Comparison
opik vs helicone
opik (Open-source AI Observability, Evaluation, and Optimization) vs helicone (Ice-cold observability for LLMs.) - live GitHub stats and typed graph relationships, not marketing.
Markdown twin · opik alternatives · helicone alternatives
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Tagline
- opik
- Open-source AI Observability, Evaluation, and Optimization
- helicone
- Ice-cold observability for LLMs.
Stars
- opik
- 20k
- helicone
- 5.9k
Forks
- opik
- 1.6k
- helicone
- 622
Open issues
- opik
- 149
- helicone
- 124
Language
- opik
- Python
- helicone
- TypeScript
Adopt for
- opik
- Opik offers a comprehensive suite for the development lifecycle of generative AI applications with features such as deep tracing, automatic prompt optimization, and advanced evaluation capabilities under an open-source,姚
- helicone
- Helicone is an open-source LLM observability platform that supports monitoring, evaluation, and experimentation of Large Language Models using a simple integration. It provides advanced tracing, intelligent routing, cost
Persona
- opik
- -
- helicone
- -
Runtime
- opik
- -
- helicone
- -
License
- opik
- Opik is released under the Apache License 2.0 which allows for commercial use but requires preservation of copyright notices.
- helicone
- Apache-2.0
Last pushed
- opik
- Jul 8, 2026
- helicone
- Jul 5, 2026
Categories
- opik
- Evaluation & Observability
- helicone
- Evaluation & Observability, Inference & Serving
Trust and health
Days since push
- opik
- 0d
- helicone
- 3d
Open issues (now)
- opik
- 149
- helicone
- 124
Security scan
- opik
- No lockfile
- helicone
- Not scanned
Full report
- opik
- Trust report
- helicone
- Trust report
Typed relationship
opik alternative heliconeHelicone and Opik both offer AI observability, evaluation, and optimization services.
Choose opik if…
- opik is primarily Python; helicone is TypeScript.
- Helicone and Opik both offer AI observability, evaluation, and optimization services.
- Tags unique to opik: langchain, llm-observability, llama-index, llm-evaluation.
- Use Opik when you are working on complex LLM systems or agentic workflows that require detailed observability and evaluation.
When NOT to use opik
- Avoid using Opik if you only need basic tools for AI model monitoring without requiring advanced functionalities such as deep tracing or automatic optimization.
- Do not use Opik if your project does not align with the observability and evaluation focus of this platform, preferring a more specialized tool.
Choose helicone if…
- helicone is primarily TypeScript; opik is Python.
- Pricing: Helicone offers a free tier (generous monthly 10k requests/month). Pricing details for additional usage tiers are available on their official pricing page..
- Requirements: Ensure TypeScript is supported in your project environment since Helicone's API is compatible with TypeScript.; Before integrating Helicone, verify that intelligent routing and fallback mechanisms align with the requirements of your LLM deployment scenario..
- Helicone and Opik both offer AI observability, evaluation, and optimization services.
- Tags unique to helicone: agent-monitoring, cost-tracking, observability, model-routing.
- Also covers Inference & Serving.
- helicone ships Docker support for self-hosted deployment.
- When you need to integrate multiple AI models with one API key through the OpenAI API, including automatic fallbacks.
When NOT to use helicone
- When the requirement demands strict adherence to proprietary systems, as Helicone encourages open-source development.
- For projects that do not necessitate monitoring metrics such as cost and latency tracking via an external platform.
- If your application does not need intelligent routing across multiple AI models or fallback mechanisms.
Explore
opik trust report →helicone trust report →Evaluation & Observability category →Inference & Serving category →All comparisonsStack workflowsTrending tools
Related comparisons
Common questions
- What is the difference between opik and helicone?
- opik: Open-source AI Observability, Evaluation, and Optimization. helicone: Ice-cold observability for LLMs.. See the comparison table for live GitHub stats and shared categories.
- When should I choose opik over helicone?
- Choose opik over helicone when opik is primarily Python; helicone is TypeScript; Helicone and Opik both offer AI observability, evaluation, and optimization services; Tags unique to opik: langchain, llm-observability, llama-index, llm-evaluation; Use Opik when you are working on complex LLM systems or agentic workflows that require detailed observability and evaluation.
- When should I choose helicone over opik?
- Choose helicone over opik when helicone is primarily TypeScript; opik is Python; Pricing: Helicone offers a free tier (generous monthly 10k requests/month). Pricing details for additional usage tiers are available on their official pricing page.; Requirements: Ensure TypeScript is supported in your project environment since Helicone's API is compatible with TypeScript.; Before integrating Helicone, verify that intelligent routing and fallback mechanisms align with the requirements of your LLM deployment scenario.; Helicone and Opik both offer AI observability, evaluation, and optimization services; Tags unique to helicone: agent-monitoring, cost-tracking, observability, model-routing; Also covers Inference & Serving; helicone ships Docker support for self-hosted deployment; When you need to integrate multiple AI models with one API key through the OpenAI API, including automatic fallbacks.
- When should I avoid opik?
- Avoid using Opik if you only need basic tools for AI model monitoring without requiring advanced functionalities such as deep tracing or automatic optimization. Do not use Opik if your project does not align with the observability and evaluation focus of this platform, preferring a more specialized tool.
- When should I avoid helicone?
- When the requirement demands strict adherence to proprietary systems, as Helicone encourages open-source development. For projects that do not necessitate monitoring metrics such as cost and latency tracking via an external platform. If your application does not need intelligent routing across multiple AI models or fallback mechanisms.
- Is opik or helicone more popular on GitHub?
- opik has more GitHub stars (20,410 vs 5,920). Stars measure visibility, not whether either tool fits your constraints.
- Are opik and helicone open source?
- Yes - both are open-source projects on GitHub (opik: Apache-2.0, helicone: Apache-2.0).
- Where can I find alternatives to opik or helicone?
- GraphCanon lists graph-backed alternatives at /tools/comet-ml-opik/alternatives and /tools/helicone-helicone/alternatives (/tools/comet-ml-opik/alternatives.md, /tools/helicone-helicone/alternatives.md), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at /compare/comet-ml-opik-vs-helicone-helicone.md mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, opik or helicone?
- opik: Very active. helicone: Very active. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for opik and helicone?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: opik: /tools/comet-ml-opik/trust; helicone: /tools/helicone-helicone/trust.