Halvr resources
Practical guides for controlling LLM API cost.
A focused reference for engineering teams that need to understand where model spend comes from, assign it to the right customer or feature, and stop overruns before the provider invoice arrives.
Summary
Start with request-level attribution, then add project and API-key budgets in the request path. Reporting explains spend after it happens; enforcement changes the outcome before tokens are consumed.
Primary topic
LLM unit economics
Implementation
One proxy base URL
Control point
Before provider spend
Measure spend at the same level as product decisions.
Provider invoices are organized around accounts, models, and time. Product teams make decisions around customers, features, sessions, agents, and API keys. Useful cost monitoring connects those two views on every request.
Halvr accepts explicit metadata alongside the model payload, records token and cost data against that context, and keeps the request decision in the same audit trail. That makes customer margin and feature cost measurable without reconstructing them from aggregate invoices.
- Attribute tokens to customerId, feature, and sessionId.
- Separate provider cost from cache savings and blocked spend.
- Keep policy, routing, latency, and cost in one request record.
Put budgets in the request path, not in a spreadsheet.
A dashboard can show that a budget was exceeded. A control plane can prevent the next request from increasing the overrun. Halvr evaluates rolling project, API-key, customer, and feature limits before forwarding eligible traffic upstream.
Warnings are delivered before exhaustion, hard limits reject over-budget requests, and deduplicated alerts prevent the same threshold from creating noise on every call.
Use the guide that matches the decision in front of you.
Read the monitoring guide when you are defining cost ownership. Use the OpenAI guide when you are wiring an existing SDK. Use the comparison page when you are deciding between a broad observability platform and a narrower spend-control system.
Related
Read next
LLM cost monitoring
What to measure, where to enforce budgets, and how to evaluate a production tool.
OpenAI API cost tracking
Add customer and feature attribution without replacing the OpenAI SDK.
Halvr vs Helicone
A sourced comparison of cost enforcement and broad LLM observability.
Reduce OpenAI API costs
A practical sequence for attribution, caching, routing, and hard limits.
LLM budget enforcement
Design rolling dollar limits, threshold alerts, and application-safe hard blocks.
AI API cost control proxy
Understand the request-path architecture behind synchronous spend controls.
What is Halvr?
A concise product definition, request workflow, and category comparison.
Halvr architecture
The production hot path, reservation engine, event sinks, and failure policy.
Pricing reference
Current Free, Performance, and Managed-Spend commercial terms.
Security and retention
Credential encryption, data modes, outbound validation, and audit evidence.
Savings methodology
Inspect every assumption behind the public calculator and verified-savings billing.
Try Halvr
Route a real request through Halvr.
Keep your current SDK. Change the base URL, attach customer and feature metadata, and set a spend limit.