AI · alternatives · 2026
24 alternatives to Qdrant Cloud Inference
Unify embeddings and vector search across modalities
Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.
- 1
Fast multimodal-native inference at scale
Dec 2025 · gmicloud.ai · its alternatives →
- 2
Deploy fast, unmetered embedding inference in your own VPC
2024 · its alternatives →
- 3
We connect visual data to AI applications.
2024 · quasara.io · its alternatives →
- 4
Deploying vision models is time consuming and tedious. Setting up dependencies. Fixing conflicts. Configuring TRT acceleration. Flashing (and re-flashing) NVIDIA Jetsons. A streamlined, developer-friendly solution for inference is needed. We, the Roboflow team, have been hard at work open sourcing Inference, an open source vision deployment solution. Our solution is designed with developers in mind, offering a HTTP-based interface. Run models on your hardware without having to write architecture-specific inference code. Here's a demo showing how to go from a model to GPU inference on a video…
2023 · github.com · its alternatives →
- 5
Qwen3.5▲288The 397B native multimodal agent with 17B active params
Feb 2026 · qwen.ai · its alternatives →
- 6
Neural network inference the Unix way. Contribute to cloudkj/layer development by creating an account on GitHub.
2019 · github.com · its alternatives →
- 7
- 8IB
We wanted to do something very challenging to prove to ourselves that we can do anything we put our mind to. The reasoning for why we chose to build a toy TPU specifically is fairly simple: - Building a chip for ML workloads seemed cool - There was no well-documented open source repo for an ML accelerator that performed both inference and training None of us have real professional experience in hardware design, which, in a way, made the TPU even more appealing since we weren't able to estimate exactly how difficult it would be. As we worked on the initial stages of this project, we…
2025 · tinytpu.com · its alternatives →
- 9
Hey HN! I built a proof-of-concept for AI memory using Git instead of vector databases. The insight: Git already solved versioned document management. Why are we building complex vector stores when we could just use markdown files with Git's built-in diff/blame/history? How it works: Memories stored as markdown files in a Git repo Each conversation = one commit git diff shows how understanding evolves over time BM25 for search (no embeddings needed) LLMs generate search queries from conversation context Example: Ask "how has my project evolved?" and it uses git diff to show actual…
2025 · github.com · its alternatives →
- 10
We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.
2025 · github.com · its alternatives →
- 11
A Linux desktop in the cloud built for Machine Learning
2016 · its alternatives →
- 12
🦉⚡️Serverless, distributed vector database as an API - TimeSurgeLabs/athenadb
2024 · github.com · its alternatives →
- 13
An educational Rust project for exporting and running inference on Qwen3 LLM family - reinterpretcat/qwen3-rs
2025 · github.com · its alternatives →
- 14
Qwen3.6-35B-A3B▲124The open sparse MoE model for agentic coding
Apr 2026 · qwen.ai · its alternatives →
- 15
Most inference UIs that I've come across pretty much just give us a chat-like interface to toy around with models in a single visual conversation thread. Given the fact that we are limited to seeing only one output at a time, it's kind of hard to compare outputs from different models, adjustments made to the prompting, and sampler settings. But even when keeping the generation parameters the same (e.g., to test for reliability in the output) and just going for multiple passes, there is no easy way to have a side-by-side comparison to keep track of the outputs from the multiple "rounds". I…
2024 · github.com · its alternatives →
- 16
Since managing the Large Language Models in production might be challenging, we've made a short demo to present how to use Cohere co.embed API and Qdrant Cloud to create a semantic QA system. This is based on bi-encoder architecture, and can be easily adopted to a different use case, like semantic search in any domain.
2022 · qdrant.tech · its alternatives →
- 17AC
For the past couple of months, I’ve been building a tool that enables natural language search over large codebases using Tree-Sitter for syntax parsing and Qdrant for vector-based retrieval. https://app.repogram.com ### How It Works - Tree-Sitter is used to parse syntax trees and extract high-quality vector embeddings of code. - These embeddings are stored in Qdrant, enabling fast similarity search across your entire repo. - A combination of re-ranking processes refine search results, producing highly relevant answers to code-related questions. The results have been incredibly…
2025 · app.repogram.com · its alternatives →
- 18
Tom from Tensil here - happy to answer questions! We developed Tensil to bring custom ML accelerators to people who don't have the resources of companies like Google, Facebook and Tesla. Currently, we're focused on supporting convolutional neural network inference on edge FPGA (field programmable gate array) platforms, but we aim to support all model architectures on a wide variety of fabrics for both training and inference. Tensil is different from other ML accelerators in that it is open source and really easy to use. For example, you can generate a custom accelerator with one command: $…
2022 · tensil.ai · its alternatives →
- 19
We’ve recently open-sourced Model2vec, a method to distill sentence transformers into static embeddings that outperform all previous approaches by a large margin on MTEB. Our new models set a new state-of-the-art for static embeddings. Main features: - Our best model (potion-base-8M) has only 8M parameters, which is ~30mb on disk - Inference is ~500x faster than the distilled base model (bge-base), on a CPU - New models can be distilled in 30 seconds on a CPU without requiring a dataset - just a vocabulary - Numpy-only inference: The packaged can be install the package with minimal…
2024 · github.com · its alternatives →
- 20Q3
Ubicloud is an open source alternative to AWS. Today, we launched our inference APIs, built with open source AI models. QwQ-32B-Preview is one of those models; and it can provide o1-like reasoning at 1% the cost. QwQ is licensed under Apache 2.0 [1] and Ubicloud under AGPL v3. We deploy open models on a cloud stack that can run anywhere. This allows us to offer great price / performance. From an accuracy standpoint, QwQ does well in math and coding domains. For example, in the MMLU-Pro Computer Science LLM Benchmark, the accuracy rankings are as follows. Claude-3.5 Sonnet (82.5),…
2025 · its alternatives →
- 21
An AI tool to help users query data and files with text.
2024 · qquest.io · its alternatives →
- 22NT
2022 · chatgpt-viz.vercel.app · its alternatives →
- 23
A simple re-implementation of Google's sparsehash as a learning excercise. - qpfiffer/Simple-Sparsehash
2015 · github.com · its alternatives →
- 24KC
If you’ve ever been curious about how GPT actually works under the hood, I built a small project you might find interesting. I implemented a GPT-style transformer from scratch in a single notebook—covering tokenization, embeddings, causal self-attention, training, and autoregressive text generation without relying on high-level abstractions. The focus was on mechanistic clarity rather than scale or performance, and the notebook is structured to read more like a technical walkthrough than an experiment log. Feedback from people who’ve built or studied transformers would be very welcome.
May 2026 · pythongiant.github.io · 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 →