Databricks App for cross-workspace AI stack governance. Unified view of agents, serving endpoints, knowledge bases, and AI Gateway usage, backed by system tables + Lakebase with auto-refreshing discovery workflows.
OTel-native typed primitives for LLM cost attribution and telemetry — published on PyPI.
Vidai — the AI control plane. Govern, cost and secure every enterprise LLM request, from within your own network. Self-install Docker releases.
PyPI-distributable LLM control plane: gateway choke point, cost attribution, OTel instrumentation, and offline reporting as an inspectable engineering artifact.
OpenTelemetry-native token/cost attribution for LLM agents, with budgets that degrade before they deny
Per-subagent cost attribution for Claude Code. Reads local JSONL session logs, computes shadow cost at marginal Anthropic API rates with proper TTL pricing.
Infer ownership of untagged AWS resources from CloudTrail behaviour and write tags back — the open-source FinOps input layer. Methodology peer-validated on Microsoft Azure 2.6M-VM dataset.
FinOps cost attribution for AI code agents: maps Kiro, Cursor, and Claude Code spend to git commits to reveal per-task cost, waste patterns, and agent ROI signals.
Pre-dispatch policy evaluation and cost attribution for LLM inference, built on llmscope.
AI FinOps Engine - GPU/LLM cost attribution for Kubernetes
Attribute cloud database spend to the teams, apps and features that caused it — joins pg_stat_statements deltas with billing exports to produce a defensible chargeback report.
The visibility gap LiteLLM doesn't close: attribute LLM spend per RAG pipeline stage (retrieval / reranking / generation / evaluation). OpenAI-compatible, Ollama-backed, <8ms overhead.
Per-PR preview environments for Kubernetes with TTL cleanup and actual cost-on-PR attribution (OpenCost)
Serverless FinOps toolkit for GKE Autopilot — automated cost anomaly detection and rightsizing recommendations delivered to Slack, built on BigQuery, Cloud Monitoring, and Cloud Functions.
Snowflake-flavored FinOps engine on DuckDB. Reconciles per-query attribution against billed warehouse credits, surfaces the auto-suspend idle-padding gap (21% of the bill on the demo), and attributes every credit to a team via fair-share allocation per warehouse-hour.
Offline LLM cost attribution recipes for OpenAI-compatible gateway logs by user, feature, route, model, retry, and tool call.
Lightweight AWS Lambda proxy for OpenAI Codex CLI — per-developer token auth, full request/response logging, cost attribution, and centralized access control.
Self-hosted pipeline observability and cost-attribution dashboard for Airflow/dbt: ingest run metadata, estimate $ cost per pipeline/team, and surface it on a Streamlit dashboard. Docker Compose + Helm deployable.
AI-assisted cost attribution methodology for finance practitioners — usage-based allocation, automated validation, and self-service financial intelligence across manufacturing, healthcare, logistics, and technology.
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