Agihalo VS HelixML

Let’s have a side-by-side comparison of Agihalo vs HelixML to find out which one is better. This software comparison between Agihalo and HelixML is based on genuine user reviews. Compare software prices, features, support, ease of use, and user reviews to make the best choice between these, and decide whether Agihalo or HelixML fits your business.

Agihalo

Agihalo
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Enable true AI agent autonomy. Agihalo is the LLM router providing decentralized X402 payment rails for 24/7 compute, eliminating manual billing failures.

HelixML

HelixML
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Helix is a private GenAI stack for building AI agents with declarative pipelines, knowledge (RAG), API bindings, and first-class testing.

Agihalo

Launched 2025-12
Pricing Model Paid
Starting Price
Tech used Google Analytics,Google Tag Manager,Framer,Google Fonts
Tag

HelixML

Launched 2023-07
Pricing Model Freemium
Starting Price
Tech used Google Analytics,Google Tag Manager,Google Fonts,Nginx
Tag

Agihalo Rank/Visit

Global Rank
Country
Month Visit

Top 5 Countries

Traffic Sources

HelixML Rank/Visit

Global Rank 3050372
Country India
Month Visit 6827

Top 5 Countries

56.93%
21.17%
10.38%
4.75%
3.65%
India United States Poland Ukraine Israel

Traffic Sources

0.03%
8.45%
59.72%
1.29%
30.17%
0.28%
mail direct search social referrals paidReferrals

Estimated traffic data from Similarweb

What are some alternatives?

When comparing Agihalo and HelixML, you can also consider the following products

Helicone AI Gateway - Helicone AI Gateway: Unify & optimize your LLM APIs for production. Boost performance, cut costs, ensure reliability with intelligent routing & caching.

Arahi AI - Deploy autonomous AI agents to automate complex workflows. Agents use advanced reasoning to execute sales, operations, and support tasks 24/7.

Cloudflare Agents - Cloudflare Agents: Durable Objects, Workflows, Workers AI. Build agentic AI with reliable execution, serverless compute, and cost optimization.

TaskingAI - TaskingAI brings Firebase's simplicity to AI-native app development. Start your project by selecting an LLM model, build a responsive assistant supported by stateful APIs, and enhance its capabilities with managed memory, tool integrations, and augmented generation system.

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