AI · alternatives · 2026
24 alternatives to Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment
Hey HN! I’m the founder of Unify, and we’ve just released our Model Hub, which provides a collection of LLM endpoints with live runtime benchmarks all plotted across time: https://unify.ai/hub A key finding is that static tabular runtime benchmarks for LLMs simply do not work. It’s necessary to take a time-series perspective, and plot the variations through time. We currently have 21 models provided by: Anyscale, Perplexity AI, Replicate, Together AI, OctoAI, Mistral AI and OpenAI, with more on the roadmap. We test across different regions (Asia, US, Europe), with varied…
Unify provides a Model Hub that tracks live performance metrics for 21 large language models from providers including OpenAI, Mistral AI, and Together AI. Rather than static benchmarks, it displays runtime performance… 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. Unify – Dynamic LLM Benchmarks and SSO for Multi-Vendor Deployment launched in 2024; newer entries below may have overtaken it.
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LLM Stats▲293I 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 →
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Find the local LLM that actually runs and performs best on your hardware. Ranked by real, recency-aware benchmarks, not parameter count. One command, run it instantly. - Andyyyy64/whichllm
May 2026 · github.com · its alternatives →
- 3AT
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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Hey Folks! I've been building an open source benchmark for measuring local LLM performance on your own hardware. The benchmarking tool is a CLI written on top of Llamafile to allow for portability across different hardware setups and operating systems. The website is a database of results from the benchmark, allowing you to explore the performance of different models and hardware configurations. Please give it a try! Any feedback and contribution is much appreciated. I'd love for this to serve as a helpful resource for the local AI community. For more check out: - Website:…
2025 · localscore.ai · 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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Hey HN, We've been building a multi-model API for LLMs called OpenRouter since we launched the Window AI extension in April this year [0]. The events of the last week make it clear the LLM landscape is unpredictable, so LLM aggregators are picking up interest. Unlike others, we're making a router on top of a public, explorable dataset. We recently built a way to rank and visualize this data, including the token counts we see going to and from different models, both open-sourced (like Llama, Mistral, finetunes, and variants) and closed-sourced (OpenAI, Anthropic). The API supports - 50+…
Apr 2026 · openrouter.ai · its alternatives →
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OpenTelemetry-native observability for agents and LLMs
Aug 2026 · openobserve.ai · 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 →
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Hey HN, I am the founder of Tensorlake. Prototyping LLM applications have become a lot easier, building decision making LLM applications that work on constantly updating data is still very challenging in production settings. The systems engineering problems that we have seen people face are - 1. Reliably process ingested content in real time if the application is sensitive to freshness of information. 2. Being able to bring in any kind of model, and run different parts of the pipeline on GPUs and CPUs. 3. Fault Tolerance to ingestion spike, compute infrastructure failure. 4. Scaling compute,…
2024 · getindexify.ai · 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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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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I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…
2025 · github.com · its alternatives →
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Deepmark AI▲97LLM benchmarking tool for task-specific metrics on your data
2023 · github.com · its alternatives →
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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 →
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An open source Next app template to monitor your AI apps
2025 · github.com · its alternatives →
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Performance comparison of ClickHouse vs PostgreSQL using LLM-style query patterns on realistic aircraft tracking data - GitHub - 514-labs/LLM-query-test: Performance comparison of ClickHouse vs PostgreSQL using LLM-style query patterns on realistic aircraft tracking data
2025 · github.com · its alternatives →
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A fast.com style benchmarking tool for LLM APIs. Compare OpenAI, Anthropic, Groq, Cerebras, Fireworks AI, Mistral, OpenRouter, Google Gemini, x.ai, z.ai, Kimi, and local models. See live streaming output with real-time throughput metrics. Race Mode lets you run multiple providers in parallel and wat
Jun 2026 · iamspeed.dev · its alternatives →
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Reproducible benchmarks for evaluating AI models
Aug 2026 · github.com · its alternatives →
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I built BenchFlow, an open-source framework that lets you integrate and evaluate AI tasks using Docker-based benchmarks. You can try it out right now by cloning the repo and running a benchmark in minutes. As an AI researcher, I was frustrated with how much time my team spent setting up benchmark environments rather than actually improving our models. We'd spend weeks configuring environments, only to find inconsistencies when comparing results with other teams. BenchFlow started as an internal tool to standardize our evaluation process, and we decided to open-source it after seeing how much…
2025 · github.com · its alternatives →
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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 →
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Hey HN, We’re excited to share PySpur, an open-source tool that provides a graph-based interface for building, debugging, and evaluating LLM workflows. Why we built this: Before this, we built several LLM-powered applications that collectively served thousands of users. The biggest challenge we faced was ensuring reliability: making sure the workflows were robust enough to handle edge cases and deliver consistent results. In practice, achieving this reliability meant repeatedly: 1. Breaking down complex goals into simpler steps: Composing prompts, tool calls, parsing steps, and branching…
2024 · github.com · its alternatives →
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Demo Video: https://www.youtube.com/watch?v=XpyVKUyt7k8 Hey all! A friend and I have been building projects with open-source LLMs for a while now (originally for other project ideas) and found that quickly iterating with different fine-tuning datasets is super hard. Training a model, setting up some inference code to try out the model and then going back and forth took 90% of our time. That’s why we built Haven, a service to quickly try out different fine-tuning datasets and base-models. Going from uploading a dataset to chatting with the resulting model now takes less than 5…
2023 · haven.run · its alternatives →
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Test and compare results from Large Language Models (LLMs) simultaneously, including Anthropic's Claude 1.2, OpenAI's GPT-4, GPT-3.5 turbo, and Cohere's CommandXL Nightly. Analyze the results side-by-side in a single UI on top of LLM APIs.
2023 · retool.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 →