AI · alternatives · 2026
24 alternatives to Monitoring your OpenAI usage without a third party
LLM observability is an absolute must-have for anyone running something in prod (or prod-like). While all the observability startups are great, you're essentially sending all your OpenAI usage history - prompts, generations, chats - to a random third party. So this script deploys a basic proxy in your Azure account, catches all incoming OpenAI requests, stores logs in your own resource group, and comes with visualizations premade (charts, timelines, chat history, cost estimation, etc). Thanks for any thoughts and feedback!
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. Prices are shown for the 1 we have checked on their own sites, 1 of them free or with a free tier. Monitoring your OpenAI usage without a third party launched in 2023; newer entries below may have overtaken it.
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OpenTelemetry-native observability for agents and LLMs
Aug 2026 · openobserve.ai · its alternatives →
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Langfuse▲771Open source tracing and analytics for LLM applications
2023 · langfuse.com · its alternatives →
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Hey HN, Nir, Gal and Tomer here. We’re open-sourcing a set of extensions we’ve built on top of OpenTelemetry that provide visibility into LLM applications - whether it be prompts, vector DBs and more. Here’s the repo: https://github.com/traceloop/openllmetry. There’s already a decent number of tools for LLM observability, some open-source and some not. But what we found was missing for all of them is that they were closed-protocol by design, vendor-locking you to use their observability platform or their proprietary framework for running your LLMs. It’s still early in the…
2023 · github.com · its alternatives →
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Hey HN, I am proud to show you guys that I have built an open source alternative to Azure OpenAI services. Azure OpenAI services was born out of companies needing enhanced security and access control for using different GPT models. I want to build an OSS version of Azure OpenAI services that people could self host in their own infrastructure. "How can I track LLM spend per API key?" "Can I create a development OpenAI API key with limited access for Bob?" "Can I see my LLM spend breakdown by models and endpoints?" "Can I create 100 OpenAI API keys that my students could use in a classroom…
2023 · github.com · its alternatives →
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If you've built any web-based app in the last 15 years, you probably used something like Datadog, New Relic, Sentry, etc. to monitor and trace your app, right? Why should it be different when the app you're building happens to be using LLMs? So today we're open-sourcing OpenLLMetry-JS. It's an open protocol and SDK, based on OpenTelemetry, that provides traces and metrics for LLM JS/TS applications and can be connected to any of the 15+ tools that already support OpenTelemetry. Here's the repo: https://github.com/traceloop/openllmetry-js A few months ago we launched…
2024 · github.com · its alternatives →
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superlog▲497Hey HN, we’re Nico and Arseniy, co-founders of Superlog (https://superlog.sh). We're building a self-installing, self healing observability tool meant not to be opened. It has a wizard that daily sets up proper logging and an agent that investigates errors and opens PRs. Super short demo: https://www.youtube.com/watch?v=xFhU9Mk247M. In our earlier startups, we tried Sentry, Datadog, Grafana, Dash0, and nothing was good enough. Proper telemetry and alerting still requires a ton of manual setup. We struggled with adding good logs, so debugging was tough, especially as…
Jun 2026 · superlog.sh · its alternatives →
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traceAI▲273Open-source LLM tracing that speaks GenAI, not HTTP.
Apr 2026 · github.com · its alternatives →
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Hi HN, Hugh and Vince here. LLMonitor helps you record, trace & search your LLM queries and chatbot conversations. You can also capture user feedback on your frontend and correlate it with backend LLM queries then use that to fine-tune your own models. The project started has an internal tool in our previous (failed) AI startup. We’re aware the LLM observability space is very crowded. Apart from being open-source, we differentiate with: - Model-agnostic and minimal lock-in (no MITM of requests). - High focus on DX and dashboard clarity. - Support for complex scenarios: e.g. a chatbot that…
2023 · github.com · its alternatives →
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TensorZero▲307Hi HN! We're Gabriel & Viraj, and we're excited to open source TensorZero. To be a little cheeky, TensorZero is an open-source platform that helps LLM applications graduate from API wrappers into defensible AI products. 1. Integrate our model gateway 2. Send metrics or feedback 3. Unlock compounding improvements in quality, cost, and latency It enables a data & learning flywheel for LLMs by unifying: • Inference: one API for all LLMs, with <1ms P99 overhead • Observability: inference & feedback → your database • Optimization: better prompts, models, inference strategies • Experimentation:…
2025 · github.com · its alternatives →
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Hi hacker news, My name is Dillion and I'm the creator of llm.report. A few months ago, I was frustrated by the lack of observability into the OpenAI API. All of us are left in the dark about API performance, latency, cost calculation, cost breakdown, and more. I just wanted to know more about how my AI app is performing in production and make data-driven decisions to improve the product. So I ended up just building it myself. There are three parts to the platform: 1. OpenAI API Dashboard (no-code) - Enter your OpenAI key and get access to detailed insights straight from the OpenAI API…
2023 · github.com · its alternatives →
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Hey HN, we're super excited to share something we've been working on: OpenLIT. After an engaging preview that some of you might recall, we are now proudly announcing our first stable release! *What's OpenLIT?* Simply put, OpenLIT is an open-source tool designed to make monitoring your Large Language Model (LLM) applications straightforward. It’s built on OpenTelemetry, aiming to reduce the complexities that come with observing the behavior and usage of your LLM stack. *Beyond Basic Text Generation:* OpenLIT isn’t restricted to just text and chatbot outputs. It now includes automatic…
2024 · github.com · its alternatives →
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LLM Gatewayfree tier▲296I built an OpenAI-compatible LLM gateway that routes requests to OpenAI, Anthropic, Ollama, vLLM, llama-server, SGLang... anything that speaks /v1/chat/completions. Single Go binary, one YAML config file, no infrastructure. It does the things you'd expect from this kind of gateway... semantic routing via a three-layer cascade (keyword heuristics, embedding similarity, LLM classifier) that picks the best model when clients omit the model field, weighted round-robin load balancing across local inference servers with health checks and failover. The part I think is most…
2025 · llmgateway.io · its alternatives →
- 16AT
I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…
2025 · llmapitest.com · its alternatives →
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Hi HN! Langfuse is OSS observability and analytics for LLM applications (repo: https://github.com/langfuse/langfuse, 2 min demo: https://langfuse.com/video, try it yourself: https://langfuse.com/demo) Langfuse makes capturing and viewing LLM calls (execution traces) a breeze. On top of this data, you can analyze the quality, cost and latency of LLM apps. When GPT-4 dropped, we started building LLM apps – a lot of them! [1, 2] But they all suffered from the same issue: it’s hard to assure quality in 100% of cases and even to have a clear view…
2023 · github.com · its alternatives →
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Hi HN! We’re the team behind Fiddler, and we recently built an Agent Cache feature. It works by capturing an LLM request/response pair and, once caching is enabled, serving subsequent matching calls locally so duplicate requests never hit the provider. It’s meant to reduce three pains that show up together in agent development: token cost, feedback-loop latency, and output non-determinism. We’d love to hear your thoughts.
Aug 2026 · telerik.com · its alternatives →
- 19Langtrace AI▲119
Improve your LLM apps with open-source observability tool
2024 · langtrace.ai · its alternatives →
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Analyze your OpenAI API usage and logs in real-time
2023 · llm.report · its alternatives →
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Hi everyone, we’re a small team, supported by Mozilla, who are working on re-imagining a UI for training, tuning and testing local LLMs. Everything is open source. If you’ve been training your own LLMs or have always wanted to, we’d love for you to play with the tool and give feedback on what the future development experience for LLM engineering could look like.
2025 · github.com · its alternatives →
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Perssua▲61Real-time guidance from any LLM (including local ones)
Nov 2025 · perssua.com · its alternatives →
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Hi HN, we're Kiran and Vijay! Over the past two years, we have built a columnar storage engine for observability: logs, metrics, and traces. Today, it's exciting for us to show what we've built on top of that foundation: LLM Agent Observability. Given how non-deterministic agents are, storing all traces without sampling was critical for us. But these traces tend to be in the MBs, sometimes GBs - we needed to store them inexpensively. We also needed the queries and analyses to be fast. To meet both these goals, we store them in S3 in our own parquet-like file format, and query them using AWS…
Jul 2026 · oodle.ai · its alternatives →
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OneGlanse▲77Free open-source GEO tracker for LLM visibility
Apr 2026 · oneglanse.com · its alternatives →
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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →