Ambertrace.dev's answer

• AI and ML engineers at startups and scale-ups who are shipping LLM-powered features to production. These are teams of 3–50 developers building AI agents, chatbots, RAG pipelines, or AI-assisted workflows using OpenAI, Anthropic, or Google APIs. They have moved past prototyping and are now dealing with production realities: silent agent failures, unpredictable token costs, debugging sessions that take hours because logs show nothing useful.
• Secondary audience includes platform and SRE teams at larger companies who need to give their AI teams the same observability infrastructure that exists for traditional backend services

Better Stack's answer:

Engineering teams of all sizes – from startups to Fortune 500 companies.

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.

Better Stack's answer:

Better Stack is a modern observability tool that leverages eBPF and OpenTelemetry to make tracing work for you.

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

Better Stack's answer:

We are software builders at Better Stack.

CEO is a software engineer, COO is a software engineer and you guessed it; CTO is an engineer, too.

We’re helping developers ship higher quality software faster.

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.

Better Stack's answer:

You get an unrivaled price-to-performance ratio. Forget sampling and ingest all your data, or decrease your costs by 30x.

Ambertrace.dev's answer

• Python and TypeScript for the open-source SDKs, with automatic monkey-patching of the official OpenAI, Anthropic, and Google client libraries.
• The backend is built on Python with a PostgreSQL database for trace storage and querying.
• The web portal uses Next.js with React.
• The SDKs use background threads (Python) and async tasks (Node.js) for non-blocking trace delivery, ensuring near-zero performance impact on the host application

Better Stack's answer:

The primary technologies used to build Better Stack are eBPF for low-level, high-performance instrumentation and ClickHouse for storing and querying large volumes of observability data efficiently.