Enterprises building fraud, risk, recommendation, or search systems face three fundamental conditions: sensitive data must remain in-account, latency must be predictable, and control must stay with internal teams.
That’s why Chalk is designed to run in your cloud, inside your VPC, on your Kubernetes. Feature computation executes adjacent to your systems, not across networks you don’t own.
On Adventures in DevOps, Chalk co-founder Andrew Moreland describes why this approach is foundational to how we operate. Deploying inside customer accounts isn’t just an architectural preference, it’s a strategic differentiator that makes Chalk viable for enterprise workloads at scale.
Why “in your cloud” wins:
- Data stays in-account. Every component runs within your environment. Chalk can be configured for zero runtime access, so compliance, auditing, and governance remain entirely under your control.
- Latency that scales. By co-locating feature computation with your applications, Chalk reduces cross-cloud hops and delivers ultra low latency for real-time decisions.
- Correctness by design. Chalk’s data platform is inherently time-aware (temporal), preventing “future leakage” into training data and ensuring consistency across online and offline pipelines.
How the advantage compounds:
- No egress overhead. Compute and storage stay together, eliminating cross-account transfers and reducing interconnect costs.
- One control plane across clouds. Kubernetes normalize deployments.
- Upgrades on your schedule. We ship versioned Helm charts and SDKs. You choose when to adopt.
- Built-in moat at scale. Running on your cloud allows Chalk to sustain throughput, control p95s, and support complex feature logic without creating operational overhead.
Listen to the full story. Andrew dives deeper into how Chalk deploys inside customer clouds, manages IAM cleanly, and ensures ML feature computation is both fast and correct.
Watch the episode below:
Learn more about how Chalk deploys in your cloud.
