Zipkin
CentminMod
VPSSIM
NewRelic
Pilvi
Prometheus
TreeSize
Addigy
Ambertrace.dev
Datadog
Helicone AI
SigNoz
LangChain
Grafana
LangSmith
NewRelic
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 1–2ms 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.
Ambertrace.devNo Ambertrace.dev videos yet. You could help us improve this page by suggesting one.
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 1–2ms 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:
Based on our record, Zipkin seems to be more popular. It has been mentiond 33 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.
Distributed tracing systems like zipkin Aim to address these challenges by allowing visualisation of flows on environments with a full setup. Code can be traced By using mono-repos with the event names being the same across services. These are techniques To deal with the inability to trace code/flows across systems and while neither of them are as effective as tracing Usages of your code, they help drive a... - Source: dev.to / 8 months ago
The standard observability stack includes Prometheus for metrics collection, Grafana for visualization, and AlertManager for notifications. For logging, consider Fluent Bit or Fluentd with Elasticsearch or cloud logging services. Jaeger or Zipkin provide distributed tracing for microservices debugging. - Source: dev.to / about 1 year ago
Distributed tracing: This technology follows requests as they bounce Between services, showing you exactly where things slow down or break. Tools like Jaeger and Zipkin support OpenTracing standards, and leveraging an OpenTelemetry plugin can make it possible to track requests across different service boundaries without losing the thread. - Source: dev.to / over 1 year ago
Getting Started: Use tools like Jaeger, Zipkin, or OpenTelemetry. Focus on critical paths, set smart sampling rules, and align trace data with system metrics. - Source: dev.to / over 1 year ago
Logging and Tracing: Use centralized logging and distributed tracing to gain visibility into the flow of requests across microservices. This helps you diagnose issues more effectively and understand the impact of changes. Tools like Jaeger or Zipkin can be integrated with your API gateway to provide detailed tracing information. - Source: dev.to / over 1 year ago
CentminMod - Centmin Mod is a LEMP stack shell menu based auto installer.
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.
VPSSIM - VPSSIM provides installer enabling users to install LEMP stack on their servers.
Helicone AI - Open-source LLM Observability for Developers
NewRelic - New Relic is a Software Analytics company that makes sense of billions of metrics across millions of apps. We help the people who build modern software understand the stories their data is trying to tell them.
SigNoz - Open source alternative to Datadog