Polyaxon vs DeepInfra
Compare Polyaxon and DeepInfra across Kubernetes workloads, hosted inference, private models, batch processing, GPU instances, sandboxes, agents, and lifecycle ownership.
Which platform fits
Choose Polyaxon when
Your team must run and govern varied AI workloads across organization-controlled Kubernetes infrastructure.
Choose DeepInfra when
You want hosted model, sandbox, or agent APIs—or dedicated GPU capacity—without operating the underlying service infrastructure.
Use both when
Polyaxon coordinates the workflow and lifecycle record while bounded inference, batch, or private-model stages run on DeepInfra.
Capability comparison
This table describes product scope and operating responsibility. It is not a benchmark or a count of integrations.
Primary scope
Polyaxon
A Kubernetes-native control plane for AI workloads, scheduling, pipelines, tracking, registries, and operational policy.
DeepInfra
A provider-operated AI cloud for shared and private inference, batch requests, GPU containers, sandboxes, and hosted agent frameworks.
Training and custom workloads
Polyaxon
Runs arbitrary containerized training, fine-tuning, data, evaluation, and distributed workloads on connected Kubernetes clusters.
DeepInfra
GPU Instances provide dedicated containers with SSH access for training, fine-tuning, and custom workloads; the customer operates the training framework and process.
Hosted inference
Polyaxon
Runs model servers as Kubernetes services while the organization chooses the runtime, accelerator, networking, scaling, and deployment policy.
DeepInfra
Provides shared model inference through OpenAI-compatible and native APIs across language, embedding, image, video, speech, and other model types.
Private models
Polyaxon
Packages custom inference runtimes as containers and runs them on connected infrastructure with the surrounding workflow and metadata.
DeepInfra
Deploys custom Hugging Face models and existing LoRA adapters on dedicated GPUs with autoscaling and an OpenAI-compatible endpoint.
Batch processing
Polyaxon
Jobs, matrices, DAGs, schedules, distributed runtimes, queues, retries, and approvals coordinate arbitrary batch workloads.
DeepInfra
Its Batch API processes supported inference requests asynchronously through an OpenAI-compatible file, batch, and result workflow.
Sandboxes and agents
Polyaxon
Sandboxes, jobs, services, pipelines, and tracked runs share the same Kubernetes workload and lifecycle model.
DeepInfra
Offers isolated Linux microVM sandboxes and managed instances of selected open-source agent frameworks as separate API products.
Infrastructure ownership
Polyaxon
The organization connects and governs its Kubernetes clusters, namespaces, GPUs, storage, networking, images, and scheduling policy.
DeepInfra
DeepInfra operates hosted endpoints and sandboxes; GPU Instances expose dedicated containers with SSH access rather than a customer-operated Kubernetes control plane.
Workflow and lifecycle
Polyaxon
DAGs, schedules, hooks, retries, approvals, experiments, artifacts, models, components, and lineage connect heterogeneous workloads.
DeepInfra
APIs manage model requests, batches, deployments, containers, sandboxes, and hosted agents; cross-system orchestration and ML lifecycle records remain with the caller.
Operations and data
Polyaxon
Workload logs, metrics, events, artifacts, lineage, and cluster-aware state are available within the platform and customer-selected infrastructure.
DeepInfra
Provides inference and deployment logs, usage and request-cost data, and documented data-handling rules that vary for standard, bulk, image, and third-party-model requests.
Best fit
Polyaxon
Platform teams standardizing diverse AI workloads and governance across their Kubernetes estate.
DeepInfra
Application teams prioritizing quick access to managed inference and adjacent AI services, or dedicated GPU containers, through a single provider.
Relevant product previews
These previews may affect the decision, but they are not included as generally available capabilities in the comparison above.
Private beta
AI gateway
Polyaxon is testing an AI gateway with private-beta customers. Teams evaluating gateway coverage can request access and validate it against their model, provider, policy, and traffic requirements.
Preview scope and timelines may change.
Ask about private accessWhen each platform fits
Choose Polyaxon when
- Kubernetes is the strategic execution layer and the organization needs explicit control over workload images, resources, queues, networking, and data location.
- Training, evaluation, data preparation, services, distributed compute, pipelines, tracking, and registries must share one operating model.
- Portability across infrastructure providers and durable lifecycle metadata matter more than consuming a single provider's managed AI services.
Choose DeepInfra when
- The primary requirement is a hosted model API with a broad catalog and OpenAI-compatible access.
- Teams want a private autoscaling model endpoint, an isolated code sandbox, a hosted agent framework, or an SSH-accessible GPU container.
- Provider-operated infrastructure and API-level integration are preferable to operating Kubernetes, model servers, and the surrounding platform.
Using Polyaxon with DeepInfra
A Polyaxon pipeline can prepare data, run evaluations, enforce approvals, and call DeepInfra for online or batch inference. DeepInfra can own a private model endpoint while Polyaxon records the surrounding inputs, outputs, artifacts, model and deployment identifiers, and operational decisions.
- Record the DeepInfra model, deployment, batch, request, sandbox, or agent identifier on the corresponding Polyaxon run.
- Assign retries, cancellation, spending limits, output persistence, and final-status reconciliation to one system at every API boundary.
- Treat DeepInfra GPU Instances as external container capacity unless a supported Kubernetes integration has been separately designed and validated.
Evaluation plan
Separate workloads from managed services
Inventory training, data, evaluation, online inference, batch inference, sandboxes, agents, pipelines, tracking, and governance as distinct requirements.
Exercise a production-shaped path
Test the expected model or workload with representative data, concurrency, latency, throughput, startup, failure, cancellation, and output handling.
Compare the operating boundary
Score infrastructure control, identity, data handling, observability, portability, lifecycle records, support, and complete cost across the full workflow.
Sources
Product capabilities change. Follow the linked documentation for current details.
Polyaxon overview
Kubernetes workloads, scheduling, tracking, pipelines, distributed runtimes, and registries.
Polyaxon workload runtimes
Jobs, services, distributed workloads, DAGs, matrices, and declarative execution.
Polyaxon sandboxes
Interactive development environments governed through the Polyaxon workload model.
DeepInfra overview
Hosted inference, private models, GPU rental, supported model types, and service boundaries.
DeepInfra API reference
OpenAI-compatible endpoints and native inference endpoints across supported model types.
DeepInfra Batch API
Asynchronous file upload, batch submission, status, cancellation, and result retrieval.
DeepInfra private models
Dedicated custom-model and LoRA deployments, GPU choices, autoscaling, and billing boundaries.
DeepInfra GPU Instances
Dedicated GPU containers, SSH access, custom images, lifecycle, and training use cases.
DeepInfra Sandboxes
Isolated Linux microVMs, command execution, file movement, lifecycle, and API access.
DeepInfra hosted agents
Managed instances of supported open-source agent frameworks and their operating model.
DeepInfra data privacy
Data retention, logging, bulk inference, generated-image, and third-party-model exceptions.
DeepInfra inference logs
Querying inference logs by deployment and time range.
Compare against your requirements
We can map your current scheduler, tracking stack, storage, GPU policy, and migration constraints before you commit to a platform change.