API gateways, and microservices are ideal examples. Stateful workloads such as databases are a poor fit: you would need to handle race conditions, data integrity, synchronization, and dynamic cluster-membership changes. This tutorial does not cover database autoscaling.

In this tutorial you will:

• Deploy a sample Nginx workload on DOKS.
• Set CPU resource limits and requests on the Deployment.
• Install Metrics Server with Helm and work around a DOKS-specific DNS issue.
• Create an HPA and validate that it scales the Deployment automatically under artificial CPU load.

Prerequisites

Before you begin, ensure you have:

• A running DigitalOcean Kubernetes cluster with kubectl configured to connect to it. Follow the Connect to your Cluster instructions shown after cluster creation, or see the DigitalOcean Kubernetes Quickstart.
• The Helm package manager installed locally and Tiller initialized on the cluster. Complete Steps 1 and 2 of the How To Install Software on Kubernetes Clusters with the Helm Package Manager tutorial on DigitalOcean.

Step 1 — Creating a Test Deployment

To demonstrate HPA, you need a workload to autoscale. This tutorial uses the official Nginx image because it is stateless, widely deployed in Kubernetes environments (for example, as the Nginx Ingress Controller), and trivially lightweight to spin up. Skip this step if you already have a Deployment you want to scale.

Create the Deployment:

kubectl create deployment web --image=nginx:latest

The --image=nginx:latest flag pulls the latest Nginx image from Docker Hub. After a few seconds, verify that the pod is running:

kubectl get pods

Expected output:

NAME                   READY   STATUS    RESTARTS   AGE
web-84d7787df5-btf9h   1/1     Running   0          11s

Notice that only one pod is running. Once autoscaling triggers, additional pods will spin up automatically.

Step 2 — Setting CPU Limits and Requests on Your Deployment

The HPA cannot make meaningful scaling decisions without knowing how much CPU a pod is allowed to use. Kubernetes uses two related concepts:

• Requests — the amount of CPU (or memory) that Kubernetes reserves on a node for the pod when scheduling. A node is only considered for scheduling if it has at least this much resource available.
• Limits — the maximum amount of CPU (or memory) the container is allowed to consume. The HPA uses this value as the 100% baseline when computing utilization percentage.

For example, if you set a CPU limit of 100m (millicores) and the pod is currently using 80m, the HPA sees the pod at 80% utilization. Without a limit, the HPA has no frame of reference.

Open the Deployment for editing:

kubectl edit deployment web

This command opens the live API object in your default editor (controlled by the KUBE_EDITOR or EDITOR environment variable, defaulting to vi on Linux or notepad on Windows).

Locate the resources: {} line under the nginx container spec and replace it with the following block:

    spec:
      containers:
      - image: nginx:latest
        imagePullPolicy: Always
        name: nginx
        resources:
          limits:
            cpu: 300m
          requests:
            cpu: 100m
            memory: 250Mi

These values are for demonstration purposes. In production, monitor your workload during both low and peak traffic periods to determine appropriate values, and adjust them over time as your traffic patterns change.

Save and close the file. If there is a YAML syntax error, kubectl will reopen the file and display the error so you can correct it.

Step 3 — Installing Metrics Server

The HPA pulls utilization data from the Kubernetes Metrics Server, a cluster add-on that scrapes resource usage from the kubelet on each node. Without it, the HPA has no data source and cannot function.

Install Metrics Server via Helm:

helm install stable/metrics-server --name metrics-server

Wait for the pod to initialize, then test it:

kubectl top pod

On DOKS, you will likely see this error immediately after installation:

Error: Metrics not available for pod
Error from server (ServiceUnavailable): the server is currently unable to handle the request (get pods.metrics.k8s.io)

Why this happens: DOKS nodes do not register DNS records for their own hostnames. By default, Metrics Server contacts kubelets using node hostnames, which fail to resolve in DOKS. The fix is to instruct Metrics Server to use node internal IP addresses instead.

Edit the Metrics Server Deployment:

kubectl edit deployment metrics-server

Find the command section for the metrics-server container and add two flags as shown below:

      containers:
      - command:
        - /metrics-server
        - --cert-dir=/tmp
        - --logtostderr
        - --secure-port=8443
        - --metric-resolution=60s
        - --kubelet-preferred-address-types=InternalIP

• --kubelet-preferred-address-types=InternalIP — instructs Metrics Server to contact nodes by internal IP, bypassing the unresolvable hostname problem on DOKS.
• --metric-resolution=60s — sets the scrape interval to 60 seconds. Reduce this to 10s or 20s if you need more granular data for faster scaling decisions.

Save and close the editor. After a minute or two, verify that Metrics Server is collecting data:

kubectl top pod

You should now see resource usage for each pod:

NAME                             CPU(cores)   MEMORY(bytes)
metrics-server-db745fcd5-v8gv6   3m           12Mi
web-555db5bf6b-f7btr             0m           2Mi

Metrics Server is now operational and feeding data into the cluster’s resource API. You are ready to configure the HPA.

Step 4 — Creating and Validating the Horizontal Pod Autoscaler

Create the HPA for the web Deployment:

kubectl autoscale deployment web --max=4 --cpu-percent=80

Flag breakdown:

• --max=4 — the HPA will never schedule more than 4 replicas of the web Deployment.
• --cpu-percent=80 — if the average CPU utilization across all pods exceeds 80% of the limit defined in Step 2, the HPA adds more pods. With a limit of 300m, the trigger point is approximately 240m average across all running pods.

You can inspect the HPA object at any time:

kubectl get hpa web

Validating Autoscaling Under Artificial Load

Open a second terminal and run the following watch command to refresh pod metrics every two seconds:

watch "kubectl top pods"

You should see a single web pod consuming minimal CPU:

Every 2.0s: kubectl top pods

NAME                              CPU(cores)   MEMORY(bytes)
metrics-server-6fd5457684-7kqtz   3m           15Mi
web-7476bb659d-q5bjv              0m           2Mi

Switch to your first terminal and open a shell inside the running web pod (replace the pod name with the one from your cluster):

kubectl exec -it web-f765fd676-s9729 /bin/bash

From inside the pod, install the stress tool and run it to generate CPU load:

# Debian/Ubuntu-based images:
apt update && apt-get install -y stress
stress -c 3

# CentOS/RHEL-based images:
yum install -y stress
stress -c 3

Leave stress running and switch back to your second terminal. After approximately two minutes — enough time for Metrics Server to collect data at the configured 60-second interval and for the HPA’s sync loop to react — you will see additional web pods appear:

Every 2.0s: kubectl top pods

NAME                             CPU(cores)   MEMORY(bytes)
metrics-server-db745fcd5-v8gv6   6m           16Mi
web-555db5bf6b-ck98q             0m           2Mi
web-555db5bf6b-f7btr             494m         21Mi
web-555db5bf6b-h5cbx             0m           1Mi
web-555db5bf6b-pvh9f             0m           2Mi

The HPA detected that the single web pod had exceeded the 80% CPU threshold and automatically scheduled additional replicas (up to the maximum of 4). When you stop the stress command with CTRL+C and exit the pod shell, the HPA will scale the Deployment back down after the load subsides and the cool-down period elapses.

Conclusion

In this tutorial you:

• Created an Nginx Deployment on DigitalOcean Kubernetes.
• Defined CPU resource requests and limits to give the HPA a utilization baseline.
• Installed and configured Metrics Server via Helm, including the DOKS-specific --kubelet-preferred-address-types=InternalIP workaround.
• Created a Horizontal Pod Autoscaler and confirmed that it automatically scales the Deployment in response to CPU load.

From here, consider:

• Tuning thresholds — monitor actual workload metrics to set realistic CPU targets and replica limits.
• Memory-based autoscaling — HPA also supports custom and external metrics beyond CPU.
• Cluster Autoscaler — if all nodes are full, new pods will remain in a Pending state. The DigitalOcean Cluster Autoscaler can add worker nodes automatically to accommodate additional pods.
• Helm v3 — the tutorial uses Helm v2 syntax (helm install stable/metrics-server --name metrics-server). Helm v3 dropped Tiller and changed the install syntax to helm install metrics-server stable/metrics-server.

Refer to the official Kubernetes HPA documentation for the full list of configuration options and supported metric sources.

Developer-Centric Cloud in a Crowded Market

India’s cloud market in 2017 was firmly dominated by enterprise-focused giants — Microsoft Azure, Amazon Web Services (AWS), and Google Cloud. Against that backdrop, DigitalOcean pursued a deliberately different strategy: build the best possible cloud experience for individual software developers and small engineering teams, rather than for large enterprise procurement departments.

According to Prabhakar Jayaraman, DigitalOcean’s India Country Manager at the time, the gap the company was filling was clear: “We found that existing services often failed to meet the needs of developers with respect to OS requirements. The developer is the core audience that we cater to as opposed to the enterprise customer. Why should the developer who will actually run a business-critical application spend time configuring storage, backup and networking when they can have it up and running in less than a minute on our service?”

India as DigitalOcean’s Fastest-Growing Market

By mid-2017, India had become DigitalOcean’s fastest-growing market globally. The company established a Bangalore development centre — launched in June 2016 with around 20 staff — where products were developed on timelines aligned with global releases. The team was intentionally kept lean, with a strong emphasis on a remote-first work culture.

The decision to open an India data centre was driven by two clear signals from existing customers: the need for lower latency due to server proximity, and regulatory requirements in sectors such as financial services and healthcare that mandated local data storage. Both are recurring pain points for any cloud provider entering emerging markets.

Edge Computing and IaaS Growth Trends

The article cited Gartner’s identification of edge cloud computing — processing data closer to its source — as one of the top ten global technology trends for 2018. Infrastructure as a Service (IaaS) was projected to be the fastest-growing segment of cloud services, followed by Platform as a Service (PaaS) and Software as a Service (SaaS).

SaaS providers made up a significant portion of DigitalOcean’s user base in India, followed by niche e-commerce platforms and advertising technology developers — a profile that reflects the startup-heavy, developer-led nature of its customer mix.

Startup Ecosystem Engagement

DigitalOcean’s India strategy extended beyond cloud infrastructure into active participation in the startup ecosystem. Key initiatives included:

• NASSCOM 10,000 Startups partnership: DigitalOcean provided up to $3,000 in cloud credits to qualifying developers and startups.
• Workshops, mentorship programs, and bootcamps: Direct developer engagement to help early-stage businesses understand and adopt cloud infrastructure.
• Karnataka Government Startup Booster Kit: Inclusion in a state-level initiative supporting technology startups.

This approach served a dual commercial purpose: it seeded DigitalOcean’s platform within the rapidly growing Indian startup ecosystem while providing cost-effective infrastructure to businesses at the earliest — and most price-sensitive — stage of their growth.

Competing with AWS Lightsail: The Pricing Advantage

When AWS launched Lightsail — its simplified virtual private server product aimed at developers — in December 2016, the developer community quickly noted its similarities to DigitalOcean’s core Droplet offering. The launch was widely interpreted as AWS acknowledging DigitalOcean as a meaningful competitor in the developer segment.

Despite the competitive pressure, DigitalOcean maintained a key differentiator: included bandwidth. Unlike AWS and some other providers, DigitalOcean did not charge separately for bandwidth usage beyond the allocation included in each plan. For developers who had experienced unexpectedly large cloud bills driven by bandwidth overages, this was a significant practical advantage.

Jayaraman framed it plainly: “There is a lot of ambiguity that people see with other services they choose that doesn’t give them control. So at the end of the month, they end up getting bills that might seem inflated due to an additional charge of bandwidth usage. Here you just pay per hour and you get a certain bandwidth usage for the service you have chosen, so there is much more control.”

Key Takeaways for DevOps Engineers

• Developer experience as a product differentiator: DigitalOcean’s success in India demonstrates that optimising for developer velocity — fast provisioning, clear documentation, minimal configuration overhead — can build durable market share even against much larger competitors.
• Transparent pricing reduces operational risk: Predictable per-hour billing with included bandwidth directly addresses one of the most common pain points in cloud cost management.
• Regulatory and latency requirements drive regional infrastructure decisions: The India data centre was a direct response to customer needs in regulated industries — a pattern that repeats across every major cloud provider entering new geographies.
• Startup ecosystem investment builds long-term platform adoption: Seeding credits and mentorship programmes is an established strategy for cloud providers to grow a loyal user base early in a market’s development cycle.

Original reporting: Business Standard, October 2017.