Solutions Built for ML Cost Optimisation
From smart detectors to automated guardrails, MLMind equips your team with everything needed to eliminate waste and maximise the return on your compute investment.
Detect Hidden Waste
OOM Loop Detection
Automatically identifies runs that repeatedly fail due to out‑of‑memory errors. Save GPU hours by alerting or stopping jobs that will never succeed without a configuration change.
Duplicate Run Detection
Recognise when experiments are being rerun with identical hyperparameters or code. Avoid paying twice for the same insight and encourage teams to iterate thoughtfully.
No Artifact Detection
Flag runs that finish without producing any model artifacts or checkpoints. Use this signal to troubleshoot upstream issues and prevent unnoticed waste.
Custom Rules
Define your own detection rules based on duration, utilisation, dataset or any metadata. Our flexible API lets you tailor the system to your workflows.
Enforce Guardrails & Automate Decisions
Warn Mode
Send real‑time alerts to Slack, email or your webhook when a waste pattern is detected. Provide context and guidance so engineers can take corrective action.
Stop Mode
Automatically terminate wasteful or misconfigured runs when confidence exceeds your defined threshold. Prevent runaway costs without manual intervention.
Block Mode
Disallow new runs that match high‑risk patterns until approval. Ensure compliance with internal budget policies and avoid overspending during busy periods.
Dry‑Run & Simulation
Validate your policies before enforcement. Simulate warnings and stops to fine‑tune thresholds and minimise false positives.
Dashboards & Reporting
Overview Dashboard
Visualise your estimated waste, GPU hours wasted, guard actions and top waste reasons at a glance. Align finance and engineering with clear, actionable metrics.
Pipelines Dashboard
Drill down by pipeline to see total GPU hours, waste percentage, latest findings and guard status. Quickly spot which experiments are burning budget.
Findings Timeline
Investigate every incident of waste. Filter by reason, severity, date or confidence and export the data for compliance or deeper analysis.
Reports & Export
Generate daily, weekly or monthly reports to track trends over time. Export JSON or CSV files for integration with BI tools. Compare periods before and after guard enforcement.
Seamless Integration & Deployment
API & SDK
Send your run metadata and logs to MLMind with a simple REST API or integrate using our SDK. No intrusive agents or code changes required.
Kubernetes‑Ready
Deploy the platform as containers on your own Kubernetes cluster (EKS, GKE, AKS or on‑prem). Helm charts and manifest examples are provided for quick installation.
Multi‑Tenant Architecture
Isolate data and policies by tenant. Whether you’re a platform team serving multiple groups or a SaaS provider, MLMind scales securely across customers.
Marketplace & SaaS
Operate MLMind as a managed SaaS, or distribute it via cloud marketplaces. Choose the deployment that best fits your procurement and governance requirements.
Enterprise Ready & Secure
Hardened Containers
Follow Kubernetes best practices with non‑root containers, read‑only filesystems, and resource limits. Built‑in NetworkPolicies and Gatekeeper policies enforce security.
RBAC & Secrets Management
Use fine‑grained roles and AWS/GCP secrets managers to keep credentials safe. External secrets are synced at runtime – no secrets in Git.
Observability
Export metrics to Prometheus and traces via OpenTelemetry. Monitor latency, memory and detector performance with dashboards and alerts.
Compliance & SLA
Meet industry standards with audit logs, encryption in transit and at rest, and configurable retention policies. Enterprise plans include dedicated support and SLA.
Advanced Capabilities
GPU Sharing & MIG Support
Our platform leverages GPU time‑slicing and NVIDIA’s Multi‑Instance GPU (MIG) technologies to maximise utilisation. Time‑slicing can run multiple inference jobs on a single GPU and deliver up to 90% cost savings【164117429400888†L134-L149】. MIG partitions a single GPU into isolated instances, allowing up to seven workloads to run simultaneously【164117429400888†L140-L197】. Combined, these techniques unlock tens of thousands of dollars in monthly savings for large deployments【164117429400888†L148-L149】.
Real‑Time Cost Attribution & Forecasting
We map costs down to individual runs and models, then forecast future spend based on utilisation and training progress. By connecting consumption to pipelines, datasets and hyperparameters, teams can predict budget overruns and reallocate resources proactively【64704301150421†L191-L212】.
Multi‑Cloud FinOps
Manage AI spend across AWS, Azure and Google Cloud from a single view. Each provider uses its own pricing models, billing exports and tagging conventions【265498857832249†L110-L130】. MLMind harmonises cost data and governance so your teams don’t juggle multiple dashboards or reactive alerts【265498857832249†L123-L130】.
Proactive AI Governance
Beyond detecting waste, MLMind recommends capacity right‑sizing, reserved capacity strategies and efficient data pipelines. As FinOps practitioners juggle many capabilities and demand grows for AI spend management【583035027031957†L270-L290】, our guidance helps scale governance and drive continuous improvement.
Transform Your AI Operations
Learn how MLMind can seamlessly integrate into your workflows and drive savings immediately. Our pay‑as‑you‑save model means you only pay 10 % of the money we help you save – the rest stays in your budget.