Comparisons · 3 min read

AtlasBurn vs Langfuse — tracing and evals, or a brake on AI spend?

Langfuse is an open-source AI engineering platform for traces, evals and prompts, with cost tracking and spend alerts. AtlasBurn tracks AI cost per feature and can slow or stop a runaway loop at runtime. They solve different problems.

By Akhil Anand · November 8, 2026

Short answer: Langfuse is for understanding and improving your LLM app — traces, evals, datasets and prompt management, open source and self-hostable. AtlasBurn is for controlling what your LLM app spends — per-feature cost, a bill forecast, and guardrails that can throttle or block a looping feature at runtime. They overlap on cost dashboards; they don't overlap on enforcement.

Last checked against Langfuse's own pricing page and docs on 8 November 2026. Sources are linked at the end. If something has changed, tell us at [email protected].

At a glance

AtlasBurn Langfuse
Built for AI spend: per-feature cost, forecasting, guardrails AI engineering: tracing, evals, datasets, prompt management
How it connects JS/TS SDK (patches fetch), or an edge proxy for any language Python and JS/TS SDKs, OpenTelemetry for other languages, 100+ integrations; an AI Gateway in beta
Stores prompts and outputs Never — usage numbers only Captures trace data; masking functions can redact content before it leaves your app
Cost tracking Per feature, model and provider, priced on ingest From ingested usage or model price definitions; by user, tags and model
Spend alerts Email; Slack, Discord and Telegram on paid plans Spend alerts by email; metric alerts to Slack and webhooks
Runtime blocking of LLM calls Yes: budgets, loop, burst and model-mix detection; throttle first, then block Not a documented feature (its gateway can restrict which models a credential allows)
Self-hosting No (hosted; the SDK is Apache-2.0) Yes, MIT licensed (except enterprise folders); Docker, Kubernetes, Terraform
Price Free during beta; plans from $0 (10M tokens/month) and $19 Hobby free (50k units/month); Core $29, Pro $199, Enterprise $2,499/month

Different jobs

Langfuse is where engineers go to see exactly what an agent did: each step, each tool call, each prompt version, and how outputs scored. If you're improving quality, it's one of the most complete open-source options.

AtlasBurn deliberately doesn't capture any of that content. It records model, tokens, cost, time and the feature label you choose, and uses those numbers for three things: cost per feature, a forecast of the month's bill, and guardrails.

Alerts vs enforcement

Langfuse can email you when spending crosses a threshold, and send metric alerts to Slack or a webhook. That tells a human something is wrong.

AtlasBurn acts on its own, within limits you set. It watches every feature for identical-request loops, context that keeps growing, request bursts, budget breaches and sudden jumps to premium models. A loop affects only the feature that looped: on the Kill Switch tier it's slowed to one call every 2 seconds, and blocked if it's still going 60 seconds later or has cost more than your limit. A budget breach blocks at once. If you only want notifications, the Alert tier never slows or blocks anything.

This matters most at night and on weekends. An agent stuck in a loop doesn't fail — it succeeds, thousands of times, at full price — and an alert waits for someone to read it.

Privacy

Langfuse captures trace data and offers masking functions in its SDKs to redact sensitive content before export. AtlasBurn never stores prompts or outputs at all; with its SDK they never leave your app.

Setup

Langfuse integrates through its SDKs, OpenTelemetry or a framework integration. AtlasBurn is npm install @atlasburn/sdk then npx abn init, or an edge-proxy base URL plus one header for Python and other languages.

Which should you choose?

  • Choose Langfuse for tracing, evals, datasets, prompt management or a self-hosted open-source platform.
  • Choose AtlasBurn for per-feature AI cost, a bill forecast, and automatic guardrails on runaway spend, without storing prompts.
  • Use both: a common setup is Langfuse for quality and debugging, AtlasBurn as the spend brake. They don't conflict.

Sources

Try AtlasBurn free