Ambertrace.dev
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LLM observability platform with an open source SDK that traces every AI agent call, token usage, and failures across OpenAI, Anthropic, and Google. Key capabilities: auto-patches OpenAI, Anthropic, and Google clients with no wrappers or decorators; unified multi-provider dashboard; token usage and cost-per-session analytics; automatic failure detection and retry loop flagging; real-time trace streaming; alerting via Slack. The SDK adds approximately 1โ2ms overhead per call. Traces are sent asynchronously in background threads. Ambertrace never breaks applications - all tracing errors are caught internally, and provider exceptions are re-raised unchanged.
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Ambertrace.dev's answer
Ambertrace is the only LLM observability platform that instruments OpenAI, Anthropic, and Google with genuinely zero code changes: you need to add just two lines of code, no wrappers, no decorators, no middleware. The SDK auto-patches provider clients at initialization, captures every request, response, token count, and latency metric, then sends trace data asynchronously in background threads with approximately 1โ2ms overhead. Most competing tools either require framework-specific plugins, manual span creation, or lock you into a single provider ecosystem. Ambertrace works at the provider SDK level, which means it traces everything regardless of whether you use LangChain, LlamaIndex, CrewAI, or custom agent code.
Ambertrace.dev's answer
Ambertrace.dev's answer
Three reasons:
First, setup friction: Ambertrace takes under 5 minutes to instrument an entire application. There are no config files, no environment variables to chain together, no framework-specific setup guides to follow. You install the package, call init(), and every LLM call is traced.
Second, no vendor lock-in: AmberTrace normalizes traces across OpenAI, Anthropic, and Google into a single unified format. You can compare cost, latency, and error rates across providers in one dashboard - critical for teams evaluating or switching models.
Third, deployment flexibility: the SDKs are open-source, and you can choose between our managed cloud or self-hosting on your own infrastructure. Competitors typically force you into one or the other. Ambertrace also uses usage-based pricing rather than per-seat pricing, so your entire team gets access without costs scaling linearly with headcount.
Ambertrace.dev's answer
Ambertrace was born from firsthand frustration. While building AI agents in production, we kept hitting the same wall: an AI agent would return a confidently wrong answer after burning through thousands of tokens, and our logs would show nothing but a series of successful HTTP 200 responses. Traditional APM tools tracked requests and database queries perfectly, but they were completely blind to what mattered in LLM applications - the reasoning chains, the token economics, the silent failures. We looked at existing solutions and found they either required heavy framework-specific integration, locked you into one provider, or were enterprise APM add-ons that cost more than our entire infrastructure. So we built Ambertrace: a lightweight, provider-agnostic observability layer that any developer can add in two lines of code. We open-sourced the SDKs because we believe the instrumentation layer running inside your application should be transparent and trustworthy
Ambertrace.dev's answer
Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.
Dynatrace - Cloud-based quality testing, performance monitoring and analytics for mobile apps and websites. Get started with Keynote today!
Helicone AI - Open-source LLM Observability for Developers
SigNoz - Open source alternative to Datadog
Splunk Enterprise - Splunk Enteprise is the fastest way to aggregate, analyze and get answers from your machine data with the help machine learning and real-time visibility.
LangChain - Framework for building applications with LLMs through composability