Helicone adds observability to AI features so teams see exactly how prompts, models, and costs behave in production. Capture requests and responses with metadata; visualize latency, tokens, and errors; and trace retries or timeouts. Group traffic by feature or cohort to compare behavior. Experiment safely by A/B testing prompts or models, then cache or route calls to control spend while improving quality and reliability for end users.
Record every LLM request with prompt, parameters, tokens, cost, and latency. Trace retries, streaming chunks, and tool calls so failure patterns are visible during incidents and rollouts. Search and filter by user, feature, or tag to isolate regressions quickly. PII-aware redaction protects sensitive fields while keeping enough context to debug real-world edge cases without copying data elsewhere. Sampling lets teams share sanitized traces with partners safely for targeted debugging and support.
Visualize usage, errors, and response times over time, broken down by model, route, or release. Set thresholds that alert on spikes, drift, or timeouts so owners respond before customers notice. Define cohorts to compare prompts, markets, or app versions, turning anecdotal feedback into measurable differences. Time-window and percentile views expose tail latency so small regressions do not hide behind averages, and aligned comparisons keep analyses fair when seasonality shifts traffic mix.
Run controlled experiments that pit prompts, parameters, or providers against each other. Score results with rubrics, heuristics, or lightweight human review, and pick winners with confidence intervals instead of hunches. Guardrail checks flag unsafe or off-brand outputs before they reach users, helping teams balance creativity and safety. Batch evaluations replay representative traffic against new settings, accelerating learning cycles without exposing customers to regressions during exploration.
Cache frequent queries to reduce latency and spend, and route traffic by rules—fallback on outage, cheaper model for drafts, premium for final moments. Budgets and per-route limits protect monthly targets while giving product owners headroom to explore. Routing can consider confidence or risk level, sending uncertain prompts to stronger models as drafts use economical tiers. Warm caches reduce cold-start spikes, and background refresh keeps popular answers quick without manual babysitting.
SDKs and proxies drop into existing code with minimal changes. Role-based access, audit logs, and retention policies meet enterprise expectations. Exports feed warehouses and BI tools so finance and ops see precise costs by feature, customer, and campaign. SCIM, SSO, and granular roles align access with policy, while residency options and export controls prevent inadvertent sharing of sensitive traces. Warehouse connectors structure logs for long-term analysis.
Recommended for product and platform teams building LLM-powered features who need to understand reliability and cost. Helicone turns opaque API calls into observable systems with traces, cohorts, and experiments. It connects engineering, product, and finance around shared truth so teams improve quality while staying within budget. Security appreciates clear audits, and finance gains accurate showback by feature and environment for planning and accountability.
Without observability, AI features drift and spend creeps. Helicone captures ground truth about prompts, models, and latency, then surfaces where to tune. Experiments replace guesswork, and caching and routing balance speed with cost. Outcomes include faster iteration, fewer incidents, and predictable bills that leadership can trust while features expand. Shared metrics reduce debates over anecdotal wins, helping teams invest where durable improvements are proven.
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