Spanlens is an open source observability tool for LLM apps. You point your OpenAI, Anthropic, or Gemini client at the Spanlens proxy by changing the baseURL, and it records every request with the full body, token counts, cost, and latency.
The dashboard shows per-model costs, latency percentiles, and error rates. Agent workflows appear as traces with a timeline view and a graph view that marks the critical path. There is also prompt versioning with A/B experiments, an LLM-as-judge eval runner, anomaly alerts, and a scanner that flags PII and prompt injection in request bodies.
The whole codebase is MIT licensed. You can use the hosted version or run it yourself with Docker Compose. SDKs exist for JavaScript and Python, and OpenTelemetry traces can be ingested over OTLP.
Real-time translation
Spanlens offers real-time translation capabilities, allowing users to quickly convert speech or text between Spanish and other languages, which is useful for immediate communication needs.
Language learning support
The platform appears designed to assist users in learning Spanish, providing tools that combine translation with educational features to help build vocabulary and comprehension.
Accessibility
As a web-based tool, Spanlens is accessible from any device with an internet connection, eliminating the need for software installation and making it convenient for on-the-go use.
User-friendly interface
The platform is designed with simplicity in mind, making it easy for users of varying technical skill levels to navigate and utilize its translation and learning features.
Focused niche
By specializing in Spanish language tools, Spanlens can potentially offer more refined and accurate features compared to general-purpose translation apps that cover many languages.
I was building LLM apps on the side and kept pasting token counts into a spreadsheet to figure out what each feature cost me. The tools I tried were either acquired mid-migration, closed source, or heavier to self-host than the app I was trying to monitor. So in April 2026 I started building the tool I actually wanted: change one line, see every request. It launched in June 2026, and the whole codebase went up on GitHub under MIT from day one.
The entire product is MIT licensed, including the dashboard, evals, and prompt A/B testing. There is no separate enterprise edition. Everything ships in one repo you can run with a single Docker Compose file. Integration is one line: you change the baseURL on your OpenAI, Anthropic, or Gemini client, and every call gets logged with its full body, token counts, cost, and latency. A few things that are usually paid add-ons come built in, like agent traces with a critical path view, A/B tests that use Welch's t-test to tell you whether a difference is real, and a recommender that flags cheaper models based on the traffic you actually send.
Mostly because of where the market went. Helicone was acquired, LangSmith is closed source, and self-hosting Langfuse takes real setup work. Spanlens fills the gap those tools left: setup in about five minutes, one Docker Compose file if you want the data on your own servers, and no feature gating between free and paid tiers. To be fair, if you need SOC 2 reports and enterprise support today, the bigger platforms are ahead. If you want request logs, costs, and traces without adopting a heavy platform, that is what Spanlens was built for.
Developers who ship LLM features in production apps. The typical user is a solo developer or a small team that added OpenAI or Anthropic calls to their product and now has no clear picture of what those calls cost or why some are slow. Agent builders are the second group, since multi-step workflows are hard to debug without traces. It is a developer tool through and through: if you don't touch code, you won't get much out of it.
We don't publish customer names yet. Spanlens launched in June 2026, and most users so far are indie developers and small AI teams.
TypeScript across the stack. The dashboard is Next.js, the API and proxy run on Hono, and data is split between Supabase Postgres for accounts and relational data and ClickHouse for request logs, which grow fast. The repo is a pnpm monorepo that also holds the JavaScript and Python SDKs, a CLI, and an MCP server. Self-hosting runs on Docker Compose, and OpenTelemetry traces can be ingested over OTLP.
I don't have verified, reliable information about Spanlens (spanlens.io) to make a confident assessment of its quality, legitimacy, or performance. Before using or purchasing from this service, I'd recommend conducting independent research.
We have collected here some useful links to help you find out if Spanlens is good.
Check the traffic stats of Spanlens on SimilarWeb. The key metrics to look for are: monthly visits, average visit duration, pages per visit, and traffic by country. Moreoever, check the traffic sources. For example "Direct" traffic is a good sign.
Check the "Domain Rating" of Spanlens on Ahrefs. The domain rating is a measure of the strength of a website's backlink profile on a scale from 0 to 100. It shows the strength of Spanlens's backlink profile compared to the other websites. In most cases a domain rating of 60+ is considered good and 70+ is considered very good.
Check the "Domain Authority" of Spanlens on MOZ. A website's domain authority (DA) is a search engine ranking score that predicts how well a website will rank on search engine result pages (SERPs). It is based on a 100-point logarithmic scale, with higher scores corresponding to a greater likelihood of ranking. This is another useful metric to check if a website is good.
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