Show HN: AImeter shows you how much money your agents are burning

Your AI agents are burning money. AIMeter shows you exactly how much. A typical AI agent setup using GPT-4o looks like it costs ~$50/month. The real number is closer to $800. Hidden costs add up fast: verbose system prompts resent on every call, silent retries, tool calls that invoke expensive models, and zero visibility into what each agent actually spends. We ran 10 identical tasks across 5 models. Here's what we found: | Model | Cost for 10 tasks | vs. Cheapest | |---|---|---| | GPT-4o | $0.0617 | 16x | | Claude Sonnet 4 | $0.0912 | 24x | | GPT-4o-mini | $0.0038 | 1x (baseline) | | Claude Haiku 4.5 | $0.0041 | 1.1x | | GPT-4.1-nano | $0.0024 | baseline | At 1,000 calls/day, choosing GPT-4o over GPT-4.1-nano costs an extra $131/month for the same tasks. AIMeter is a lightweight Python SDK that tracks every LLM call, calculates the real cost, and connects it to business outcomes. Zero dependencies. Two lines of code. Works offline. pip install aimeter[openai] import openai from aimeter import track_openai, MemoryExporter, configure # 1. Set up tracking mem = MemoryExporter() configure(project="my-agent", exporters=[mem]) # 2. Wrap your client (one line) client = track_openai(openai.OpenAI(), project="my-agent") # 3. Use it normally — costs are tracked automatically response = client.chat.completions.create( model="gpt-4o", messages=[{"role": "user", "content": "Summarize this support ticket..."}], ) # See what you spent print(f"Cost: ${mem.total_cost:.4f}") print(f"Tokens: {mem.total_tokens}") print(mem.summary()) Every LLM call automatically records: | Token costs | Input, output, and cached token counts with USD breakdown | | Model & provider | Which model handled each call (GPT-4o, Claude Sonnet 4, etc.) | | Latency | Per-call duration in milliseconds | | Tool calls | Function/tool names invoked (names only — never arguments, for privacy) | | Errors | Failed calls with error messages and cost of retries | | Outcomes | Link agent costs to business results: "this call resolved a $12.50 ticket" | Privacy by default — AIMeter tracks cost metadata only. No message content, prompts, or tool arguments are ever captured. | Framework | Status | Adapter | |---|---|---| | OpenAI SDK | Supported | track_openai() | | Anthropic SDK | Supported | track_anthropic() | | Any LLM | Supported | track_llm_call() context manager | | LangChain | Planned | Callback handler | | CrewAI | Planned | | | AutoGen | Planned | # OpenAI from aimeter import track_openai client = track_openai(openai.OpenAI(), project="support-agent") # Anthropic from aimeter import track_anthropic client = track_anthropic(anthropic.Anthropic(), project="research-agent") # Any LLM (manual instrumentation) from aimeter import track_llm_call with track_llm_call(provider="cohere", model="command-r-plus") as call: response = my_llm_call(...) call.input_tokens = response.meta.tokens.input_tokens call.output_tokens = response.meta.tokens.output_tokens This is what makes AIMeter different. Not just "how much did I spend?" but "how much did each business result cost?" from aimeter import record_outcome # After your agent resolves a support ticket record_outcome( run_id="run-123", outcome="ticket_resolved", value_usd=12.50, metadata={"ticket_id": "T-1234", "resolution_time_min": 3}, ) # Now you know: this ticket cost $0.05 in LLM calls # and delivered $12.50 in value. ROI: 250x. AIMeter ships with built-in pricing for OpenAI, Anthropic, Google, and Mistral. Need more? from aimeter import CostRegistry registry = CostRegistry() # Pull 300+ models from litellm's community-maintained registry registry.update_from_litellm() # Or fetch from your own endpoint registry.update_from_url("https://aimeter.ai/api/pricing.json") # Or set manually registry.register("mycloud", "my-model", ModelPricing( input_per_1k=0.001, output_per_1k=0.002 )) The SDK never phones home by default. Remote pricing updates are always opt-in. ┌─────────────────────────────────────────────┐ │ Your Agent Code │ │ (OpenAI / Anthropic / LangChain / Custom) │ │ │ │ client = track_openai(openai.OpenAI()) │ <- 1 line to add └──────────────────┬───────────────────────────┘ │ records LLMEvent (tokens, cost, latency) ▼ ┌─────────────────────────────────────────────┐ │ AIMeter SDK (in-process) │ │ │ │ ┌──────────┐ ┌───────────┐ ┌────────────┐ │ │ │ Cost │ │ Tracker │ │ Outcome │ │ │ │ Registry │ │ (enrich + │ │ Attribution│ │ │ │ (300+ │ │ export) │ │ │ │ │ │ models) │ │ │ │ │ │ │ └──────────┘ └─────┬─────┘ └────────────┘ │ └─────────────────────┼───────────────────────┘ │ ┌─────────┼─────────┐ ▼ ▼ ▼ ┌────────┐ ┌────────┐ ┌────────┐ │Console │ │Memory │ │ HTTP │ │(stderr)│ │(local) │ │(cloud) │ <- future └────────┘ └────────┘ └────────┘ Zero dependencies. The core SDK uses only Python stdlib. Framework adapters (OpenAI, Anthropic) are optional extras. pip install aimeter # core only — zero deps pip install aimeter[openai] # + OpenAI SDK pip install aimeter[anthropic] # + Anthropic SDK pip install aimeter[all] # everything from aimeter import configure, MemoryExporter mem = MemoryExporter() configure( project="my-agent", tags={"team": "cx", "env": "prod"}, exporters=[mem], ) Or via environment variables: export AIMETER_PROJECT=my-agent export AIMETER_EXPORT=console # or "memory" export AIMETER_DEBUG=true # log unknown models export AIMETER_ENABLED=false # kill switch See the examples/ directory: - Model Comparison — Run the same tasks across GPT-4o, GPT-4o-mini, GPT-4.1-nano, Claude Sonnet 4, and Claude Haiku 4.5. Generates a screenshot-ready cost report. We're building the financial infrastructure for AI agents — the Datadog + Stripe of the agentic era. And we'd love your help. See CONTRIBUTING.md for how to get started. Good first issues: - Add a new framework adapter (LangChain, CrewAI, AutoGen) - Add a new exporter (file, HTTP, OpenTelemetry) - Update model pricing in the cost registry - Improve the terminal report formatting See ROADMAP.md for the full plan. Now: SDK with cost tracking, outcome attribution, OpenAI + Anthropic adapters Next: LangChain/CrewAI adapters, streaming support, file exporter, CLI report command Later: Hosted dashboard, billing-as-a-service, agent marketplace economics Apache 2.0 — use it in production, fork it, build on it. No strings attached.