Hyperparam Enterprise

Use cases

• A customer reports that your AI chatbot failed in production, and your team needs to trace the issue and find every other session the same pattern touched.
• Your developers are shipping with Claude Code, Cursor, and internal agents, and you need one view into how that AI is performing across the team, plus a way to share the skills and context that make it work better.
• After a prompt, tool, or model update rolls out across the team, you need to debug the regression sitting in the long-tail 1% of agent sessions before users notice.
• An internal agent is burning tokens on retry loops or rabbit-holes, and you need the trace evidence to motivate the CLAUDE.md, prompt, or skill change that steers it.
• Legal or security blocked a vendor LLM observability tool because it would copy customer prompts outside your VPC, and you need to debug agent behavior without ever moving the data.

HypStack: an open observability stack you own

Hyperparam Enterprise is the analysis side of HypStack, an open architecture for AI observability: Collectivus collects traces and writes them to your bucket, Hyperparam reads them on your laptop. The stack is MIT licensed and dependency-free, so security has one thing to audit and no supply chain to chase. Data lands in open formats (Apache Iceberg, Parquet, OTLP) that interoperate with whatever you already run. It is designed for procurement, security, and compliance teams who need a clear answer to “where does our AI data live and who owns it?”

• Collect with Collectivus: An OpenTelemetry collector for AI workloads, deployable across your fleet via your existing MDM (Jamf, Kandji, Intune). Captures developer laptops, IDE plugins, agents, MCP tool calls, chatbots, and production LLM features. Pure OTel underneath, so anything that already speaks OTLP works.
• Analyze with Hyperparam: A browser-native client that reads Iceberg and Parquet directly from your S3, GCS, or Azure bucket. No cluster, no SQL endpoint, no ingestion step. Credentials stay in the browser; an agent can query its own traces with no auth proxy in between.

The data in between is just files in your bucket. Object-storage prices (around $0.023/GB-month vs. $0.10–$2.00/GB ingest for traditional logging vendors), infinite retention, and same-day interop with Snowflake, Databricks, DuckDB, Spark, and Trino because Iceberg and Parquet are formats every major engine already reads.

Because Hyperparam runs in your infrastructure (data in your bucket, queries on your laptop) your existing controls extend automatically. The IAM policies, audit logs, retention rules, VPC boundaries, and access reviews you already have for your cloud account apply to your AI traces with nothing new to configure.

The AI workbench for LLM datasets

• Inspect production LLM logs across the full dataset
• Filter by prompt, response type, tool call, or score
• Query the dataset in chat
• Create filters and derived columns
• Score outputs with LLM-as-a-judge
• Rerun useful workflows as skills on new production data

Why teams choose Hyperparam Enterprise

• Data sovereignty: Prompts, retrieved context, and tool outputs stay in your bucket under your IAM. No vendor copy of sensitive customer data behind someone else's API.
• Object-storage economics: Two orders of magnitude cheaper than per-GB ingest tiers, with no retention cliffs. Keep months of traces instead of weeks.
• Open formats, no lock-in: Iceberg and Parquet are read by every major engine. If you replace Hyperparam tomorrow, the data is still in your account in a format your warehouse already speaks.
• Faster issue diagnosis: Browser-native architecture and AI-assisted column generation let teams find the broken 1% of traces without warehouse round-trips.
• Reusable workflows: Save inspection workflows as skills for team continuity across people, projects, and ownership changes.
• Fleet-wide collection: Deploy Collectivus across laptops and services with your existing MDM. No per-app SDK integration.