AI · alternatives · 2026

24 alternatives to UpTrain (YC W23) – open-source tool to evaluate LLM response quality

Hello, we are Shikha and Sourabh, founders of UpTrain(YC W23) - an open-source tool to evaluate the performance of your LLM applications on aspects such as correctness, tonality, hallucination, fluency, etc. The Problem: Unlike traditional Machine learning or Deep learning models where we always have a unique Ground Truth and can define metrics like Precision, Recall, accuracy, etc. to quantify the model’s performance, LLMs are trickier and it is very difficult to estimate if their response is correct or not. If you are using GPT-4 to write a recruitment email, there is no unique correct…

UpTrain is an open-source tool that evaluates the quality of large language model responses across dimensions like correctness, tonality, hallucination, and fluency. It is designed for developers building LLM… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes. UpTrain (YC W23) – open-source tool to evaluate LLM response quality launched in 2023; newer entries below may have overtaken it.

1
UpTrain – A Practical Approach to Finetuning LLMs for Custom Use-Cases▲10
2023 · github.com · its alternatives →
2
UpTrain – Open-source ML observability and refinement tool▲88
A couple of months ago, we left our jobs to build UpTrain AI, an open-source machine learning observability and refinement tool which helps users understand the performance of their models in production and improves them over time by identifying problematic data-points for retraining. Data drift, Distribution shifts, Model degradation, Edge cases - we have personally faced these problems in our previous organizations and have built a lot of tooling to solve them. We are building UpTrain so that others don’t need to build them and can solely focus on improving their ML models while we…
2023 · github.com · its alternatives →
3
LLM Stats▲293
I was overspending on GPT-4o. It was really hard to compare different models I could switch to, so I built this LLM comparison tool. It shows leaderboards, pricing, and performance data across 100+ LLMs (including all major providers and open-source models). Key features: - Live pricing comparisons - Benchmark Scores (MMLU, HumanEval, GPQA, etc.) - Context length vs cost analysis - Speed/throughput tests across providers - Quality vs price visualizations - Open source (all data verifiable) Try it out: https://llmstats.com I'd like to know your opinion :) Tech stack: Next.js,…
Oct 2025 · llm-stats.com · its alternatives →
4
TensorZero▲307
Hi 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 →
5
TuneTrain.ai▲79
Do you know that Small Language Models (SLM) can outperform LLMs if trained (fine-tuned) on domain specific datasets, like your companies knowledge base? The issue: fine-tuning a SLM usually requires a dataset of 10k - 100k records, which is huge. I created a platform for applying "human in the loop" augmentation techniques on your small dataset, so that you can start with maybe 100 records and build-up quickly huge datasets and launch a training without prior knowledge. I implemented 2 techniques, based on LLM distillation, however: more to come. HN, what Do you think? Do you see value in…
2025 · tunetrain.ai · its alternatives →
6
Countless.dev – A website to compare every AI model: LLMs, TTSs, STTs▲361
Compare AI models easily! All providers in one place. Find the best LLM for your needs with our comprehensive pricing calculator and feature comparison tool. OpenAI, Anthropic, Google, and more.
2024 · countless.dev · its alternatives →
7
I implemented evals metrics for LLMs that runs locally on your machine▲22
The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
8
ChainForge, a visual tool for prompt engineering and LLM evaluation▲177
Hi HN! We’re been working hard on this low-code tool for rapid prompt discovery, robustness testing and LLM evaluation. We’ve just released documentation to help new users learn how to use it and what it can already do. Let us know what you think! :)
2023 · chainforge.ai · its alternatives →
9
DeepEval – Evaluation and Unit Testing for LLMs▲18
The LLM Evaluation Framework. Contribute to confident-ai/deepeval development by creating an account on GitHub.
2023 · github.com · its alternatives →
10
LLM Explorer▲152
Find your best LLM for a local inference
2023 · its alternatives →
11
Open-source model and scorecard for measuring hallucinations in LLMs▲65
Hi all! This morning, we released a new Apache 2.0 licensed model on HuggingFace for detecting hallucinations in retrieval augmented generation (RAG) systems. What we've found is that even when given a "simple" instruction like "summarize the following news article," every LLM that's available hallucinates to some extent, making up details that never existed in the source article -- and some of them quite a bit. As a RAG provider and proponents of ethical AI, we want to see LLMs get better at this. We've published an open source model, a blog more thoroughly describing our methodology (and…
2023 · vectara.com · its alternatives →
12
LLM Aided Transcription Improvement▲11
I recently submitted another project for using LLMs to correct errors and improve formatting of OCRed documents which was well received. The low cost and high quality/speed of the latest "value tier" models from OpenAI and Anthropic have made it possible to get compelling results at a very reasonable price in that application. It occured to me that the same approach taken there (namely, splitting documents into chunks and sending each chunk through a chain of LLM prompts that each take the output of the previous prompt and apply an additional layer of processing) could be easily applied…
2024 · github.com · its alternatives →
13
Improve Text-to-SQL Accuracy with LLM▲17
A repository that contains models, datasets, and fine-tuning techniques for DB-GPT, with the purpose of enhancing model performance in Text-to-SQL - eosphoros-ai/DB-GPT-Hub
2023 · github.com · its alternatives →
14
Fine-tune an 8B model on a 4 GB laptop GPU▲139
Fine-tune LLMs from one YAML. Layer streaming trains an 8B model on a 4 GB laptop GPU. - MakazhanAlpamys/Soup
Aug 2026 · github.com · its alternatives →
15
Toolkit for LLM Fine-Tuning, Ablating and Testing▲14
Hello all! Very happy to share this toolkit that allows you to fine-tune your choice of open-source LLMs on your data! The toolkit also allows you to run ablation studies across LLMs, prompt designs, training configurations, and can ingest different data files -- all through just ONE YAML file! After fine-tuning, you can also run a bunch of tests to ensure that the fine-tuned LLM behaves as expected, enabling faster time-to-production! Why this toolkit? Why now? While closed-source LLMs have become popular for chat-based applications, enterprises are considering a shift to self-hosted SLMs…
2024 · github.com · its alternatives →
16
Confident AI▲86
all-in-one LLM evaluation platform
2024 · confident-ai.com · its alternatives →
17
Improve LLM Performance by Maximizing Iterative Development▲104
I have been working in AI space for a while now, first at FAANG with ML since 2021, then with LLM in start-ups since early 2023. I think LLM Application development is extremely iterative, more so than any other types of development. This is because to improve an LLM application performance (accuracy, hallucinations, latency, cost), you need to try various combinations of LLM models, prompt templates (e.g., few-shot, chain-of-thought), prompt context with different RAG architecture, different agent architecture, and more. There are thousands of possible combinations and you need a process…
2024 · github.com · its alternatives →
18AP
Automatic prompt optimizer for LLMs▲126
2023 · promptperfect.jina.ai · its alternatives →
19
LLM Ops Toolkit by Lamatic.ai▲96
Aggregate uptime monitoring across OpenAI, Claude, and more
Apr 2026 · tools.lamatic.ai · its alternatives →
20
LLM, a Rust Crate/CLI for CPU Inference of LLMs (LLaMA, GPT-NeoX, etc.)▲45
G'day, HN! I'm one of the maintainers of `llm`. I've been working alongside a trusty group of contributors to bring this project to life, and we're now at a point where we're ready to share it with the world. Large language models (LLMs) are taking the computing world by storm due to their emergent abilities that allow them to perform a wide variety of tasks, including translation, summarization, code generation, and even some degree of reasoning. However, the ecosystem around LLMs is still in its infancy, and it can be difficult to get started with these models. `llm` is a one-stop shop for…
2023 · github.com · its alternatives →
21
Open-Source Alternative to OpenAI Platform, for Local Models▲93
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 →
22CD
Cancer diagnosis makes for an interesting RL environment for LLMs▲46
Hey HN, this is David from Aluna (YC S24). We work with diagnostic labs to build datasets and evals for oncology tasks. I wanted to share a simple RL environment I built that gave frontier LLMs a set of tools that lets it zoom and pan across a digitized pathology slide to find the relevant regions to make a diagnosis. Here are some videos of the LLM performing diagnosis on a few slides: (https://www.youtube.com/watch?v=k7ixTWswT5c): traces of an LLM choosing different regions to view before making a diagnosis on a case of small-cell carcinoma of the lung…
Nov 2025 · its alternatives →
23
Fine-grained stylistic control of LLMs using model arithmetic▲85
We developed a new framework that enables flexible control of generated text in language models. By combining several models and/or system prompts in one mathematical formula, it lets you tweak your style and combine model outputs with ease. A handy tool for those working with LLMs, looking for more fine-grained control of stylistic output. More details in our paper: https://arxiv.org/abs/2311.14479. Feedback and potential applications are welcome.
2023 · github.com · its alternatives →
24
finetune LLMs via the Finetuning Hub▲80
Hi HN community, I have been working on benchmarking publicly available LLMs these past couple of weeks. More precisely, I am interested on the finetuning piece since a lot of businesses are starting to entertain the idea of self-hosting LLMs trained on their proprietary data rather than relying on third party APIs. To this point, I am tracking the following 4 pillars of evaluation that businesses are typically look into: - Performance - Time to train an LLM - Cost to train an LLM - Inference (throughput / latency / cost per token) For each LLM, my aim is to benchmark them for…
2023 · 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 →