Growth Metrics Checklist for AI & Machine Learning

Measure the median time from signup to the first successful API inference or notebook run. Instrument via Segment or PostHog and your API gateway (Kong/Envoy) to spot onboarding friction in SDK installs and auth.

Track pip/npm/conda install success and first import errors across Python, Node, and Go. Capture telemetry with OpenTelemetry and surface per-language failure rates to prioritize fixes.

Record the share of new users who achieve a domain-specific accuracy threshold on a provided dataset or prompt pack within 24 hours. Use prompt packs, sample datasets, and evaluation harnesses to guide early wins.

Measure completion of an interactive LLM onboarding that covers API keys, prompt examples, and evaluation. Tools like Callouts in the docs and in-app tours can lift completion and reduce support load.

Track queries returning no results for keywords like fine-tuning, quantization, and RAG. Use Algolia or Elastic to log zero-result queries and feed them into a docs backlog.

Break down conversion by LLM, vision, and embeddings endpoints to see which tasks monetize. Align trials and credits with the families that show the highest conversion lift.

Report F1 for extraction, Rouge/BLEU for summarization, and pass@k for code. Run nightly evaluations with MLflow or Weights & Biases and gate deployments on p95 thresholds, not just averages.

Estimate the share of responses that include unsupported claims using a rubric and LLM judges plus human spot checks. Use Promptfoo or LangSmith with golden sets to calibrate.

Maintain a suite of canary prompts and measure deltas after model or prompt changes. Block releases if regression exceeds a defined threshold on critical scenarios.

Track toxicity with Perspective API or OpenAI Moderation and measure bias with HolisticBias or custom tests. Monitor subgroup performance and enforce mitigation policies.

Measure expected calibration error and the rate of abstentions in high-risk tasks. Use confidence scores and selective prediction so the system declines when uncertain.

For RAG pipelines, track precision@k of retrieved chunks and how often sources contain the answer. Evaluate with labeled queries and vector stores like Pinecone, Milvus, or FAISS.

Measure endpoint latency and streaming throughput by tokens per second across model sizes. Emit metrics via Prometheus and expose SLO dashboards to engineering and support.

Track requests per second and scale-to-zero/scale-out responsiveness on Kubernetes with KEDA or HPA. Verify no cold start storms and that scale decisions match real traffic patterns.

Monitor SM utilization, memory, and kernel-level bottlenecks using DCGM and Grafana. Right-size batch sizes and model parallelism to maximize useful utilization.

Include GPU/TPU time, egress, vector DB queries, and cache layers in unit cost. Report by model family and tenant to guide deprecations and price floors.

Measure cache effectiveness for embeddings and deterministic prompts via Redis or Memcached. High hit rates reduce both cost and latency for common queries.

Track the share of traffic served by quantized or compiled models (INT8, GPTQ, TensorRT, ONNX Runtime). Use per-endpoint flags to verify gains and watch for quality regressions.

Count users who executed pipelines, scheduled training, or deployed an endpoint. Tie WAU/MAU to specific ML actions instead of mere logins to reflect real engagement.

Measure repeat usage of fine-tuning, hyperparameter sweeps, and evaluation runs. These features correlate with deeper adoption and expansion opportunity.

Categorize churn by cost spikes, model drift, low task accuracy, or latency. Feed tags back into roadmap prioritization and success playbooks.

Track drift alerts with Evidently, Arize, or Fiddler and measure time to remediation. Correlate drift with ticket volume and NPS to quantify impact on retention.

Measure forks or clones of notebooks, RAG templates, and prompt kits. Higher adoption usually shortens time-to-value and boosts activation.

Track doc page reads before ticket creation on ML topics like deployment and tokenization. Aim to improve read-to-ticket ratios with code-first examples and diagnostics.

Compute revenue minus variable costs: GPU/TPU runtime, egress, vector DB queries, and model hosting. Pin margins at the endpoint level to inform pricing and sunsetting.

Include free credits, compute sponsorships, and content costs in CAC, then track payback from usage revenue. Segment by acquisition source like GitHub, Hackathons, or search.

Model lifetime value with anticipated token growth, new endpoints adoption, and enterprise seats. Tie expansion drivers to product features like batch inference and eval suites.

Run A/B tests on tokens versus requests and on small versus large model price deltas. Ensure telemetry captures elasticity so you can shift traffic to higher-margin options.

Measure days from trial start to security sign-off including data residency and SOC 2 reviews. Shorter cycles predict faster enterprise revenue recognition.

Track weekly A/Bs on prompts, retrieval settings, and model variants using feature flags and sequential tests. Report win rate and impact on accuracy, latency, and margin.