AI workloads don’t behave like traditional cloud infrastructure. They are driven by model selection, token consumption, routing logic, prompt structure, caching behavior, and retry patterns. These variables directly determine cost and performance. Most cloud optimization tools weren’t built to analyze that layer. PointFive is.
We’re introducing DeepWaste™ AI Profiler, an extension of PointFive’s continuous AI optimization across AWS Bedrock, Azure OpenAI, and GCP Vertex AI. AI Profiler analyzes the prompts and invocation patterns sent to AI models, evaluates how they are structured and executed, and translates those findings into quantified efficiency opportunities.
DeepWaste™ 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.
Importantly, AI Profiler delivers substantial optimization insights without requiring access to raw inference logs.
By intelligently unifying metrics, billing, and resource-level metadata, PointFive can detect structural inefficiencies in model selection, routing behavior, token allocation, caching configuration, and retry patterns, without analyzing customer prompt content.
This enables meaningful, high-impact optimization while preserving data privacy and minimizing access requirements.
The collected signals are patched and normalized into a structured, invocation-level dataset. Each request is enriched with task classification, application grouping, cost attribution, and behavioral context. Instead of viewing AI through aggregated billing metrics, organizations gain a detailed operational understanding of how models are actually being used in production.
This structured foundation enables accurate workload-level analysis of AI unit economics.
Once structured, the data runs through DeepWaste™, PointFive’s detection engine built specifically for AI services.
DeepWaste evaluates the full execution stack of AI workloads. It analyzes whether the selected model aligns with task complexity and identifies routing downgrade opportunities where a smaller or lower-cost model can deliver equivalent outcomes. It benchmarks similar workloads to detect cost outliers that signal structural inefficiency.
At the token layer, DeepWaste evaluates allocation patterns and configuration signals to detect prompt bloat, compression opportunities, output overprovisioning, and parameter-task misalignment. These adjustments reduce unnecessary token consumption while maintaining quality.
The engine also analyzes caching effectiveness and reuse patterns, surfacing duplicated inference and underutilized cache strategies as measurable waste.
Operationally, DeepWaste detects retry-driven cost leakage and provisioning misalignment against actual workload demand- inefficiencies that often remain invisible in traditional cloud dashboards but materially impact AI spend.
Each detection is grounded in behavioral signals derived from unified workload data, not surface-level billing anomalies.
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For organizations that want to go further, AI Profiler also supports a deeper diagnostic mode.
With explicit customer consent, PointFive can analyze inference logs to perform contextual and architectural-level reviews, including prompt design patterns, system prompt efficiency, model interaction structure, and inference-level execution behavior.
This enables even more granular optimization at the prompt engineering and model orchestration level.
Customers can choose the depth of analysis appropriate for their governance and privacy requirements- from metadata-driven optimization to full inference-level architectural review.
AI Profiler does not stop at analysis. Every inefficiency detected is translated into a quantified savings opportunity, with clear guidance on what can be optimized- whether through model routing adjustments, token limit calibration, caching improvements, retry handling changes, or prompt restructuring.
Recommendations are prioritized by impact and mapped to engineering and FinOps workflows. Teams can see not only what to change, but what the financial effect will be before implementing it.
The outcome is measurable efficiency improvement across active AI workloads, without disrupting application logic or development velocity.
As AI adoption scales, cost visibility alone is insufficient. Efficiency requires understanding how AI services behave at the execution level and continuously aligning usage patterns with business value.
DeepWaste™ AI Profiler provides:
Most cloud optimization tools weren’t built for AI behavior. AI Profiler was.
Actual waste- found, measured, and actionable, across every AI workload you run.
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