# PointFive Labs | The Research Engine Behind Our Detection Depth

Canonical: https://www.pointfive.co/labs

PointFive Labs is the in-house research team behind PointFive's detections. Research across AWS, Azure, GCP, and Snowflake; the Q1 2026 update reported ~$24M in newly addressable annual savings for its customer cohort, including ~$8M in AI.

# Where the depth comes from.

Research into the infrastructure inefficiencies that billing data alone cannot explain.

[Explore the findings](https://www.pointfive.co/labs#examples) [Read the Q1 2026 update](https://www.pointfive.co/blog/pointfive-runs-deep-56-new-detections-in-90-days)

## Look beyond the obvious.

Four detection examples from the Q1 2026 update. Each starts with a specific behavior and investigates its cost.

Amazon Bedrock

### Guardrails that cost more than inference

Per-text-unit assessment costs can exceed the inference cost they protect.

What Labs investigates

Labs compares guardrail charges with inference costs to surface the imbalance.

Snowflake

### Recoverable tables with permanent storage costs

Intermediate tables can be rebuilt, but still incur Fail-safe and extended Time Travel charges.

What Labs investigates

Labs traces table lineage to find data written exclusively from upstream sources.

Azure OpenAI

### Rejected requests. Billed input.

Content-safety rejections can leave you paying for input processing with no useful output.

What Labs investigates

Labs examines rejection patterns and their associated token costs.

AWS EC2

### CPU credits that change fleet economics

Burstable instances can cost more than a comparable non-burstable fleet.

What Labs investigates

Labs aggregates CPU-credit overages across auto-scaling groups.

## Four domains. Deeper questions.

Configuration, usage, and cost analysis across cloud, data, and AI.

Infrastructure  Compute, storage, and networking

AWS EC2, EBS, EFS, S3, Lambda, OpenSearch; Azure Storage, Site Recovery, App Configuration; GCP Dataflow, BigQuery, Compute

Platform services  Behavior behind managed services

ElastiCache provisioned and serverless; SQS standard and FIFO patterns; DynamoDB and RDS Multi-AZ behavior

Data platforms  Warehouses, tables, and pipelines

Snowflake warehouses, table lineage, and ingestion; Databricks and BigQuery coverage in active expansion

Production AI  Inference, models, and GPUs

Bedrock and SageMaker; Azure OpenAI deployments and PTU economics; Routing, caching, guardrails, model selection, and GPU utilization

[Explore platform coverage](https://www.pointfive.co/coverage)

## From observation to detection.

PointFive Labs is the in-house research team behind DeepWaste detections.

### Start with customer environments.

Research begins with real configurations, usage patterns, and workload behavior.

### Follow the inefficiency below the bill.

Researchers apply an investigative approach drawn from cybersecurity, examining configuration, utilization, and lineage.

### Turn findings into detections.

Detections include a cost model to quantify potential impact. Teams validate savings after implementation.

## Open research.

Labs also publishes open, reproducible research on how AI infrastructure spend is incurred, with the benchmarks and data behind each finding.

July 2026

### [Token Reduction Is Not Cost Reduction](https://arxiv.org/abs/2607.12161)

An empirical study of end-to-end efficiency in API-based coding agents.

August 2026

### [Prompt-Induced Waste in Coding Agents](https://arxiv.org/abs/2608.01347)

How prompt wording changes reasoning, effort, and end-to-end cost for coding agents.

Benchmarks

### [Open benchmarks and data](https://github.com/PointFiveLabs/ai-efficiency-benchmark)

The benchmark and data behind both papers, released so the results can be checked.

[Read Introducing PointFive Labs](https://www.pointfive.co/labs/introducing-pointfive-labs)

## What the research uncovered.

The Q1 2026 report on new detections across cloud, data, and AI.

[Read the full report](https://www.pointfive.co/blog/pointfive-runs-deep-56-new-detections-in-90-days)

AI detections introduced

17

Initial Snowflake detections

7

Detections in one release

32

Newly addressable annual savings

~$24M

Reported for the Q1 2026 customer cohort, including ~$8M in AI opportunities. Addressable savings are estimates, not realized savings or a forecast for every environment.

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Source: the public page above. Product screenshots and illustrative interfaces are examples, not live customer data.