LLMtrack shows your costs the moment a request completes — not 24 to 48 hours later like your provider dashboard. Break down every dollar by feature, model, and user. One async call. No proxy. No performance hit.
Tracking costs across every major LLM provider
Provider dashboards show you a total, once a day at best. By the time you see the spike, it's already cost you. LLMtrack shows every request, every dollar, the instant it happens.
A single agent loop with bad context management can cost 10× your normal daily average. You find out when Stripe charges you — not when the loop runs.
Your provider dashboard shows a single monthly total. Not which chatbot, summarizer, or search feature is responsible. You're optimizing blind.
GPT-4o Mini might be 80% cheaper for your specific use case. But without your real token counts per request, you're guessing.
No proxy. No configuration. No new infrastructure. Drop one async call into your existing LLM code.
Provider dashboards update every 24 to 48 hours. LLMtrack ingests your request data server-side and reflects it in your dashboard under 1 second. You know about a cost spike before it compounds — not after it shows up on your invoice.
Every tracked request carries a feature_name you define. LLMtrack groups and ranks them automatically — so you instantly see that your chatbot costs 4× more per request than your summarizer, and whether it's getting worse.
LLMtrack calculates what your usage would cost on every cheaper alternative — using your real average token counts per request, not benchmarks. The savings are ranked by impact, biggest first.
LLMtrack fits a linear regression to your last 14 days of usage and projects forward through the end of next month. Set a monthly budget and the dashboard shows you exactly how many days until you exceed it — so you can act before the invoice hits.
Helicone and Langfuse are powerful. They're also built for dedicated ML ops teams with weeks to integrate. LLMtrack is for the developer shipping a SaaS product who needs answers in one afternoon.
Start free. Pay only when your usage grows. No annual lock-in. No sales calls.
For developers testing the integration or building a first AI feature.
For solo builders shipping production AI apps.
For SaaS teams with multiple models, keys, and stakeholders.
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No. The tracking call is async and fire-and-forget — you use .catch(()=>{}) so any failure is silently swallowed. It runs after your LLM response is returned to your user, on a separate async thread. Zero impact on your app's response time.
Your provider's usage dashboard is updated on their schedule — typically once per day. LLMtrack is different: when your app calls our ingest endpoint, we immediately write that record to our database. Your dashboard reads directly from that database in real time. We're not polling your provider — we're receiving your data directly.
Never. LLMtrack stores only metadata: token counts, computed cost, model name, feature name, latency, and status code. Your actual prompts and generated responses never touch our infrastructure. Row-level security at the database layer ensures complete workspace isolation.
LLMtrack maintains a server-side pricing table for every major model across OpenAI, Anthropic, Google, Mistral, Groq, and Cohere. On each ingest, we compute cost from your token counts against that table. You never need to pass a cost — but you can override it if your deployment uses custom pricing.
Three things. First: real-time data in under 1 second vs. delayed aggregation. Second: business unit economics — cost per user, break-even analysis, and margin impact — which neither competitor offers at any price tier. Third: automated model savings recommendations calculated from your actual token distribution, not generic benchmarks. Helicone and Langfuse are excellent for ML ops teams who need tracing and evals. We're built for the solo developer who needs cost answers in one afternoon.