PointFive Launches DeepWaste™ AI for Full-Stack AI Cost Optimization

Tel Aviv, February 27, 2026 – PointFive has announced the release of DeepWaste™ AI, a latest module designed to continuously optimize the cost and performance of artificial intelligence workloads across major cloud providers. The tool addresses the increasing complexity of production AI, moving beyond simple volume metrics to analyze the interconnected decisions that govern AI execution.

The Challenge of Scaling AI Costs

As AI adoption scales from experimentation to production, inefficiency is no longer confined to a single layer. Factors such as model selection, token consumption, routing logic, caching behavior, GPU utilization, retry patterns, and data platform orchestration all significantly impact AI cost and performance. Traditional cloud optimization tools were not designed to analyze this AI-specific execution stack, creating a need for a more holistic approach.

DeepWaste™ AI: Full-Stack Optimization

DeepWaste AI provides connectivity across major cloud platforms, including:

• AWS (Bedrock, SageMaker, and AI managed services)
• Azure (Azure OpenAI, Azure ML, Cognitive Services)
• GCP (Vertex AI and AI services)
• OpenAI and Anthropic direct APIs

Beyond Large Language Model (LLM) services, DeepWaste AI optimizes GPU infrastructure by identifying underutilized or idle GPUs, instance-type mismatches, OS and driver misconfigurations, and hardware-to-workload misalignment. It too extends optimization to AI data platforms with native support for Snowflake and Databricks, providing conclude-to-end coverage from data ingestion through inference.

Agentless Design and Privacy

DeepWaste AI connects directly to cloud APIs, LLM service metrics, GPU telemetry, and billing systems without requiring agents, instrumentation, or code changes. Optimization runs using metadata, billing signals, performance metrics, and resource configuration data, without accessing raw inference logs by default, prioritizing privacy.

A Four-Layer Detection Model for Inefficiency

DeepWaste AI utilizes a four-layer model to detect inefficiency across the AI execution stack:

• Model & Routing Intelligence: Identifies model-task mismatches, downgrade opportunities, and misalignment between batch and real-time processing.
• Token & Prompt Economics: Detects prompt bloat, context window overprovisioning, and inefficient parameter configurations.
• Caching & Reuse Optimization: Identifies duplicate inference, underused caching, and cache miss rate inefficiencies.
• Infrastructure & Operational Leakage: Detects idle GPUs, instance mismatches, driver-level throughput limits, and retry-driven cost inflation.

Turning Insights into Actionable Recommendations

DeepWaste AI provides quantified savings estimates and clear implementation guidance for identified inefficiencies. Recommendations are prioritized by financial impact and mapped to engineering and FinOps workflows, allowing teams to evaluate projected savings and track improvements over time. This shifts the focus from reactive monitoring to continuous optimization.

The Future of AI Cost Management

“AI workloads introduce a new category of operational complexity,” said Alon Arvatz, CEO of PointFive. “DeepWaste AI gives organizations the intelligence required to scale AI efficiently, across models, infrastructure, and data platforms, without sacrificing control.”

DeepWaste AI is currently available to PointFive customers.