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OptScale — FinOps
FinOps overview
Cost optimization:
AWS
MS Azure
Google Cloud
Alibaba Cloud
Kubernetes
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OptScale — MLOps
ML/AI Profiling
ML/AI Optimization
Big Data Profiling
OPTSCALE PRICING
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Acura — Cloud migration
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Database replatforming
Migration to:
AWS
MS Azure
Google Cloud
Alibaba Cloud
VMWare
OpenStack
KVM
Public Cloud
Migration from:
On-premise
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Acura — DR & cloud backup
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Migration to:
AWS
MS Azure
Google Cloud
Alibaba Cloud
VMWare
OpenStack
KVM

Track and compare ML/AI model performance with dynamic leaderboards

Continuously evaluate your model performance using standardized metrics and custom KPIs — and make data-driven decisions with confidence
ML/AI Leaderboards
metrics under control

Evaluation metrics and KPI tracking

Model comparison

Dynamic leaderboards and model comparison

Champion or Candidate testing workflow

Champion/candidate testing workflow

Audit-ready evaluation reports

Audit-ready evaluation reports

Evaluation metrics & KPI tracking

Kiroframe provides an automated evaluation framework that collects and compares model metrics — including accuracy, loss, precision, recall, and more — across training runs, environments, and dataset versions.

With support for custom KPIs and thresholds, teams can benchmark models against production goals and ensure consistency across experiments with the best candidate for production deployment.

Evaluation metrics and KPI tracking
Dynamic leaderboards and model comparison

Dynamic leaderboards and model comparison

Gain instant visibility into model performance with built-in leaderboards that auto-rank models by your selected metrics. Use tags, filters, and versioning to compare experiments across:

  • Model architectures

  • Hyperparameter sets

  • Datasets and environments

The leaderboard view helps you quickly identify top-performing configurations and validate model changes over time.

Champion/candidate testing workflow

Support continuous model evaluation using champion/candidate methodology — automatically promoting new model versions if they outperform existing ones under defined conditions.

This option helps data science and MLOps teams deploy with confidence and reduce regression risk in production environments.

Champion:candidate testing workflow
Audit-ready evaluation reports

Audit-ready evaluation reports

Kiroframe generates structured evaluation logs and visual reports for every training run, enabling compliance, reproducibility, and knowledge transfer across teams.

Reports include metric trends, model metadata, and evaluation context — all of which are available via the UI or API.

Supported platforms

aws
ms azure logo
google cloud platform
Alibaba Cloud Logo
Kubernetes
databricks
PyTorch
kubeflow
TensorFlow
spark-apache