Promptwatch

Quick summary of Promptwatch

Promptwatch is an LLM monitoring + observability platform — 'Sentry for LLMs' tracking every prompt + response + token count + cost + latency + metadata across OpenAI + Anthropic + Google + other LLM API calls, organizing them into traces + sessions for multi-step AI apps, providing search + filtering + analytics + error detection + cost optimization tools for teams building AI products. Distinguished from LangSmith (LangChain's official observability $39+/user/mo with native LangChain integration) by simpler onboarding without LangChain dependency, distinguished from Helicone (open-source friendly competitor) by hosted-first approach + UX, distinguished from Langfuse (another open-source player) by competitive positioning, distinguished from Arize AI (enterprise ML/LLM observability broader) by LLM-focused simplicity + indie-friendly pricing, distinguished from Datadog + Sentry + New Relic LLM features (added to existing platforms) by focused LLM specialization. For ML engineers + AI engineers + product teams building LLM-powered products needing observability beyond console.log, Promptwatch is competitive option 2026 in rapidly evolving AI observability category. Core features: SDK for Python + JavaScript wrapping LLM API calls, proxy mode pointing LLM calls through Promptwatch endpoint, automatic capture of every prompt + response + token count + cost + latency + metadata, trace organization for multi-step LLM apps (chain of calls), session grouping for user interactions, search + filtering across captured traces, analytics dashboards with cost + usage + quality metrics, error detection + alerting on issues, cost optimization recommendations identifying expensive prompts, quality scoring for response evaluation, A/B testing infrastructure for prompt variations, prompt versioning + iteration tracking, integration with OpenAI + Anthropic + Google Gemini + Cohere + Mistral + custom LLMs, retention configurable from 7-90+ days based on plan, team collaboration features with role-based permissions, comment + annotation on specific traces for team review, API access for programmatic operations (Pro+), webhook delivery for events + alerts, custom alerting rules based on cost + latency + quality thresholds, integration with Slack + email for notifications, GDPR-compliant data handling, SOC 2 compliance (Business), SSO for enterprise (Business), data residency options (Business), audit logging, regular dashboard improvements + AI-powered insights, prompt library for managing reusable prompts across projects, version comparison for prompt iteration, response quality classification, user feedback integration for measuring AI quality from user signals. Best for teams building AI products needing visibility into LLM behavior in production beyond console.log + manual reviews, ML engineers + AI engineers debugging prompts + reducing costs + improving quality across LLM API calls, product teams tracking AI feature performance + user interactions + engagement metrics, AI agencies serving clients needing reporting + monitoring + cost transparency, developers shipping LLM-powered features who need observability the way web apps need Sentry + Datadog, AI cost optimization initiatives identifying expensive prompts + opportunities, prompt iteration workflows with A/B testing + measurement, quality improvement programs systematically improving AI outputs based on real production data, teams without LangChain dependency wanting LLM observability without framework lock-in. Skip for LangChain-heavy stacks where LangSmith's native integration earns its premium pricing (use LangSmith for tighter ecosystem fit), open-source preferences where Langfuse + Helicone better match philosophy + budget, enterprise needs with broader ML observability beyond LLMs (Arize AI more comprehensive across ML + LLM monitoring), teams already using Datadog/Sentry/New Relic preferring single-vendor consolidation (use their LLM features for consolidated observability), simple AI products where basic logging suffices + observability adds unnecessary complexity, very high volume needs where dedicated infrastructure required. Pricing: Free tier 10k traces/mo + 1 project + basic analytics + 7-day retention for personal use; Starter $19/mo for 100k traces + unlimited projects + email support + 30-day retention + team access; Pro $49/mo for 500k traces + advanced analytics + priority support + 90-day retention + API access; Business $99/mo+ for 1M+ traces + custom retention + SSO + compliance + dedicated support + custom integrations. Direct competitors: LangSmith ($39+/user/mo, LangChain's official observability with native LangChain integration), Helicone (open-source friendly, free + paid cloud), Langfuse (open-source player, free + paid cloud), Arize AI (enterprise ML/LLM observability), Datadog LLM Observability (within Datadog platform), Sentry LLM (within Sentry platform), New Relic AI Observability (within New Relic), Phoenix by Arize (open-source ML observability), WhyLabs (ML observability), Aporia (ML observability), Galileo (LLM evaluation + observability), Truera (ML quality), Weights & Biases (ML experiment tracking with LLM features). Promptwatch wins on simpler onboarding without LangChain + focused LLM observability + competitive indie-friendly pricing + modern UX; LangSmith wins on LangChain ecosystem integration + brand recognition; Helicone + Langfuse win on open-source philosophy + flexibility; Arize wins on enterprise ML/LLM broader; Datadog/Sentry/New Relic win on consolidated platform vs adding new tool. For focused LLM observability in 2026, Promptwatch is competitive option in rapidly evolving category.