Tel Aviv, February 27, 2026 — PointFive, the pioneer of Cloud & AI Efficiency Management, announced DeepWaste™ AI — a standalone, full-stack AI cost optimization module designed to continuously optimize LLM services, GPU infrastructure, and AI data platforms across major cloud providers.
The AI Cost Challenge
As AI transitions from experimentation to production, inefficiencies span multiple layers — model selection, token consumption, routing logic, caching behavior, GPU utilization, retry patterns, and data platform orchestration all influence AI cost and performance. Traditional cloud optimization tools lack AI-specific analysis capabilities. DeepWaste™ AI addresses this gap.
Full-Stack AI Optimization
DeepWaste AI provides native, agentless connectivity to:
- AWS — Bedrock, SageMaker, and AI managed services
- Azure — Azure OpenAI, Azure ML, Cognitive Services
- GCP — Vertex AI and AI services
- Direct APIs — OpenAI and Anthropic
Beyond LLM services, the platform continuously optimizes GPU infrastructure by identifying underutilized or idle GPUs, instance-type mismatches, OS and driver misconfigurations, and hardware-to-workload misalignment.
With native Snowflake and Databricks support, DeepWaste AI extends optimization across AI data platforms, providing end-to-end coverage from data ingestion through inference.
Agentless and Privacy-Preserving
DeepWaste AI connects directly to cloud APIs, LLM service metrics, GPU telemetry, and billing systems — without agents, instrumentation, or code changes. By default, it operates using metadata, billing signals, performance metrics, and resource configuration data, without requiring access to raw inference logs.
Optional inference-level analysis can be enabled for deeper evaluation of prompt architecture and orchestration logic. Organizations control the depth of analysis, with optimization adapting accordingly.
Multi-Layer Detection
DeepWaste AI structures every invocation with task classification, routing context, cost attribution, and infrastructure alignment signals. It detects inefficiency across four core layers:
- Model & Routing Intelligence — Model-task mismatch, routing downgrade opportunities, batch vs. real-time routing misalignment, and workload benchmarking outliers
- Token & Prompt Economics — Prompt bloat, context window overprovisioning, output inflation from misconfigured max_tokens, and structural token waste patterns
- Caching & Reuse Optimization — Duplicate inference detection, underutilized native caching capabilities, and cache miss rate inefficiencies
- Infrastructure & Operational Leakage — Idle GPUs, instance-type mismatch, driver-level throughput limitations, retry-driven cost inflation, and latency outliers
Quantified Remediation
Each finding includes quantified savings estimates and implementation guidance, prioritized by financial impact and mapped to engineering and FinOps workflows. Teams can evaluate projected savings before acting and track realized improvements over time.
Executive Perspective
Alon Arvatz, CEO of PointFive, noted that AI workloads introduce a new category of operational complexity, and that DeepWaste AI equips organizations with the intelligence needed to scale AI efficiently across models, infrastructure, and data platforms while maintaining control.
Availability
DeepWaste™ AI is now available to PointFive customers.
About PointFive
PointFive pioneered Cloud & AI Efficiency Management, 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 reporting into operational discipline. Customers achieve sustained improvements in cost, performance, reliability, and engineering accountability at scale.