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Langfuse vs Helicone Comparison

Last updated:

Langfuse

4.7(185 reviews)

Starting at Free free

  • Small Business
  • Mid-Market

Langfuse is an open-source LLM engineering platform for tracing, evaluation, prompt management, and metrics. It provides detailed traces of LLM application executions — capturing every LLM call, tool use, retrieval step,…

Helicone

4.6(145 reviews)

Starting at Free free

  • Small Business
  • Mid-Market

Helicone is an open-source LLM observability platform that works as a proxy — developers route their LLM API calls through Helicone's gateway with a single URL change, and it automatically logs every request, tracks cost…

Langfuse leads on user satisfaction with a 4.7-star rating across 185 reviews.

Langfuse vs Helicone — at a glance

FeatureLangfuseHelicone
Rating4.7 / 54.6 / 5
Reviews185145
Starting priceFree freeFree free
Free trial No No
Free version No No
Best forSmall Business, Mid-Market, EnterpriseSmall Business, Mid-Market, Enterprise
CategoryMLOps PlatformsMLOps Platforms
PlatformsCloud, On-Premise, LinuxCloud, On-Premise
APIAvailableAvailable
Support modesGitHub Issues, Discord Community, Email Support, Enterprise SupportDiscord Community, GitHub Issues, Email Support, Enterprise Support

Key differences between Langfuse and Helicone

  • Pricing: Langfuse starts at Free free, while Helicone starts at Free free.
  • User satisfaction: Langfuse scores higher with a 4.7-star average.
  • Deployment: Langfuse supports Cloud, On-Premise, Linux; Helicone supports Cloud, On-Premise.

Langfuse vs Helicone — find the better fit before you commit.

01

Which tool fits your team best

02

Which is actually cheaper for your team size

03

Where each product wins, per real buyers

Most MLOps Platforms tools look identical on paper. This comparison cuts to the differences that matter — pricing structure, team fit, and what real buyers found after signing up.

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Biggest differences

Start here before you go deeper into features.

Langfuse

Best for

Small Business, Mid-Market, Enterprise

Helicone

Best for

Small Business, Mid-Market, Enterprise

Langfuse typically suits Small Business and Mid-Market. Helicone tends to fit Small Business and Mid-Market better. The right choice depends on your team size, workflow, and whether a free trial matters.

Description

Langfuse is an open-source LLM engineering platform for tracing, evaluation, prompt management, and metrics. It provides detailed traces of LLM application executions — capturing every LLM ... Read More about Langfuse

Helicone is an open-source LLM observability platform that works as a proxy — developers route their LLM API calls through Helicone's gateway with a single URL change, and it automatically ... Read More about Helicone

Entry Level Pricing

  • Starts from Free
  • Starts from Free

Free Trial Availability

  • No free trial
  • No free trial

SpotScore

What's this? ↗

9.4/10

9.2/10

User Ratings

Based on verified Spotsaas reviews
Get pricing help
Get pricing help

Where each option fits best

See where each product is strongest, which teams it fits, and what causes buyers to keep looking — before you commit.

Based on buyer reviews and verified product data collected by Spotsaas.

Strengths

Key strengths

Langfuse

  • Data Sovereignty for AI Traces: Self-hosted MIT deployment keeps all LLM traces, prompts, and evaluation data on company infrastructure — critical for regulated industries.
  • Free at Scale: 50k free cloud traces plus unlimited self-hosted cover most production AI applications without per-trace cost anxiety.
  • Product-Level AI Analytics: Session grouping lets you analyze how users interact with AI features across multi-turn conversations — beyond per-call debugging.

Helicone

  • Zero-Code Instrumentation: One URL change instruments every LLM call — no SDK, no decorators, no code changes beyond the base URL.
  • Immediate Cost Visibility: Real-time cost dashboard shows exactly how much each user, feature, or prompt variation is spending — before the cloud bill arrives.
  • LLM Cost Reduction: Built-in prompt caching reduces API calls for repeated inputs, turning the observability layer into a cost-savings tool.
Best fit

Best fit

Langfuse

  • European and regulated-industry teams self-hosting LLM observability to maintain data residency compliance
  • Early-stage AI startups instrumenting production LLM applications at zero cost with the cloud free tier
  • Teams running systematic LLM-as-judge evaluations to catch prompt quality regressions before deployment

Helicone

  • Indie AI developers and small teams who want instant LLM observability without instrumenting code
  • SaaS companies building per-user AI features who need user-level cost tracking and rate limiting
  • Teams looking to reduce OpenAI bills by adding prompt caching with minimal integration effort

Software Demo

Demo

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How do Langfuse and Helicone Compare on Features?

Total Features

8 Features

7 Features

Unique Features

No unique features

No unique features

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Compare Langfuse and Helicone on pricing

Review starting price, plan structure, and free-trial access side by side so you can see which option fits your budget and buying process.

Pricing Option

      Starting From

      • Free
      • Free

      Pricing Plans

      • Free

        Free

        • 100k requests/month

        • All providers

        • Dashboard

        Show more +

      • Pro

        $20

        paid

        • 1M requests/month

        • Custom properties

        • Team access

        Show more +

      • Enterprise

        Custom

        paid

        • Unlimited requests

        • SSO

        • On-premise

        Show more +

      Other Details

      Organization Types supported

          Platforms Supported

              Modes of support

              • 24/7 (Live rep)
              • Business Hours
              • Online
              • 24/7 (Live rep)
              • Business Hours
              • Online

              API Support

              • Available
              • Available
              Get help choosing
              Get help choosing

              Langfuse User Reviews & Rating Comparison

              User Ratings

              4.7

              (based on 185 reviews)

              4.6

              (based on 145 reviews)

              Rating Distribution

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              0

              0

              0

              0

              0

              0

              0

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              Spotsaas Editor’s POV generated by AI

              Buyer sentiment

              Buyer sentiment is very strong across 185 reviews, with consistently positive feedback.

              What buyers like

              • MIT open-source license with Docker-based self-hosting means teams with data privacy requirements can run full LLM observability without sending traces to a third-party cloud.
              • Generous free cloud tier (50k traces/month) covers most early-stage AI applications without any cost.
              • Session-level trace grouping links multiple LLM calls into user sessions — enabling product-level analysis of where users encounter AI failures.

              Common complaints

              • Smaller ecosystem and fewer integrations than LangSmith — some niche frameworks require manual instrumentation rather than automatic SDK integration.
              • Self-hosting requires Docker knowledge and ongoing maintenance; small teams without DevOps support may prefer the managed cloud.

              Buyer sentiment

              Buyer sentiment is very strong across 145 reviews, with consistently positive feedback.

              What buyers like

              • Two-minute integration via proxy — change one URL and every LLM call is automatically logged with zero SDK changes or code instrumentation.
              • Built-in prompt caching and rate limiting turn a logging tool into an active cost-reduction layer, not just observability.
              • Supports every major LLM provider through a single proxy endpoint — no separate integrations per provider.

              Common complaints

              • Proxy architecture adds a network hop to every LLM call — latency-sensitive applications (real-time voice agents) may prefer SDK-based instrumentation.
              • Less sophisticated evaluation and prompt management features than LangSmith or Langfuse — Helicone is strongest on logging/cost; weaker on systematic evaluation.

              Pros and Cons

              • MIT open-source license with Docker-based self-hosting means teams with data privacy requirements can run full LLM observability without sending traces to a third-party cloud.

              • Generous free cloud tier (50k traces/month) covers most early-stage AI applications without any cost.

              • Session-level trace grouping links multiple LLM calls into user sessions — enabling product-level analysis of where users encounter AI failures.

              • Smaller ecosystem and fewer integrations than LangSmith — some niche frameworks require manual instrumentation rather than automatic SDK integration.

              • Self-hosting requires Docker knowledge and ongoing maintenance; small teams without DevOps support may prefer the managed cloud.

              • Two-minute integration via proxy — change one URL and every LLM call is automatically logged with zero SDK changes or code instrumentation.

              • Built-in prompt caching and rate limiting turn a logging tool into an active cost-reduction layer, not just observability.

              • Supports every major LLM provider through a single proxy endpoint — no separate integrations per provider.

              • Proxy architecture adds a network hop to every LLM call — latency-sensitive applications (real-time voice agents) may prefer SDK-based instrumentation.

              • Less sophisticated evaluation and prompt management features than LangSmith or Langfuse — Helicone is strongest on logging/cost; weaker on systematic evaluation.

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              Disclaimer: This research has been collated from a variety of authoritative sources. We welcome your feedback at [email protected].

              Frequently asked questions

              Which is better, Langfuse or Helicone?
              Langfuse edges out the other on user ratings (4.7 vs 4.6). That said, the best pick depends on your use case — use the comparison tables above to evaluate each dimension.
              Do Langfuse and Helicone offer a free trial?
              Neither Langfuse nor Helicone currently lists a free trial.
              What is the starting price of Langfuse vs Helicone?
              Langfuse starts at Free free. Helicone starts at Free free.