CodeKeep VS Helicone AI

Which is more popular?

CodeKeep might be a bit more popular than Helicone AI. We know about 6 links to it since March 2021 and only 5 links to Helicone AI.

social mentions: 6 vs 5
Developer Tools popularity: 12% vs 88%
alternatives listed: 99 vs 240+

Base details

Website, pricing, platforms and company facts side by side.

	CodeKeep	Helicone AI
Website	codekeep.io	helicone.ai
Pricing	Freemium $4 / Monthly	Official pricing
Platforms	Browser	—
Listed in	Design Playground JavaScript Cloud Computing Snippet Snippet Manager Code Screenshots +3	AI Productivity Developer Tools Help Desk +1

Features and specs

What each product offers, as listed by its team.

CodeKeep 3 features

Helicone AI 0 features

Collaboration Tools
Code Editor
Code snippets

No features have been listed yet.

Analysis

An editorial look at what each product does well and who it suits.

CodeKeep

Helicone AI

No analysis of CodeKeep yet.

Overall verdict

Helicone is a strong, developer-friendly LLM observability platform that offers easy integration, useful logging, and cost tracking, making it a solid choice for teams building with large language models.

Why this product is good

Simple integration that often requires only a change to the API base URL or a lightweight proxy setup
Comprehensive request logging, tracing, and monitoring for LLM applications
Built-in cost tracking and usage analytics to help manage and optimize spending
Features like caching, rate limiting, and prompt management that improve performance and reliability
Open-source core with self-hosting options, giving flexibility and transparency
Support for popular providers like OpenAI, Anthropic, and others

Recommended for

Developers and startups building applications on top of LLM APIs
Teams that need visibility into token usage and API costs
Companies wanting to monitor, debug, and optimize their AI-powered features
Organizations that prefer open-source tools with self-hosting capabilities
Product teams iterating on prompts and needing analytics on model performance

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100

CodeKeep

Helicone AI

12% 12%

Developer Tools

88% 88%

0% 0%

100% 100%

16% 16%

Productivity

84% 84%

100% 100%

Design Playground

0% 0%

User comments

Share your experience with using CodeKeep and Helicone AI. For example, how are they different and which one is better?

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

CodeKeep 6 mentions

Helicone AI 5 mentions

Bending Spoons laid off almost everybody at Vimeo yesterday
I have made https://codekeep.io for storing snippets, have similar features to evernote. All users will get free pro membership now. If you are thinking about moving , please consider codekeep too. - Source: Hacker News / 9 months ago
Show HN: VS Code extension to share code snippets instantly
I had a similar idea and created https://codekeep.io , it also has an option to generate screenshots of code https://codekeep.io/screenshot. - Source: Hacker News / over 1 year ago
Ask HN: Who wants to be hired? (September 2024)
Hi there, I'm Jithin, a full-stack developer with ~9 years of experience looking for exciting new opportunities (remote or relocation friendly). I'm passionate about building robust, scalable cloud-native solutions with a focus on Golang... - Source: Hacker News / about 2 years ago

Best AI Monitoring Tools in 2026: LLM, Agent, and MCP Observability Compared
Helicone takes the simplest possible approach to LLM monitoring: it's a proxy. Change your OpenAI base URL from api.openai.com to oai.helicone.ai, add your Helicone API key as a header, and every LLM request is logged — latency, tokens,... - Source: dev.to / 4 months ago
What is an LLM evaluation harness? A deep dive into lm-eval-harness
You're monitoring production traffic. You need Langfuse / Phoenix / Helicone / Braintrust for that. Online eval is a different problem class: implicit feedback, drift detection, hallucination rates on your data, not on HellaSwag. - Source: dev.to / 4 months ago
Building Your Own AI Proxy: Route, Cache, and Monitor LLM Requests in TypeScript
For many teams, especially those starting out or with simpler needs, commercial solutions like Portkey, Helicone, OpenPipe, or LiteLLM Proxy offer off-the-shelf capabilities that cover many common proxy use cases (caching, logging, cost... - Source: dev.to / 6 months ago

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Handpicked code snippets you can use in your web projects

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Build and deploy LLM applications with confidence

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Code snippets for easier coding

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liteLLM
One library to standardize all LLM APIs

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