Growth Metrics Checklist for AI & Machine Learning

Track the median minutes from signup to a 200 OK inference response via SDK or REST. Optimize docs, starter notebooks, and API examples to pull this below 15 minutes for most use cases.

Monitor the share of new accounts that create and use an API key at least once in the first week. Segment by language SDK to identify friction between Python, JS, and backend integrations.

Instrument step-level analytics on quickstart pages and notebooks to see where users drop off. Aim for a 70 percent+ completion rate by tightening dependency setup and environment checks.

Measure the percentage of SDK installs that result in a successful call within 24 hours. Add post-install messages, copy-paste token helpers, and smoke-test commands to raise conversion.

Build monthly cohorts split by task (chat, classification, retrieval, vision) and by model family to see where churn concentrates. Tie retention to feature releases and fine-tuning availability.

Track how often a docs search results in a code snippet copy. Low ratios suggest missing examples or poor snippet discoverability for frameworks like LangChain, LlamaIndex, or FastAPI.

For each task, log F1, accuracy, BLEU, ROUGE, or Exact Match on fixed datasets using MLflow or Weights & Biases. Compare new checkpoints to baselines before any rollout.

Use retrieval-grounded tests that compute factuality, citation coverage, or answer faithfulness. Report hallucination rate per domain and penalize outputs without source alignment.

Evaluate recall@k and mean reciprocal rank on labeled query-document pairs. Tune chunking, embeddings, and vector index parameters in Pinecone, Weaviate, FAISS, or pgvector based on results.

Use tools like Evidently to track feature distribution shifts and performance decay. Alert when PSI or KL divergence crosses thresholds so you can retrain or refresh embeddings.

Compute TPR parity, false positive parity, or equalized odds across sensitive segments when legally permissible. Add bias dashboards for review before promoting models to production.

Track inter-rater reliability (e.g., Cohen’s kappa) on evaluation and RLHF tasks. Investigate sudden drops that indicate rubric ambiguity or evaluator fatigue.

Instrument tokenization, retrieval, generation, and post-processing latencies separately. Set SLOs per stage and watch regressions after model upgrades or prompt changes.

Track 5xx, 4xx, timeouts, and rate-limit hits per model route. Reduce 5xx with Triton or vLLM health checks and pre-warm strategies, and surface client misuse patterns for 4xx.

Measure SM occupancy and memory usage across inference pods. Right-size instances, enable auto-suspend, and batch requests using TensorRT, ONNX Runtime, or vLLM to cut idle burn.

Compute fully loaded cost that includes GPU hours, egress, and storage. Compare across quantization levels, KV caching, and model sizes to identify profitable defaults.

Track Redis or vector cache hit rates to reduce repeat work. Raise hits by normalizing queries, applying deterministic chunking, and storing KV caches for long sessions.

Measure time-to-scale-up with Kubernetes HPA/KEDA or Ray Serve, then test headroom via load tests. Keep CPU, GPU, and I/O saturation under predefined limits during traffic spikes.

Use MLflow or W&B to pin data versions, code commits, and dependencies. Report the percentage of experiments that can be recreated within 5 percent metric variance.

Ensure 100 percent of production models are promoted from a registry with lineage and approvals. Block ad hoc artifacts from deployment via CI policies.

Monitor feature staleness and read latency in Feast or custom stores. Alert when freshness violates serving-time requirements for real-time recommendations or fraud detection.

Track GPU hours and wall-clock time for each training run. Use spot capacity, mixed precision, and gradient checkpointing to cut costs without sacrificing quality.

Measure the hours from a model or prompt change to a canary deployment using pipelines like GitHub Actions, Airflow, or Argo. Set gates for automated evals before rollout.

Run Great Expectations or custom validators for schema, nulls, and label consistency. Track pass rate per dataset slice and block training when critical checks fail.

Scan responses for PII patterns and hashes with DLP tooling, then redact or block as needed. Track incidents per 10K requests and enforce tenant-level policies.

Run adversarial prompts and red-teaming suites regularly, logging successful bypass percentage. Add guardrails and content filters to reduce the rate below an acceptable threshold.

Measure time to rotate model provider keys, webhooks, and service accounts. Enforce automatic rotation and least-privilege scopes with Vault or cloud KMS.

Report the percentage of endpoints protected by role-based policies and verify cross-tenant isolation. Include row-level security checks for vector stores and feature stores.

Ensure 100 percent logging of model invocations, prompt templates, and data access paths. Centralize logs in a SIEM and retain per compliance requirements.

Track mean time to detect and resolve quality or safety regressions after deploy. Use canary releases, shadow traffic, and rollback playbooks to keep MTTR low.

Compute margins per SKU after accounting for inference compute, storage, and egress. Adjust SKUs, default context windows, and batch sizes to keep margins healthy.

Measure lifetime value using expansion and churn, and compare to fully loaded acquisition costs. Target a ratio above 3 by reducing onboarding friction and improving retention.

Track the percentage of free developers that cross token, request, or GPU-hour limits and choose a paid plan. Test threshold placement and upgrade prompts for optimal lift.

Measure net expansion from higher-context LLMs, fine-tuning, premium embeddings, or enterprise controls. Attribute upsell to specific feature releases and campaigns.

Calculate per-account compute spend and trigger alerts when burn exceeds plan budgets. Offer spend caps, token quotas, or autoscaling controls to improve gross margin.

Track the share of trials that become paid deployments and how long it takes. Shorten cycles by providing reference architectures, compliance docs, and migration tooling.