Observed Signal · Apr 4, 2026 · Open Source Release · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
Developer builds LLMeter to track LLM bills
A developer built and open-sourced LLMeter, a dashboard that polls LLM provider usage APIs hourly, normalizes disparate usage formats into a Postgres schema, and shows actual costs by provider and model. The stack uses Inngest for hourly jobs, Supabase Postgres for storage and auth, and a Next.js + Shadcn UI frontend. LLMeter supports OpenAI, Anthropic, DeepSeek and OpenRouter, encrypts provider API keys at rest with AES-256-GCM, and provides budget alerts. Running LLMeter revealed ~70% of the author's spend came from a single background job using gpt-4o; fixing it saved an estimated $200/month. The project is available under AGPL-3.0 on GitHub (github.com/amedinat/LLMeter) and via llmeter.org for self-hosting or a free tier.
Practical open-source tool that improves observability and cost management for LLM usage across providers; useful to developers and small teams but not industry-shifting.
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Key Takeaways & Evidence Grounding
- Author built LLMeter to poll provider usage APIs hourly and normalize usage/cost data into a single Postgres schema.
- Tech stack: Inngest for polling jobs, Supabase (Postgres + auth) for database, Next.js with Shadcn UI for frontend.
- LLMeter supports OpenAI, Anthropic, DeepSeek and OpenRouter and breaks down costs by provider and model.
- Provider API keys are encrypted at rest using AES-256-GCM; polling retries use exponential backoff up to three times.
- Project is open-source under AGPL-3.0 and available at github.com/amedinat/LLMeter and llmeter.org.
Connected Companies & Entities
4 Entities mappedRelated Market Signals & Shifts
Recent verified developments and strategic activity across this market segment.
Teams Waste 43% of LLM API Budgets
A DEV Community post by John Medina (May 8, 2026) reports that analysis across several teams found about 43% of LLM API spend is wasted due to architectural issues rather than pure usage. Identified causes include 'retry storms' (repeated failed requests), duplicate calls (lack of caching), context bloat (sending oversized prompts), and wrong model selection. The author introduced LLMeter, an open-source (AGPL-3.0) dashboard to track per-customer and per-model costs and claims basic tenant-level breakdowns and budget alerts can reduce bills by ~20% in the first week.
Rebuilding an accurate LLM subscription usage meter
An engineer describes building a reliable usage meter after an overnight automation exhausted their Claude Code subscription and triggered a multi-day lockout. The author iterated through four versions: a fabricated dollar budget, a raw percentage from Anthropic's usage endpoint, a time-based "reach" gauge showing projected days remaining, and a statistically grounded projection using a Jeffreys prior (Gamma posterior) to avoid noisy early-week false alarms. The final meter also detects stale or missing input data, reports freshness, refreshes every 15 minutes, and refuses to guess when inputs are invalid. The post highlights two broader lessons: plausible but invented numbers are dangerous, and meters must signal when their inputs are unavailable.
Open Dataset Tracks Monthly LLM Pricing for 22 Models
An author at AIscending published an open dataset that tracks monthly pricing and metadata for 22 LLM/AI models across four tiers (Frontier, Efficiency, Reasoning, Open Source). Each model record includes price per 1M tokens (prompt, completion, blended), context window size, and provider information. The dataset is updated automatically on the 1st of each month by pulling standardized pricing from the OpenRouter API and preserves historical snapshots. The project exposes two composite indices—AI CPI (Cost Pressure Index) and a Budget Index—that summarize market-wide cost trends and efficiency-to-frontier savings. Data files (JSON/CSV) and the repo (github.com/AIscending/llm-pricing-index) are openly available with attribution; the initial snapshot covers April 2026.
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