BYOC in Practice: Architectures, Tradeoffs, and What We Learned

Recently, I hosted a panel discussion on Bring Your Own Cloud (BYOC) with engineering and infrastructure leaders from ClickHouse, Zilliz, and groundcover. The goal was to move beyond buzzwords and unpack what BYOC actually means in practice.

What followed was a candid, technical conversation about architecture, cost models, operational tradeoffs, and how AI workloads are accelerating adoption.

What Is BYOC?

At its core, BYOC is a deployment model where a vendor runs its software inside the customer's cloud account rather than in the vendor's own SaaS environment.

The cleanest mental model is control plane and data plane separation.

The vendor hosts the control plane: UI, APIs, user management, orchestration logic, and automation. The data plane, meaning the compute and storage that process and hold customer data, lives entirely inside the customer's cloud account.

In practical terms:

• Your data never leaves your cloud account.
• The vendor still provides a managed experience.
• Communication between control plane and data plane is minimal, encrypted, and auditable.

This preserves the operational ease of SaaS while shifting data ownership and infrastructure boundaries back to the customer.

At groundcover, BYOC is not an add-on or alternative deployment tier. The platform was designed BYOC-native from day one, and that distinction shapes everything from architecture to commercial model.

Why Is BYOC Gaining Momentum?

Across ClickHouse, Zilliz, and groundcover, three consistent drivers came up.

1. Data Sovereignty and Regulatory Requirements

What used to be a "nice to have" is increasingly a deal blocker in regulated industries. BYOC ensures that sensitive data stays within the customer's cloud boundary. In AI-driven environments where training data and embeddings are core IP, companies are less comfortable with data residing in third-party SaaS infrastructure.

2. Cost Transparency and Egress Economics

When large volumes of data move between accounts, especially in AI and observability workloads, network transfer costs accumulate quickly. BYOC eliminates many of those cross-account egress costs by keeping traffic within the same cloud environment.

For vendors that started as SaaS, BYOC can also reduce infrastructure overhead by running inside the customer's existing cloud footprint.

At groundcover, this shift is more fundamental than simply reducing egress costs. Traditional observability platforms are typically priced on ingestion volume: you pay for every log line, trace, or metric you send. That model worked when telemetry growth roughly tracked application growth.

AI is breaking that assumption. AI-assisted development, automated pipelines, and increasingly complex distributed systems are generating telemetry at a rate that far outpaces traditional software delivery. In that environment, a pricing model that scales linearly with signals quickly becomes a constraint.

If organizations want to adopt AI at the speed the market now expects, they need observability infrastructure that can absorb massive signal growth without the cost curve exploding alongside it. BYOC changes that equation by aligning infrastructure costs with the customer’s own cloud environment instead of charging per signal. The result is a cost model that can keep up with the telemetry realities of modern engineering.

3. Enterprise Infrastructure Alignment

Sophisticated Enterprise platform teams want visibility into instance sizing, autoscaling policies, and how external systems integrate with their internal standards. BYOC enables deeper integration with internal networking, IAM, and compliance controls. For many enterprise buyers, this is a hard requirement, not a preference.

Where BYOC Does Not Fit

BYOC is not a universal replacement for SaaS.

If you don't have strict data residency requirements or regulatory constraints, SaaS remains the simpler option. However, it's important to distinguish between BYOC implementations. When designed correctly, BYOC should introduce little to no additional operational overhead for the customer beyond what they already manage in their cloud environment.

The vendor is still responsible for running and operating the platform, including upgrades, orchestration, and lifecycle management. The customer's role is primarily providing the cloud boundary in which the data plane runs.

In practice, the complexity is shifted to the vendor. Operating across many isolated customer environments requires strong automation, infrastructure-as-code, and operational tooling.

For small deployments, BYOC may still introduce base infrastructure costs that a multi-tenant SaaS model can spread more efficiently across customers.

The takeaway: BYOC expands your deployment options. It doesn't replace SaaS.

Comparing Approaches: ClickHouse, Zilliz, and groundcover

Each company on the panel approached BYOC from a different starting point.

ClickHouse: SaaS and BYOC with Strong Separation

ClickHouse Cloud emphasizes strict control plane and data plane separation, keeping customer compute and storage in the customer's account while maintaining a consistent control experience across both deployment models.

Key characteristics:

• Strong isolation by default
• Minimal, encrypted cross-boundary communication
• Consistent UI and operational experience between SaaS and BYOC

This approach prioritizes security, regulatory compliance, and operational familiarity.

Zilliz: Evolving from SaaS to Enterprise BYOC

Zilliz started as a pure SaaS vector database before introducing BYOC in response to enterprise demand. Their architecture places the vector database and its dependencies in the customer VPC, with orchestration, upgrades, and workflow automation remaining in the control plane. Connectivity is handled through private networking mechanisms.

For Zilliz, BYOC unlocked AI workloads that couldn't move data outside the customer's network boundary.

groundcover: BYOC-Native from Day One

At groundcover, BYOC is foundational rather than a secondary offering. The design rests on three explicit tradeoffs we made upfront:

• Posture: Meet privacy, regulatory, and sovereignty requirements without compromise, which means no data ever leaves the customer's cloud account.
• Experience: Deliver a managed, SaaS-level experience even though the system runs in the customer's infrastructure. The operational burden stays on us, not the customer.
• Enablement: Align infrastructure costs with the customer's cloud economics, decoupling observability pricing from the unpredictability of ingestion-based billing.

We also made explicit trust decisions. There are no impersonation mechanisms by default. Access requires an explicit customer invitation, which reinforces the trust boundary inherent to BYOC.

The Operational Reality of BYOC

BYOC introduces real complexity for vendors. We handle Kubernetes upgrades, incident response, and day-two operations across many customer environments rather than a single shared platform. Some vendors use just-in-time access mechanisms for troubleshooting, which adds some latency but preserves security posture.

Cloud-native tooling becomes essential at scale: GitOps for deterministic rollouts, infrastructure as code for repeatability, and autoscaling frameworks to handle bursty data patterns.

One side effect worth noting: in distributed BYOC architectures, failures are often isolated to a specific customer environment rather than causing platform-wide outages. The blast radius is smaller by design.

AI Workloads Are Accelerating BYOC Adoption

AI is increasing data volume significantly. Training workloads, inference pipelines, and vector search systems move large datasets, and moving that data across cloud boundaries is expensive and inefficient. In many architectures, it makes more sense to bring computation closer to data rather than the reverse.

In observability, AI-generated code compounds this. It produces more telemetry, more logs, and more traces at a rate that makes volume control increasingly unrealistic. BYOC doesn't reduce data growth, but it makes the cost model survivable at scale.

Final Thoughts

BYOC is not just a deployment option. It represents a shift in trust boundaries, cost alignment, and data ownership.

For organizations without strict data residency requirements, SaaS remains the right default. For data-intensive, AI-driven, or heavily regulated environments, BYOC is increasingly a baseline expectation rather than a premium feature.

The core design challenge: preserve SaaS simplicity while returning cloud control to the customer. That balance is hard to get right, but when it works, it changes the operational and commercial relationship between vendors and enterprise infrastructure teams in ways that matter.

Check out the recording of our webinar to learn more.

Speakers: 

• Aashish Kohli, Product Management at ClickHouse
• Xiaofan(James) Luan, VP Engineering at Zilliz
• Noam Levy, Field CTO at groundcover