Multi-Cloud, Multi-Model Optimization: DeepWaste AI Connects Across AWS, Azure, GCP, and Direct APIs

On February 27, 2026, PointFive announced DeepWaste™ AI, a full-stack optimization module designed to operate across AWS, Azure, GCP, and direct OpenAI and Anthropic APIs. A central theme of the launch is coverage: DeepWaste AI is built to operate across major cloud providers and direct model APIs, reflecting how production AI is increasingly deployed in real organizations.

The Multi-Provider Reality of Production AI

Few enterprises run AI in a single place. Teams may use managed services for speed and compliance, direct APIs for flexibility, and multiple clouds for regional, organizational, or architectural reasons. As AI adoption scales, inefficiency becomes layered across the execution stack, model selection, token consumption, routing logic, caching behavior, GPU utilization, retry patterns, and data platform orchestration. When those layers span multiple providers, optimization becomes harder: signals are scattered, and changes made in one environment may shift costs in another.

PointFive's argument is that traditional cloud optimization tools weren't built for this AI-specific, multi-layer execution behavior. DeepWaste AI is positioned as a module that treats AI as a system across providers.

Connectivity Across Clouds and Direct APIs

PointFive says DeepWaste AI provides native, agentless connectivity across:

• 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

This list matters because it covers both provider-managed services and direct consumption patterns. DeepWaste AI is designed to continuously optimize AI workloads regardless of where they run, applying the same full-stack lens to cost and performance signals.

Agentless Deployment to Support Consistency

PointFive emphasizes that DeepWaste AI connects directly to cloud APIs, LLM service metrics, GPU telemetry, and billing systems without agents, instrumentation, or code changes. By default, optimization runs using metadata, billing signals, performance metrics, and resource configuration data without requiring access to raw inference logs. This agentless, metadata-first approach is meant to reduce deployment friction and support consistent rollout across environments where instrumentation may vary or be restricted.

For organizations that choose to go deeper, optional inference-level analysis can be enabled to evaluate prompt architecture and orchestration logic, with customers controlling the depth of analysis.

Full-Stack Scope Across LLMs, GPUs, and Data Platforms

DeepWaste AI's multi-cloud message is paired with a full-stack message. Beyond optimizing LLM services, the module continuously optimizes GPU infrastructure by identifying underutilized or idle GPUs, instance-type mismatches, OS and driver misconfigurations, and hardware-to-workload misalignment. PointFive is positioning GPU efficiency as a shared challenge across clouds, not a single-provider tuning exercise.

On the data platform side, native support for Snowflake and Databricks extends optimization across AI data platforms, covering workflows from data ingestion through inference. In a multi-provider environment, data platforms can be the connective tissue between different model services, making end-to-end visibility a practical requirement.

A Four-Layer Detection Model for Cross-Provider Behavior

DeepWaste AI structures and enriches invocations with task classification, routing context, cost attribution, and infrastructure alignment signals. It detects inefficiency across four layers:

1. Model & Routing Intelligence

2. Token & Prompt Economics

3. Caching & Reuse Optimization

4. Infrastructure & Operational Leakage

PointFive says detections are grounded in unified workload signals rather than surface-level billing anomalies, which is especially relevant in multi-cloud settings where billing formats and reporting structures differ by provider.

Quantified Remediation Across Teams

PointFive says every finding includes a quantified savings estimate and clear implementation guidance. Recommendations are prioritized by financial impact and mapped directly to engineering and FinOps workflows. In a multi-cloud context, this mapping is meant to help teams coordinate changes across boundaries, routing adjustments, token policy updates, caching improvements, GPU provisioning changes, and pipeline orchestration tweaks, while tracking outcomes over time.

DeepWaste AI Is Available Now

"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 now available to PointFive customers.