Vision
Polyaxon's goal is to give teams one operational platform for machine learning systems, LLM applications, and AI agents.
That means a single platform where teams can run work, track evidence, evaluate behavior, observe production systems, manage versions, and govern access across the machine learning lifecycle and the AI agent lifecycle.
AI work should not be restricted to companies that can afford to stitch together an internal platform from scratch. It should be possible for teams in every industry to build useful systems without turning operations into a permanent integration project.
The problem
AI and ML teams lose time because context is split across too many places. Training runs live in experiment trackers. Prompts live in code or documents. Traces live in observability tools. Evaluation results live in notebooks. Models and artifacts live in registries. Access policy lives somewhere else.
That fragmentation makes review harder than it needs to be. A team should be able to answer basic questions without running a forensic investigation:
- Which data, code, prompt, model, tool, or index produced this behavior?
- Which tests or evaluations passed before release?
- Which version is running now?
- Who had access, who changed it, and what happened afterward?
- What should we fix next?
The problem is not a lack of tools. The problem is that the tools do not share enough evidence.
What we are building
Polyaxon connects the work behind AI and ML systems: workloads, runs, artifacts, models, prompts, traces, evaluations, dashboards, automations, permissions, and audit history.
For machine learning teams, this means reproducible training, experiment tracking, orchestration, model management, artifact lineage, and scheduled workflows.
For LLM and agent teams, this means prompt management, application tracing, evaluation datasets, live evaluators, tool-call visibility, cost and latency tracking, and release evidence.
For the broader organization, this means different personas can work from the same operational record without needing the same interface or the same level of technical detail.
Product principles
Polyaxon is open and self-hostable. Teams should be able to run the platform near their compute, data, models, and policies.
Polyaxon is Kubernetes-native. AI and ML work needs real execution control: jobs, services, queues, agents, schedules, DAGs, resources, and secrets.
Polyaxon favors evidence over demos. A good demo is cheap. A release needs tracked inputs, outputs, versions, evaluations, traces, and review history.
Polyaxon integrates with existing systems. One platform does not mean one tool owns every model provider, data warehouse, vector store, serving stack, or business application.
Polyaxon attaches governance to the work. RBAC, connections, audit logs, retention, and service accounts should be part of the lifecycle, not a separate spreadsheet.
Polyaxon gives different roles different views. Data scientists, AI application engineers, QA teams, platform engineers, domain owners, release managers, and executives do not need the same screen. They do need the same evidence.
Short term
In the short term, we are focused on making the core lifecycle easier to adopt and review:
- Better tracking and run comparison for ML work.
- Stronger orchestration for jobs, services, schedules, DAGs, and repeatable workflows.
- Clearer model, artifact, component, and prompt versioning.
- Better observability for LLM applications and agents.
- More useful evaluation workflows for datasets, experiments, human review, custom scores, and live evaluators.
- Documentation that explains how the platform fits real use cases instead of only listing features.
Long term
Long term, we want Polyaxon to make AI operations less manual and less fragile.
That means stronger automation around evaluations, releases, alerts, retraining, reindexing, approvals, rollback, and incident review. It also means better lifecycle intelligence: surfacing stale models, risky prompt changes, failing evals, expensive traces, weak coverage, and operational drift before they become production incidents.
We also want better executive and cross-team visibility. Leaders should be able to see which projects are moving, which systems are risky, where cost is going, and which releases need decisions without inventing a reporting process outside the platform.
The long-term product should make technical work easier to inspect without flattening it into fake simplicity. AI systems are complicated. The platform should preserve that complexity where it matters and hide it where it does not.
What we will not do
We will not pretend one product should own all code, data, infrastructure, model providers, vector stores, warehouses, serving systems, and business apps.
We will not sell "agents" as a strategy. An agent is an implementation choice. The strategy is the job the system must do, the evidence that it works, and the controls that keep it safe when it does not.
We will not optimize for feature checklists at the cost of lifecycle coherence. The work is to make AI and ML systems easier to build, inspect, operate, and govern.