Architecture Teardown: How Replicate and Modal Host AI Models Using Kubernetes 1.34

Replicate and Modal have emerged as two of the most popular platforms for hosting and serving AI models, each catering to slightly different developer workflows. While Replicate focuses on simplified model deployment via its Cog containerization tool, Modal offers a code-first serverless platform for AI workloads. Both platforms rely heavily on Kubernetes 1.34 as their underlying orchestration layer, leveraging its cutting-edge features for GPU management, pod scheduling, and scalable inference. This teardown breaks down how each platform architects its hosting stack on K8s 1.34, highlighting shared patterns and key differentiators.

Replicate's K8s 1.34 Architecture

Replicate’s core workflow revolves around Cog, a tool that packages AI models into standardized Docker containers with pinned dependencies and model weights. When a user deploys a model, Replicate’s control plane creates a Kubernetes Deployment for each model version, with pods running the Cog container. K8s 1.34’s Dynamic Resource Allocation (DRA) for GPUs is a critical component here: unlike legacy device plugin approaches, DRA allows Replicate to request fractional GPU resources, pack multiple small inference workloads onto a single GPU, and dynamically adjust allocations based on real-time demand.

Replicate also leverages K8s 1.34’s improved scheduler latency, which reduces pod startup time for inference requests by up to 30% compared to older K8s versions. For model weight storage, Replicate uses a combination of container image caching (via node-local image registries) and persistent volumes for large model weights, with K8s 1.34’s CSI driver improvements reducing mount latency. To handle traffic spikes, Replicate uses the K8s Horizontal Pod Autoscaler (HPA) v2 with custom metrics for request queue depth, paired with K8s 1.34’s priority preemption to bump low-priority batch training jobs in favor of high-priority inference pods.

Modal's K8s 1.34 Architecture

Modal takes a code-first approach: developers decorate Python functions with @stub.function(gpu="A100") to request GPU resources, and Modal handles provisioning. Under the hood, each Modal function maps to a K8s Deployment, with pods running sandboxed containers (using gVisor, paired with K8s 1.34’s enhanced seccomp and SELinux profiles for isolation). Modal’s custom scheduler integrates with K8s 1.34’s scheduler framework to optimize for GPU topology: it uses K8s 1.34’s Topology Manager to align GPUs with nearby CPU cores and memory, reducing NUMA-related latency for inference workloads.

Modal also leans on K8s 1.34’s cgroups v2 default support to enforce strict resource isolation between tenant workloads, preventing noisy neighbor issues in its multi-tenant environment. For scaling, Modal uses K8s 1.34’s Custom Metrics API to feed function invocation queue depth and GPU utilization into its autoscaler, with support for scale-to-zero when no requests are pending. Ephemeral containers, promoted to stable in K8s 1.34, allow Modal’s engineering team to debug live inference pods without disrupting running workloads.

Shared K8s 1.34 Features

Both platforms rely on a core set of K8s 1.34 features to deliver reliable, cost-effective AI hosting:

• Dynamic Resource Allocation (DRA): Fine-grained GPU sharing, fractional GPU requests, and support for mixed GPU workloads (inference + training) on the same node.
• cgroups v2: Better resource isolation, support for hierarchical memory limits, and improved I/O throttling for inference pods.
• Scheduler Framework Improvements: Reduced scheduling latency, better scoring for GPU nodes, and support for custom scheduling plugins.
• Custom Metrics API: Integration with autoscalers for scaling based on inference-specific metrics (latency, queue depth, GPU utilization).
• Ephemeral Containers: Live debugging of running pods without terminating workloads, critical for troubleshooting production inference issues.

Key Architectural Differences

While both use K8s 1.34 as a foundation, their higher-level architectures reflect their target use cases:

• Model-centric vs. Function-centric: Replicate treats each model version as a standalone K8s Deployment, while Modal maps individual Python functions to deployments. This makes Replicate better for pre-trained model hosting, and Modal better for custom inference logic with pre/post-processing.
• Containerization: Replicate requires Cog for containerization, which enforces standardized model packaging. Modal uses its own container builder that integrates with its Python SDK, allowing more flexibility for custom dependencies.
• Scaling Logic: Replicate scales based on HTTP request volume to model endpoints, while Modal scales based on function invocation queue depth and GPU utilization for arbitrary Python workloads.

Challenges and Tradeoffs

Adopting K8s 1.34 comes with its own set of challenges for both platforms. Upgrading from older K8s versions requires validating compatibility with NVIDIA GPU drivers (525+ is required for K8s 1.34 DRA support) and container runtimes like containerd. Cold start latency remains a concern: both platforms use pod pre-warming and K8s 1.34’s faster image pulling to reduce startup times, but large model weights can still add seconds to cold starts. Cost management relies on K8s 1.34’s resource quotas and limit ranges to prevent runaway pod spawning, paired with node auto-scaling to match demand.

Conclusion

Replicate and Modal demonstrate the power of Kubernetes 1.34 for AI model hosting, leveraging its advanced GPU management, scheduling, and scaling features to deliver reliable, low-latency inference. While their higher-level abstractions differ, their shared use of K8s 1.34’s core features highlights the orchestration platform’s growing role as the backbone of modern AI infrastructure. As K8s continues to evolve, expect both platforms to adopt newer features like GPU partitioning and serverless integration to further optimize AI workload hosting.