LLM Evaluation and Observability Tools Compared
LLM evaluation and observability tools fall into three groups: open-source libraries you run in your own code and CI (Promptfoo, DeepEval, Ragas), tracing-first platforms with evaluation on top (Langfuse, LangSmith, Arize Phoenix), and evaluation-first platforms (Braintrust). Choose by where you need to run it, whether you must self-host, and whether evaluation has to sit next to the rest of your QA.
Teams searching for "LLM evaluation tools" or "Langfuse alternatives" usually want to know two things: which tools exist, and which one fits their constraints. This page answers both, including where NexGen QA is not the right choice. For the practice itself, start with the LLM evaluation guide.
The questions that decide it
- Do you need to self-host, or is a hosted service acceptable for your data?
- Is your main need offline evaluation in CI, production tracing, or both?
- Who runs it: engineers in code, or a mixed team that needs a UI, review queues and reports?
- Do you need governance artifacts (evidence exports, audit logs, SSO) for reviewers outside engineering?
- Does AI evaluation have to live next to your other QA (API, security, accessibility, performance)?
The tools at a glance
| Tool | Type | Known for | Consider it when |
|---|---|---|---|
| Promptfoo | Open-source CLI and library | Declarative prompt evals, red teaming, a GitHub Action for before/after comparison on pull requests | You want evals and red teaming as code in CI with no platform |
| DeepEval | Open-source Python framework | pytest-style test cases, a broad metric library including G-Eval and RAG metrics; Confident AI is its hosted platform | Your team writes Python tests and wants many ready-made metrics |
| Ragas | Open-source Python library | RAG metrics such as faithfulness and context precision | You mainly need RAG scoring inside your own pipeline |
| Langfuse | Open-source platform, hosted or self-hosted | Tracing, prompt management, datasets, evaluations and annotation | You want an open-source platform you can self-host |
| LangSmith | Hosted platform from LangChain | Tracing, datasets and evaluation, closely integrated with LangChain and LangGraph | You build on LangChain or LangGraph |
| Arize Phoenix | Open-source, OpenTelemetry-based | Tracing and evaluation built on OpenTelemetry and OpenInference; Arize AX is the commercial platform | You want OTel-native tracing you can run locally or self-host |
| Braintrust | Hosted platform | Evaluation-first: experiments, datasets, scorers, prompt playground, CI integration | Offline evaluation and experiment comparison are your core need |
| NexGen QA | Hosted platform (no self-hosting) | AI evaluation and tracing in the same workspace as 35+ software QA modules, on your own model keys | You want AI evaluation, red teaming and evidence next to the rest of your QA |
Open-source libraries: Promptfoo, DeepEval, Ragas
Libraries are the fastest way to start and the easiest to control: evaluation logic lives in your repository, runs in your CI, and sends data only to the model providers you call. What they do not give you by default is a shared place for datasets, traces, human review and history across teams. Many teams use a library in CI and a platform for production tracing and review.
Tracing-first platforms: Langfuse, LangSmith, Phoenix
These start from production traces and add datasets, evaluations and prompt management. Langfuse and Phoenix are open source and can be self-hosted, which matters when data must stay in your environment. LangSmith is the natural fit for LangChain and LangGraph users. All three accept or interoperate with OpenTelemetry to some degree; instrumenting against the OpenTelemetry GenAI conventions keeps switching costs low (see LLM observability with OpenTelemetry).
Evaluation-first platforms: Braintrust
Evaluation-first platforms put experiments, datasets and scorers at the centre and are strong at comparing variants and gating changes in CI. If your main question is "is this prompt or model change better", this group is built for it.
Where NexGen QA fits, and where it does not
NexGen QA combines tracing (OpenTelemetry over OTLP/HTTP, with SDK setup for LangChain, LlamaIndex, the OpenAI Agents SDK, OpenAI and Anthropic), conversation sessions, online evaluations of spans and whole conversations (Pro), drift monitors (Pro), a prompt optimizer whose candidates a person accepts (Pro), annotation queues, prompt management, the Evaluation Kit (versioned datasets, scorers, feedback API, monitors, MCP checks, calibration, an OpenAPI-described REST API, and Python and TypeScript SDKs and CLI), RAG metrics, red-team campaigns mapped to the OWASP LLM Top 10, agent execution against your endpoint, multimodal evaluation, release gates and hash-chained evidence, in the same workspace as API, security, accessibility, performance and other QA modules. Every AI call uses your own provider key, and Pro adds customer controls such as your own SSO, storage bucket, retention, export, verified deletion, model allow-lists with a spend limit, and SIEM streaming.
It is not the right choice if you need to self-host the whole platform: NexGen hosts the application, its database and orchestration, and customer storage covers bucket-backed files only. Other current limits: the SDKs are not yet published to PyPI or npm; there is no model gateway or proxy; code scorers are JavaScript only and run in a WebAssembly sandbox inside the hosted service rather than a separate container per customer; and the prompt optimizer runs one round at a time and only creates a version when a person accepts a candidate. If one of those is essential, one of the tools above is a better fit today. See AI evaluation in NexGen QA for what each plan includes.
A fair way to choose
- Write down your hard constraints (self-hosting, data residency, SSO, budget).
- Take 30 real cases from your product and one real trace flow.
- In each shortlisted tool, build the same dataset, run the same two scorers, compare two prompt versions, and send the same traces.
- Compare the time it took, what a reviewer outside engineering can see, and what you would export for an auditor.
Frequently asked questions
What are the main LLM evaluation tools?
Open-source libraries such as Promptfoo, DeepEval and Ragas; tracing-first platforms such as Langfuse, LangSmith and Arize Phoenix; and evaluation-first platforms such as Braintrust. NexGen QA combines evaluation and tracing with software QA modules.
Which LLM evaluation tools can be self-hosted?
Open-source tools such as Langfuse, Arize Phoenix, Promptfoo, DeepEval and Ragas can run in your own environment. NexGen QA is hosted only. Check each vendor's current documentation for its options.
Is NexGen QA a Langfuse alternative?
For teams that want tracing, evaluation, red teaming and evidence next to their other QA on a hosted service, yes. For teams that must self-host, Langfuse or Phoenix is the better fit.
Sources
NexGen QA puts AI evaluation (tracing, datasets and scorers, red teaming, human review, release gates and evidence) next to 35+ software testing modules, on your own model keys.
Related reading
More articles
- How to Evaluate an LLM Application, Step by Step
An eight-step process for evaluating LLM apps: success criteria, error analysis, a versioned test set, scorers, baselines, specialised suites and production checks.
- LLM Evaluation Metrics: Which to Use and When
The four families of LLM evaluation metrics (deterministic checks, reference similarity, judge-based scores and human ratings) and which to use for each task.
- How to Build an LLM Evaluation Dataset
What goes in a golden dataset for LLM evaluation, where cases come from, how many you need (with confidence intervals), and how to version and split it.
Published by the NexGen QA team at Techtonic Innovations LLC · About NexGen QA