Executive Summary

Engineering teams, SREs, and platform teams at both startups and enterprises increasingly struggle with invisible AI tool calls: when an app spawns tens to hundreds of LLM or tool API calls per user flow, costs, tail latency, and data-exposure risks become opaque and expensive to debug. These problems hit particularly hard in organizations routing production traffic for consumer-facing agents, LLM-augmented workflows, and automated pipelines, where a single rogue prompt or inefficient model choice can spike spend and breach latency SLAs. You could build a profiler/auditor that captures per-call telemetry—model id, token counts, prompt/response metadata with redaction, cost attribution, latency, and caller context—and surface that as searchable traces, cost maps, and automated alerts tied into CI/CD and APM tools. Key product features would include low-overhead instrumentation compatible with OpenTelemetry, sampling and redaction controls for privacy, per-endpoint cost budgeting, and prescriptive optimizations (e.g., model substitution, prompt truncation) delivered as actionable runbook items. The market is ripe: we estimate an $18B addressable market (600,000 developer organizations at roughly $30K ACV for observability/APM plus AI add-ons), and three trends converge—agentization that multiplies call surfaces, observability consolidation across infra/security/AI telemetry, and growing sensitivity to token/model economics. Model-choice arbitrage and token costs mean teams can often cut AI spend materially—differences of several-fold between models—so concrete per-call visibility becomes an immediate ROI lever. To stand out you'll need to combine extremely low overhead, enterprise-grade privacy controls, and deep integrations with existing APM and security stacks, plus a developer-first UX that translates telemetry into concrete cost and latency fixes rather than raw logs. The main challenges are convincing customers to trust telemetry over sensitive prompts, overcoming integration friction with diverse stacks, and competing with established observability vendors, but the payoff is real if you can demonstrably deliver measurable cost reductions (on the order of 10–30%) and latency improvements in production.

Market Opportunity

Visibility for AI tool calls — profiler to audit calls, costs, latency targets a $18.0B = 600,000 developer organizations x $30K ACV (observability/APM + AI-specific add-ons) total addressable market with medium saturation and a year-over-year growth rate of 35%+ — driven by AI adoption and observability convergence.

Key trends driving demand: Agentization of apps -- more tool/API calls per user flow creates new observability surfaces; Observability convergence -- APM, infra, security and now AI-telemetry unify in the stack; Cost-conscious AI adoption -- token and model-choice economics make per-call visibility actionable; Open instrumentation standards -- OpenTelemetry and community langchain instrumentations speed integrations.

Key competitors include PromptLayer, LangSmith (LangChain Labs), Datadog, Sentry, Workarounds / Adjacent Solutions.

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Visibility for AI tool calls — profiler to audit calls, costs, latency

Executive Summary

Market Validation

Market Opportunity

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