Tel Aviv, February 26th, 2026- PointFive today announced DeepWaste™ AI Profiler, a new capability designed to bring measurable, workload-level efficiency to production AI environments across AWS Bedrock, Azure OpenAI, and GCP Vertex AI.
AI Profiler extends PointFive’s continuous AI optimization platform with a new layer of behavioral analysis focused on how AI models are actually invoked, routed, and configured in production.
As AI adoption scales, cost and performance are increasingly shaped by prompt structure, token allocation, model routing decisions, caching configuration, and retry behavior. Traditional cloud optimization tools were not built to analyze these AI-specific execution patterns.
DeepWaste™ AI Profiler is.
AI Profiler connects agentlessly to native cloud data sources and ingests AI workload signals, including model usage, token consumption, performance metrics, billing data (CUR), and resource metadata.
Using this unified data foundation, DeepWaste™ detects structural inefficiencies without requiring access to raw inference logs.
By analyzing invocation patterns, model selection behavior, token configuration, and workload-level signals, AI Profiler surfaces high-impact optimization opportunities while preserving customer privacy and minimizing data access requirements.
For organizations seeking deeper analysis, AI Profiler also supports optional inference-level reviews with explicit customer consent.
DeepWaste™ evaluates AI workloads across multiple optimization dimensions like:
Each detection is grounded in invocation-level behavioral signals rather than surface-level billing anomalies. The result is a precise understanding of how AI services operate, and where efficiency can be improved.
DeepWaste™ AI Profiler does not stop at analysis. Every detected inefficiency is translated into a quantified savings opportunity with clear remediation guidance.
Recommendations are prioritized by financial impact and mapped directly to engineering and FinOps workflows. Teams can evaluate projected savings before implementation and track realized improvements over time.
This transforms AI efficiency from reactive cost monitoring into a continuous, measurable discipline.
As enterprises move from experimentation to production AI, understanding unit economics at the workload level becomes critical. Efficiency is no longer about monitoring spend- it requires analyzing how AI services behave at execution time.
DeepWaste™ AI Profiler provides:
“AI workloads introduce a new category of operational complexity,” said Alon Arvatz, CEO of PointFive. “DeepWaste™ AI Profiler gives organizations the visibility and behavioral intelligence required to scale AI efficiently, without sacrificing control.”
DeepWaste™ AI Profiler is now available to PointFive customers.
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About PointFive
PointFive pioneered Cloud Efficiency Posture Management (CEPM), redefining how enterprises continuously optimize cloud, infrastructure, and AI environments. By combining a real-time cloud and AI data fabric with AI-driven detection and guided remediation, PointFive transforms efficiency from a reporting exercise into an operational discipline. Customers achieve sustained improvements in cost, performance, reliability, and engineering accountability, at scale.
To learn more, book a demo.
