Dev tools · alternatives · 2026
24 alternatives to Observyze
Trace, debug, and price every prompt — in real-time
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
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OpenTelemetry-native observability for agents and LLMs
Aug 2026 · openobserve.ai · its alternatives →
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TraceRoot.AI▲342Fix bugs faster with open source, AI native observability
2025 · traceroot.ai · 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 →
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Langfuse▲771Open source tracing and analytics for LLM applications
2023 · langfuse.com · its alternatives →
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Middleware▲633Observability copilot to resolve production issues instantly
2023 · middleware.io · 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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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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VoltOps▲111Trace, debug, and monitor AI agents apps in n8n-style
2025 · github.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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Foglamp▲101The missing observability layer for AI agents built on the Vercel AI SDK. Costs, latency, tokens, distributed traces, evals, and alerts for every generateText / streamText call — in two lines.
Jun 2026 · foglamp.dev · 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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An open source observability stack for your backends
2023 · autometrics.dev · its alternatives →
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Tracium.ai▲70Hi everyone, thanks for checking out tracium. At work I needed some clarity on how much each agent costs us and how they perform on production so I started looking at existing solutions for this such as LangSmith and LangFuse. Honestly, they felt like they are overcomplicated to integrate and that there should be a simpler solution so I decided to build Tracium. Tracium keeps track of every single AI API call that you make to an LLM provider, with this you can see traces, spans, I/O, tokens in/out, latency. You can compare during A/B testing, see usage from each customer and…
Mar 2026 · tracium.ai · its alternatives →
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- 18Langtrace AI▲119
Improve your LLM apps with open-source observability tool
2024 · langtrace.ai · its alternatives →
- 19
Hello Everyone, I'm excited to introduce a new open-source observability platform and would love to hear your feedback. We are aware that there are lots of open-source/commercial tools out there. However, we believe that monitoring the clusters and extracting actionable insights requires deep know-how about the tools/domain. We mainly focused on this problem. - Alaz is an eBPF agent installed on your K8s cluster as DaemonSet. Thanks to eBPF, Alaz collects traces directly from Linux kernels. This means there's no need for sidecars, instrumentations, or service restarts. - The UI not…
2023 · github.com · its alternatives →
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Uptrace is an all-in-one tool that supports distributed tracing, metrics, and logs. It uses OpenTelelemetry observability framework to collect data and ClickHouse database to store it. You can ingest data using OpenTelemetry Protocol (OTLP), Vector Logs, and Zipkin API. You can also use OpenTelemetry Collector to collect Prometheus metrics or receive data from Jaeger, X Ray, Apache, PostgreSQL, MySQL and many more. The latest Uptrace release introduces support for OpenTelemetry Metrics which includes: - User interface to build table-based and grid-based dashboards. - Pre-built dashboard…
2022 · app.uptrace.dev · 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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Dynamically instrument Live applications from your IDE
2023 · lightrun.com · its alternatives →
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Hey HN! Sam, Alex and Ben from Invaria (formerly Patchwork Technologies): http://invaria.io. Observability is built on telemetry (metrics, logs and traces). Telemetry data quality is often poor because instrumenting code is still a time consuming task which requires a lot of hand-finishing. We believe that structured event data is the right end goal, and we’re starting by making it easier to get there using structured logging. For our Launch HN (Aug), we set up a demo instance with pre-baked results. We wanted to go further to allow HN readers to give the product a proper test…
2024 · its alternatives →
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Hi HN, I'm Kaushik, and I built Rocketgraph. I believe that while other spaces have caught up to the AI wave, the observability space is still lagging behind, using the same tools and dashboards that we use to analyse logs from human-written code. But now the code is written and debugged by AI, so we need to rethink how we do observability where the observer itself is an AI. The problem that I run into is when an alert fires, I have to manually check the Grafana dashboards and write LogQL queries, which is pretty much like greping. But production usually breaks due to a schema mismatch, or a…
Jun 2026 · github.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 →