Xantly vs Helicone

Xantly and Helicone both sit between your application and LLM providers, but they solve different problems. Helicone is primarily an observability and logging layer, a proxy that records requests, tracks costs, and provides analytics. Xantly is a full AI infrastructure platform that actively routes, caches, and optimizes every request across 10,000+ models with 12ms median overhead, semantic caching (62% hit rate, sub-5ms), persistent memory, and waterfall failover, reducing costs by up to 80%.

Overview

Helicone is an open-source LLM observability platform. It acts as a logging proxy that captures request/response data, tracks token usage and costs, and provides dashboards for monitoring LLM usage patterns. Helicone gives engineering teams visibility into their AI spend without changing model routing.

Xantly is an AI gateway that goes beyond observation to active optimization. It analyzes each request across 15 parameters, routes to the optimal model from 10,000+ models, caches responses semantically, maintains persistent memory, and provides automatic failover, all through a single OpenAI-compatible API.

Key Differences

Active Routing vs Passthrough

Xantly intelligently routes each request using task classification, context analysis, and Bayesian adaptive learning (BaRP). It selects the optimal model based on cost, quality, and latency targets, with 5 intelligence modes and a continuous preference dial. Helicone is a passthrough proxy, it forwards requests to whatever provider you specify and records what happens.

Caching

Xantly implements multi-layer caching with exact match and semantic matching, achieving a 62% cache hit rate with sub-5ms response times. This eliminates redundant provider calls and drives significant cost savings. Helicone offers basic request caching that can replay identical requests, but does not support semantic similarity matching.

Memory and Learning

Xantly maintains persistent per-organization memory that carries context across sessions. It automatically detects sessions, extracts knowledge, and assembles relevant context for future requests. Helicone has no memory system, it records data for human review but does not inject learned context into requests.

Cost Reduction

Xantly reduces costs up to 80% through semantic caching, intelligent model selection (routing to cheaper models when quality permits), and cross-conversation deduplication. Helicone provides cost tracking and visibility so teams can identify where money is going, but does not actively reduce costs.

Voice AI

Xantly includes a full voice pipeline with 30+ models, sub-300ms latency, and a two-lane hybrid architecture for speech-to-text, text-to-speech, and realtime audio. Helicone does not offer voice capabilities.

Open Source

Helicone is open source and can be self-hosted, which appeals to teams with strict data residency requirements or those who prefer to own their infrastructure. Xantly is a managed platform, zero-ops deployment with no self-hosting overhead.

Feature Comparison

When to Choose Xantly

Choose Xantly when you need infrastructure that actively optimizes your LLM workloads. Xantly is the right choice if you want to reduce API costs through caching and routing, need persistent memory for personalization, require voice AI, want automatic failover and reliability across providers, or need intelligent model selection without manual configuration.

When to Choose Helicone

Choose Helicone when your primary need is observability and you want to keep full control over model selection. Helicone is a good fit if you need detailed logging and analytics for compliance or auditing, want an open-source solution you can self-host, prefer to handle routing decisions in your own code while getting visibility into costs, or need a lightweight proxy that adds minimal complexity to your stack.