The Best VDP Alternatives in 2026

ModelFox supports training, deployment, and monitoring for machine learning models.

TensorRT-LLM provides Python and C++ runtimes with optimizations for language model inference on NVIDIA GPUs.

File Parser optimised for LLM Ingestion with no loss. Parse PDFs, Docx, PPTx in a format that is ideal for LLMs.

MiroShark describes a swarm intelligence engine for simulating scenarios with stated time and cost targets.

dstack coordinates AI compute and supports training and inference across GPU clouds, Kubernetes, VMs, and bare metal.

ClearML combines experiment and data management, pipelines, scheduling, orchestration, and model serving for AI workloads.

ClearML manages AI experiments, data, pipelines, orchestration, scheduling, and model serving in one MLOps platform.

Kedro helps create reproducible, maintainable, and modular data science and data engineering pipelines.

The colibri-core NLP tool and library works with n-grams, skipgrams, and other linguistic patterns.

Mosec serves machine learning models with dynamic batching and CPU or GPU pipelines for inference workloads.

BentoML helps build and serve AI apps and models through inference APIs, job queues, and multi-model pipelines.

Marquez collects, aggregates, and visualizes metadata about a data ecosystem.

Neurolink connects AI model providers to applications and includes generation, streaming, voice, RAG, and file processing features.

Mirascope describes itself as an alternative approach to building applications with large language models.

onWatch tracks AI API quotas across providers and displays usage in real time through a lightweight background monitor.

Entroly reduces AI context size with reversible compression and receipts for recovering the original content.

CML provides continuous machine learning and CI/CD workflows for machine learning projects.

Laminar provides open-source observability tools designed for applications built with AI agents.

ORMB packages machine learning models as OCI artifacts, in a workflow described as Docker for ML models.

Cog packages machine learning models into containers to support deployment across environments.

DJL is a Java framework for running deep learning models across different engines.

Machine learning operations and large language model operations are the focus of Starwhale’s platform.

ONNX-MLIR compiles ONNX models using MLIR compiler infrastructure for model deployment workflows.

Manifest connects AI agents and tools to multiple model providers through an LLM gateway.