Route ML jobs to regional infrastructure with Polyaxon
Configure regional execution with Polyaxon agents, queues, and storage connections, then measure data-transfer costs without assuming same-region traffic is free.
InfrastructureSchedulingGuides
Latest articles
The latest guides, tutorials, and product updates.
Unify hybrid AI platform operations
Operate AI workloads across clusters and clouds with central intent, local execution, placement policy, consistent identity, connected evidence, and failure-aware control.
Hybrid CloudInfrastructureMLOps
Reduce the cost of self-hosted ML workloads
Reduce self-hosted ML costs with outcome-based accounting, right-sized resources, elastic capacity, interruption-ready workloads, local data paths, and deliberate retention.
InfrastructureKubernetesScheduling
What is sovereign AI? Control across the AI lifecycle
Define sovereign AI as control over data, models, compute, operations, providers, and evidence, then implement it with Kubernetes.
InfrastructureKubernetesSecurity
What are your ML jobs connecting to?
Trace image pulls, Git clones, S3 and GCS access, Hugging Face downloads, and artifact uploads across the lifecycle of Kubernetes jobs and sandboxes.
KubernetesObservabilityInfrastructure
Kubernetes for AI agents: A platform engineering guide
Design a Kubernetes platform for AI agents with clear workload boundaries, durable state, scoped access, resource controls, and end-to-end evidence.
AI AgentsKubernetesInfrastructure
Design reliable self-hosted ML infrastructure
Build self-hosted ML infrastructure around explicit failure domains, durable queues and artifacts, eligible failover, actionable telemetry, and tested recovery.
InfrastructureKubernetesMonitoring
ML infrastructure explained for business teams
Understand what ML infrastructure pays for, how it affects delivery and reliability, and how to evaluate an investment using measurable workflow outcomes.
MLOpsInfrastructureGuides
Place ML workloads close to their data
Design region-aware ML execution around dataset, registry, model, cache, artifact, and service locality without confusing proximity with data residency.
InfrastructureKubernetesGuides
Docker build caching for ML workloads on Kubernetes
Reduce container build and startup time for ML jobs with reusable dependency layers, persistent build caches, and deliberate image and model caching.
DockerKubernetesInfrastructure
Design open infrastructure for portable AI workloads
Keep AI workloads portable with explicit execution contracts, open packaging and telemetry, hardware abstraction, data boundaries, and tested migration paths.
InfrastructureKubernetesMLOps
Run ML workloads on your existing Kubernetes cluster
Plan a Polyaxon deployment on an existing Kubernetes cluster, covering GPUs, storage, identity, networking, workload ownership, and recovery.
KubernetesInfrastructureGuides
Self-hosted vs. managed AI inference
Choose between self-hosted and managed AI inference using data policy, model access, latency, utilization, reliability, staffing, and cost per accepted outcome.
InferenceInfrastructureFinops
Scale agentic AI without breaking the infrastructure
Scale AI agents with admission control, dependency-aware concurrency, durable state, bounded authority, backpressure, and outcome-based capacity planning.
AI AgentsInfrastructureReliability
GPU sharing on Kubernetes: MIG vs. time-slicing
Compare NVIDIA MIG and GPU time-slicing on Kubernetes, including memory isolation, resource names, scheduling behavior, and workload fit.
GPUKubernetesInfrastructure
Estimate the operating cost of AI sandboxes
Estimate AI sandbox operating costs from measured active and idle resources, retries, storage, and engineering ownership using Polyaxon workload evidence.
AI AgentsCost OptimizationInfrastructure
Evaluate AI agent infrastructure reliability
Evaluate AI agent infrastructure reliability through task-level outcomes, recovery behavior, Polyaxon execution evidence, and explicit operational ownership.
AI AgentsReliabilityInfrastructure
Design data-residency controls for AI execution
Map AI execution data paths to Polyaxon compute placement, connections, artifacts, external providers, and documented residency controls.
SecurityData GovernanceInfrastructure
Compare serverless and dedicated LLM inference with Polyaxon
Use Polyaxon components, matrices, tracking, and run comparisons to evaluate serverless model APIs against dedicated inference services on quality, latency, and cost.
LLMOpsInferenceInfrastructure
Choose execution patterns for AI agents with Polyaxon
Use Polyaxon jobs, sandbox-enabled services, schedules, and tracking to match agent tasks to their execution, persistence, and capacity requirements.
AI AgentsInfrastructureOrchestration
Choose compute platforms for AI agent jobs
Choose compute platforms for AI agent jobs by execution shape, state, resource needs, and operational responsibility, using Polyaxon as the workload context.
AI AgentsComputeInfrastructure
Prepare an AI platform for next-generation GPUs
Make AI platforms ready for new accelerator generations through portable workload contracts, device-aware scheduling, topology, storage, compatibility, and migration evidence.
GPUKubernetesInfrastructure
Define trusted execution boundaries for AI workloads
Distinguish confidential computing from workload isolation, and define how Polyaxon scheduling and evidence fit a trusted execution design.
SecurityInfrastructureAI Agents
Five shifts shaping enterprise AI platforms
Plan enterprise AI platforms around measurable outcomes, heterogeneous compute, durable agents, cost per successful task, and continuous governance.
MLOpsInfrastructureStrategy