Tech & AI, Explained

helicone

3 min read

🇺🇸 USA · Helicone

Status: 🟩 COMPLETE 🟦 LIVING Last updated: 2026-06-26 Plain-English tagline: Observability for AI apps. See every LLM call your app makes — costs, latency, errors, full request / response — across providers. The Datadog / Sentry of LLM operations.

Front-matter facts

FieldValue
VendorHelicone Inc (USA)
Country / origin🇺🇸 USA
Recommended for Australian users?✅ Yes — fully accessible from AUS; open-source option
Privacy summaryHelicone Cloud: data flows through Helicone for observability; self-hosted option = full control; PII redaction available
Free tierYes — 10K requests/month free
Paid tiersPro US100/mo; Enterprise quoted; self-hosted free
First released2023
Last reviewed2026-06-26
Official sitehttps://helicone.ai

What it is

Helicone is observability for LLM applications — track every AI call your app makes with full request / response, cost, latency, errors, and analytics. Think Datadog or Sentry, but purpose-built for LLM workloads where standard APM tools don’t quite fit (prompts / tokens / streaming need different treatment).

Two modes:

  1. Helicone Cloud (hosted) — proxy calls through Helicone; observability automatic
  2. Self-hosted (open-source) — run Helicone yourself; data never leaves your infrastructure

Features:

  • Per-request logs — full prompt / response / metadata
  • Cost tracking — per-provider, per-model, per-user, per-app
  • Latency monitoring
  • Error tracking — see what failed and why
  • Custom properties — tag calls with user_id, session_id, etc.
  • Caching to reduce duplicate costs
  • Rate limiting
  • Prompt versioning + experiments
  • PII redaction for sensitive data
  • Multi-provider — works with OpenAI, Anthropic, Google, Bedrock, Vertex, Together, Groq, etc.

What you’d use it for

  • Building AI products and needing visibility into LLM calls
  • Cost monitoring at scale across providers
  • Debugging AI workflows — what prompt got what response
  • Compliance / audit — record of every AI interaction
  • A/B testing prompts / models
  • Performance optimisation — latency analysis
  • Customer support — investigate “the AI gave me wrong answer” reports

How to use from Australia

Helicone Cloud

  1. Sign up at helicone.ai
  2. Get Helicone API key
  3. Update your OpenAI / Anthropic / etc. base URL to route through Helicone: 
    from openai import OpenAI
client = OpenAI(
    api_key="openai-key",
    base_url="https://oai.helicone.ai/v1",
    default_headers={"Helicone-Auth": "Bearer helicone-key"}
)
  4. View dashboard at helicone.ai
  5. AUS card accepted

Self-hosted

  1. Deploy Helicone (Docker / k8s)
  2. Configure providers
  3. Same proxy pattern, your infrastructure

What it costs

Free tier

  • 10K requests/month
  • Basic dashboard
  • Suitable for small projects

Pro — US$25/month

  • 100K requests/month
  • Advanced features (caching, prompts, etc.)

Team — US$100/month

  • 2M requests/month
  • Team dashboards

Enterprise — quoted

  • Unlimited
  • SLAs, custom features

Self-hosted

  • Free (open-source)
  • You pay your hosting (Docker / cloud)

How it compares to alternatives

AspectHeliconeLangfuseLangSmithPortkey
Open-source self-hostableYesYesNoOptional
Multi-providerYesYesLangChain-focusedYes
Cloud + self-hostedYes bothYes bothCloud onlyCloud + enterprise
Pricing (Pro tier)US$25Free + paidUS$39Tier-based
PII redactionYesYesYesYes
Best forMulti-provider observability + costLangChain-agnostic observabilityLangChain usersEnterprise observability + gateway

For lightweight AI observability with multi-provider support, Helicone is a strong default.

Privacy / data handling

  • Cloud: data flows through Helicone (encrypted at rest)
  • Self-hosted: full control; data never leaves your infrastructure
  • PII redaction available (configurable)
  • SOC 2 compliant (cloud)
  • For sensitive workloads (medical, legal, finance), self-hosted strongly recommended

Recent changes

  • 2026: Expanded analytics; prompt experimentation matured
  • 2024: Major adoption among AI startups

Gotchas

  • Cloud mode adds latency — typically ~50ms; usually negligible
  • Self-hosted adds infrastructure overhead
  • For Bible-Quest-scale projects without serious AI volume, observability may be over-engineering
  • PII redaction is opt-in — configure if needed

See also

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