Distributional
Streamline and scale data distribution with real-time processing.. [Contact for Pricing]
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What is Distributional?
Distributional is an analytics platform for production AI products that helps teams discover and track hidden behavioral signals in their production AI logs. It turns raw trace data into actionable insights to continuously improve AI agents by uncovering behavioral insights corresponding to daily shifts, clusters, or outliers from analysis of production AI logs.
Key features include enriching production logs with statistical metrics, attributes, evals, and LLM-as-judge metrics; running adaptive unsupervised analysis including high-dimensional clustering, topic modeling, anomaly detection, and change detection; and publishing human-readable insights with notifications on deviations from tracked metric thresholds. The platform offers an extensible test framework, configurable test dashboards for collaboration, and intelligent test automation that adapts to each AI application.
Distributional is designed for AI engineering teams, product teams, CIOs, CTOs, and enterprises building generative AI applications. It serves companies in finance, technology, and industrial sectors who need to proactively identify, understand, and address AI risk before customer impact. The platform is particularly valuable for teams working with generative AI that is prone to non-determinism and non-stationary behavior.
The platform can be deployed freely to your VPC with options for local installation or scalable Kubernetes cluster deployment. It includes enterprise controls for data security, authentication, access permissions, and privacy. Flexible integrations support log ingestion via OTEL, SQL, or SDK, compatibility with current LLM providers and frameworks, and the ability to bring your own evals/metrics.
Distributional pricing
Pricing model: Freemium
Distributional's full service is open and free to use. The platform is free and open for deployment to your VPC of choice with options to install locally or in a scalable Kubernetes cluster. There is no publicly listed paid tier pricing on the website. For enterprise needs and specific use cases, teams are encouraged to reach out to [email protected] or [email protected] to discuss enterprise requirements.
Distributional pros
- Free and open platform deployable to your VPC
- Self-managed solution with Kubernetes cluster support
- Enriches logs with statistical metrics and LLM-as-judge evals
- Automated high-dimensional clustering and topic modeling
- Built-in anomaly detection and change detection
- Human-readable insights with threshold deviation notifications
- Extensible test framework for custom tests and metrics
- Configurable test dashboards for team collaboration
- Intelligent test automation with adaptive calibration
- OTEL, SQL, and SDK log ingestion options
- Enterprise-grade authentication and access permissions
- Similarity Index (Sim Index) for shift detection
- Behavioral Fingerprint unique to each AI application
- Notable Results for targeted prompt-response pair review
- Full data history with admin-configurable access controls
- Works with any LLM provider and AI framework
- Root cause analysis with correlation and comparative visualizations
- PagerDuty and Slack notification integration
Distributional cons
- Primarily focused on enterprise customers
- Requires production AI logs to function
- Steep learning curve for statistical testing concepts
- No explicit public pricing tier information available
- Demo और setup requires contacting support team
- GPU analytics cluster may require additional infrastructure
- Best suited for generative AI rather than traditional ML
- Custom metrics require additional configuration work
Frequently asked questions about Distributional
What is Distributional?
Distributional is the modern enterprise platform for consistent, adaptive, and reliable AI testing. It is an analytics platform for production AI products that helps teams discover and track hidden behavioral signals in production AI logs, turning raw trace data into actionable insights to continuously improve AI agents.
Who should use Distributional?
Distributional is designed for enterprise CIOs, CTOs, AI engineering teams, and AI product teams who need to proactively and continuously identify, understand, and address AI risk before it harms customers. It serves companies in finance, technology, and industrial sectors building generative AI applications.
How does Distributional detect AI behavior changes?
Distributional uses the Similarity Index (Sim Index), which is derived from statistical tests comparing metrics across experiment and baseline runs. It automatically applies the Sim Index to detect changes over time at the application, component, column, and result levels, with configurable thresholds to minimize false positives or negatives.
What is the Behavioral Fingerprint?
The Behavioral Fingerprint is created by Distributional's Eval Module and represents information on AI application status beyond performance. It includes distributions of metrics that define application status, including statistical metrics, custom user-defined metrics, LLM-as-judge metrics, business metrics, and data properties. Each AI application has a unique Behavioral Fingerprint.
What is the Distributional workflow?
The Distributional workflow has four stages: Define, Detect, Understand, and Improve. Define involves quantifying AI application behavior with metrics. Detect uses automated Sim Index testing to identify changes. Understand provides root cause analysis with visualizations and Notable Results. Improve allows creating new tests and adding data to golden datasets for future development.
Can I use my own evals and metrics?
Yes, Distributional allows you to bring your own evals/metrics, customize existing ones, and create your own. The platform includes a library of statistical tests that you can easily add or adjust thresholds for, either programmatically or with a single click through the dashboard or SDK.
How is Distributional deployed?
Distributional is freely available and can be deployed to your VPC of choice with options to install locally or in a scalable Kubernetes cluster. It is a self-managed solution that integrates with existing datastores, workflow systems, and alerting platforms. A GPU analytics cluster can be spun up as part of deployment.
What integrations does Distributional support?
Distributional supports flexible integrations including log ingestion via OTEL (OpenTelemetry), SQL, or their SDK. It works with current LLM providers and AI frameworks. The platform integrates with alerting platforms like PagerDuty and Slack for notifications based on change severity.
How does Distributional help with root cause analysis?
Distributional provides Similarity Insights that immediately guide users on what likely caused a change, a Similarity Report showing insights in context of all changes, comparative analysis and visualizations of distributions, and Notable Results—specific rows of data that most contextualize the change, shifting from random sampling to targeted review of specific prompt-response pairs.
Is Distributional suitable for traditional ML or just generative AI?
Distributional is built to test the consistency of any AI/ML application, but is especially designed for generative AI, which is particularly unreliable due to non-determinism (varying outputs from given input) and non-stationarity (shifting components outside developer control). The platform's adaptive testing is purpose-built for the unique challenges of generative AI.