Two Architectures for Cost Tracking
Helicone and ClawHQ both track AI agent costs, but they use fundamentally different architectures. This difference matters more than you might think.
Helicone: The Proxy Approach
Helicone works as a proxy — your LLM API calls route through Helicone's servers before reaching OpenAI/Anthropic/etc. This lets Helicone log every request and calculate costs at the request level.
Pros: Detailed per-request logging, caching can reduce costs, rate limiting built in.
Cons: Adds a network hop (latency), creates a single point of failure, requires changing your API base URL, all your traffic goes through a third party.
ClawHQ: The OTLP Approach
ClawHQ receives cost metrics asynchronously via OpenTelemetry. Your LLM calls go directly to providers. Cost data travels on a separate, async channel.
Pros: Zero latency, no single point of failure, no traffic routing through third parties, OTLP is an open standard.
Cons: No built-in request caching (use your own), no proxy-level rate limiting (use gateway config).
Feature Comparison
- Cost tracking: Both ✅ — Helicone per-request, ClawHQ per-agent/per-task
- Budget alerts: Helicone ❌, ClawHQ ✅
- Spend forecasting: Helicone ❌, ClawHQ ✅
- Model optimization: Helicone basic, ClawHQ ✅ recommendations
- Team cost allocation: Helicone ❌, ClawHQ ✅
- Latency impact: Helicone adds latency, ClawHQ zero
- Vendor lock-in: Helicone proprietary, ClawHQ OTLP standard
- Pricing: Helicone free tier + usage-based, ClawHQ $0-249/mo flat
Which Is Better for Cost Management?
If your primary goal is cost management — budgets, alerts, team allocation, optimization — ClawHQ is purpose-built for this. Helicone is primarily a logging/observability tool with basic cost display.
If you want request-level logging with caching, Helicone's proxy architecture offers that. You can also use both — Helicone for caching, ClawHQ for cost management.
