Multi-cloud sounds great on slides.
- No vendor lock-in
- Best-of-breed services
- Higher availability
- Better negotiation power
- We believed all of that too
At Haptik, we did not set out to build a multi-cloud platform. But over time, we found ourselves running real production workloads across AWS, Azure, and GCP. Not experiments. Not proof of concepts. Customer-facing systems that had to work every day.
This is the honest truth about what multi-cloud really gives you, and what it quietly takes away.
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How We Ended Up on Three Cloud Platforms
Our journey was driven by business reality, not ideology.
Different customers had different compliance and data residency needs.
Customers also operated in different geographic regions, with strict regional presence and regulatory requirements.
Some enterprises were deeply committed to a specific cloud.
Certain managed services were clearly stronger on one platform than another.
Expansion, partnerships, and enterprise deals introduced constraints we could not ignore.
We started on AWS, which powered our early platform and shaped how we operate. As enterprise adoption increased, Azure came in. Not because AWS failed us, but because enterprise expectations and cost economics pushed us there. Over time, Azure became a large and critical part of our infrastructure.
Later, we added GCP, this time intentionally. Its pricing and strengths in compute and data-heavy workloads made sense for a clearly defined scope.
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Today, AWS and Azure make up the majority of our footprint, with GCP running a meaningful but focused set of production workloads.
Overall, investing across multiple clouds made strategic sense given customer demand, compliance considerations, and long-term business flexibility.
This is how multi-cloud usually happens. Not as a grand strategy, but as a series of practical decisions.
The First Hard Truth: Cloud-Agnostic Architectures Do Not Exist
You can standardize on Kubernetes, Terraform, CI/CD pipelines, and observability patterns. That helps.
But the moment you go beyond basic compute and storage, differences show up fast.
Each cloud has its own IAM model, networking behavior, load balancers, managed databases, and failure modes. Even Kubernetes behaves differently once you factor in CNI choices, ingress controllers, storage classes, and cloud integrations.
You end up writing cloud-specific logic anyway. It just hides in different places.
Operational Complexity Grows Faster Than You Expect
Running one cloud is hard.
Running three is much harder than 3x
Identity and access management becomes a maze.
AWS IAM, Azure Entra ID, and GCP IAM all work differently. Audits take longer. Least-privilege is harder to enforce. Incident response slows down.
Networking and observability are never set up and forget.
Latency, egress costs, and cross-cloud debugging become part of daily life. Logs and metrics can be centralized, but real incident resolution still requires cloud-specific knowledge.
Your SREs are no longer experts in one ecosystem. They have to be fluent in three.
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Cost Optimization Becomes a Full-Time Job
One of the biggest myths is that multi-cloud automatically saves money.
In reality, you duplicate baseline infrastructure, pay egress costs between clouds, and maintain parallel tooling and expertise.
Optimizing cost on a single cloud is hard. Doing it across multiple clouds requires strong FinOps practices, disciplined tagging, and cloud-specific cost intelligence.
Without that, multi-cloud often costs more, not less.
The Real Benefits of Multi-Cloud
Despite the complexity, we believe multi-cloud makes sense when done deliberately.
Enterprise enablement
This is the biggest reason. Some customers will not move off their preferred cloud. Multi-cloud lets you meet them where they are and close deals that would otherwise stall.
Risk distribution
It matters, but not in the way people expect. Multi-cloud does not eliminate outages. It reduces long-term business risk tied to a single vendor, region, or regulatory environment. This is a board-level benefit, not an engineering one.
Best-of-breed services
It can be valuable in limited, controlled cases. Certain clouds genuinely do specific things better.
Some providers lead in analytics and data warehousing, others in ecosystem depth and managed infrastructure maturity, and others in hybrid enterprise integration
The key is restraint.
What We Would Do Differently
If we were advising our past selves:
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Be intentionally multi-cloud. Every cloud should exist for a clear business reason and be periodically re-evaluated.
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Standardize aggressively at the platform layer — not to hide differences, but to reduce cognitive load through clear patterns and golden paths.
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Accept that some things will always be cloud-specific. Fighting that reality wastes time. Portability should exist at the workload level, not at every service API.
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Invest early in a strong governance framework that spans all clouds, rather than stitching together separate policies later.
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Establish a clear cybersecurity maturity framework to continuously assess identity, network, workload, and data security posture across environments.
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Adopt centralized security controls such as CSPM, SIEM, and unified policy enforcement to maintain visibility, detect misconfigurations, and respond consistently.
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Keep everything tied together with centralized monitoring and observability so operational and security signals are not fragmented across clouds.
Looking Ahead: Should You Go Multi-Cloud?
If you do not have a strong business reason, do not.
A well-run single-cloud setup beats a poorly executed multi-cloud strategy every time.
But if you serve large enterprises, operate across regions and regulations, or need long-term strategic flexibility, multi-cloud can be the right choice, as long as you go in with open eyes.
At Haptik, multi-cloud was about building systems that worked for our customers and our future, even when that meant embracing complexity.
That is the part no slide deck ever tells you.