Comparisons · 4 min read

AtlasBurn vs Helicone — LLM observability or AI cost guardrails?

Helicone is an open-source LLM observability platform with a gateway and rate limits. AtlasBurn is built only for AI spend — per-feature cost and guardrails that stop runaway loops. Here's how they differ, and when to use which.

By Akhil Anand · November 8, 2026

Short answer: pick Helicone if you want an open-source observability platform that logs every request and response so you can debug, evaluate and version prompts. Pick AtlasBurn if your main problem is AI spend: knowing what each feature costs, and having runaway agent loops slowed or stopped automatically without storing your prompts. Plenty of teams run both.

Last checked against Helicone'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] and we'll fix it.

At a glance

AtlasBurn Helicone
Built for AI spend: per-feature cost, forecasting, guardrails LLM observability: logging, tracing, evals, prompt management
How it connects JS/TS SDK (patches fetch), or an edge proxy for any language Gateway (base-URL swap, their recommended method), or async logging for Python and JS/TS
Stores prompts and outputs Never — usage numbers only Logs request and response content; can be omitted with headers or SDK settings
Cost tracking Per feature, model and provider Cost tracked; segment with user IDs and custom properties
Spend enforcement Budgets plus automatic loop, burst and model-mix detection; throttle first, then block Rate limits by request count or cost (in cents) per window, globally, per user or per property; returns 429
Loop detection Yes: identical requests and runaway context growth, per feature Not stated in their docs (sessions and traces give visibility)
Self-hosting No (hosted; the SDK is Apache-2.0) Yes, open source under Apache-2.0
Price Free during beta; plans from $0 (10M tokens/month) and $19 Free hobby tier (10,000 requests); Pro $79/month, Team $799/month

The real difference: logging vs guarding

Helicone is an observability product. Its gateway sits in front of your provider and records each call so you can search it, trace agent sessions, score outputs, run experiments and version prompts. That's genuinely useful when you're debugging quality.

AtlasBurn doesn't try to do that. It never stores prompts or outputs — only model, tokens, cost, time and the feature label you choose. Everything it does is about money: what each feature spends, what the month will cost, and what to do when spend goes wrong.

Stopping spend: rate limits vs runaway detection

Helicone does have enforcement. You can set a rate-limit policy by request count or by cost in cents, over a time window of at least 60 seconds, globally, per user or per custom property. Requests over the limit get a 429. If you know the right numbers in advance, that's a solid cap.

AtlasBurn takes a different approach because runaway agents rarely look like "too many requests per user." A loop usually looks like the same call repeated, or a context window that keeps growing. AtlasBurn watches each feature for:

  • Identical-request loops — the same model and input size repeated within 30 seconds.
  • Runaway context — inputs that keep growing until they've at least doubled in a short burst.
  • Request-rate bursts, daily and hourly budget breaches, and sudden concentration on premium models.

When a loop is found, only that feature is affected. On the Kill Switch tier it's first slowed to one call every 2 seconds; if the loop is still running 60 seconds later, or has cost more than your limit, it's blocked. Budget breaches block straight away. Alert and Throttle tiers never block.

Privacy

Helicone's docs describe headers (Helicone-Omit-Request, Helicone-Omit-Response) that keep content out of your logs, and note the content is still sent to their backend. With async logging you can disable content tracing in the SDK.

AtlasBurn is content-blind by design. With the SDK, prompts never leave your app. With the edge proxy they pass through on the way to the provider but aren't stored or logged.

Setup

Both take minutes. Helicone's recommended path is changing your base URL to their gateway. AtlasBurn's is npm install @atlasburn/sdk then npx abn init, which patches fetch and auto-detects 13 providers. For Python and other languages, AtlasBurn's edge proxy works the same way as a gateway: change the base URL and add one header.

Which should you choose?

  • Choose Helicone if you need request logs, tracing, evals, prompt versioning or a self-hosted open-source stack.
  • Choose AtlasBurn if you need per-feature AI cost, a bill forecast, and guardrails that catch loops and stop them without you watching a dashboard — and you'd rather not store prompts.
  • Use both if you want Helicone for debugging and AtlasBurn as the spend brake. AtlasBurn's SDK auto-captures calls that go straight to a provider, so this pairs most simply with Helicone's async logging; if you route through Helicone's gateway, send usage to AtlasBurn's ingest API instead.

Sources

Try AtlasBurn free