Looking for something like LLM App? Here are 24 alternatives, ranked by how closely they match it on purpose, features and platforms. Of these, 22 are open source.
Pathway offers cloud templates for RAG, AI pipelines, and enterprise search that stay synchronized with connected data sources.
| # | Alternative | Pricing | Open source | Platforms |
|---|---|---|---|---|
| 1 | currentslab/awesome-vector-search | — | Yes | — |
| 2 | GPTCache | — | Yes | — |
| 3 | txtai | — | Yes | Web |
| 4 | Zilliz Cloud | — | Not stated | Web |
| 5 | fenn | — | Yes | — |
| 6 | Kale | — | Yes | — |
| 7 | ClearML | — | Yes | — |
| 8 | Kedro-Viz | — | Yes | — |
| 9 | ClearML | — | Yes | — |
| 10 | VectorChord | — | Not stated | — |
Curated list of awesome vector search framework/engine, library, cloud service and research papers to vector similarity search.
GPTCache provides semantic caching for large language models and integrates with LangChain and LlamaIndex.
txtai combines semantic search, language-model orchestration and AI workflows in an open-source framework.
Zilliz Cloud provides a managed vector data platform powered by Milvus, with real-time search and data operations across major cloud regions.
The fenn Python framework supports machine learning workflows and LLM agents, with trainers, templates, logging, and configuration management.
Kale simplifies data science workflows for deploying Kubeflow Pipelines.
ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.
Kedro-Viz visualizes Kedro data and machine learning pipelines and tracks experiments.
ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.
Scalable, fast, and disk-friendly vector search in Postgres, the successor of pgvecto.rs.
Kubeflow Pipelines orchestrates and manages machine learning workflows on Kubeflow.
TPOT uses genetic programming to optimize machine learning pipelines in Python.
MLRun provides an open-source MLOps platform for managing machine learning applications, pipelines, and CI/CD delivery.
Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.
ZenML provides a platform for machine learning pipelines and AI agents.
BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.
Xinference runs language, speech, and multimodal models through a unified inference API across cloud, on premises, or laptop setups.
dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.
Featureform turns existing data infrastructure into a virtual feature store.
Apache Mahout develops Qumat, a backend agnostic quantum circuit library, and QDP, a GPU accelerated quantum data encoding engine.
Catniff provides JavaScript developers with a Torch-like deep learning framework.
Sagify describes its project as a way to work with large language models and machine learning more easily.
ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.
Merlin supports machine learning model management, deployment, and serving in Kubernetes environments.
Alternatives are suggested from the purpose and features described in our listings. Different pages for the same product appear once. Read each description to check whether it fits your task. How we choose alternatives · Suggest an alternative
