DocArray vs Vellum
Compare coding AI Tools
Open source Python library for representing and moving multimodal documents and embeddings across services for search, RAG and generative apps.
Vellum is an AI agent building platform that combines a prompt playground, evaluation tools, and hosted agent apps so teams can iterate on LLM workflows with debugging and knowledge base support, starting with a free tier and upgrading for more credits.
Feature Tags Comparison
Key Features
- Typed Document and DocumentArray classes for multimodal data
- Fast binary serialization for inter process and network transport
- Field validation and schema versions for reproducibility
- Helpers for chunking splitting and hierarchical docs
- Vector friendly ops for indexing similarity and ranking
- Integrations with PyTorch TensorFlow and ONNX runtimes
- Free and Pro plans: Pricing starts at $0 with 50 credits and Pro at $25 with 200 builder credits so solo builders can scale testing
- Prompt playground: Compare models side by side and iterate prompts systematically instead of relying on subjective testing
- Evaluations framework: Run repeatable quality tests at scale to detect regressions and track improvements across prompt versions
- Hosted agent apps: Share working agents with teammates through hosted apps for demos
- reviews
- and stakeholder feedback cycles
Use Cases
- RAG pipelines passing chunks and embeddings between steps
- Multimodal search services combining text and images
- ETL jobs moving vectors between stores during migrations
- Evaluation harnesses that track inputs outputs and scores
- Realtime inference systems that batch requests across workers
- Dataset curation with typed metadata for training
- Agent prototyping: Build an agent by chatting with AI then refine logic with low code steps and controlled prompt versions
- Prompt iteration: Compare LLM outputs side by side and select prompts that improve accuracy and reduce unwanted variation
- Regression testing: Run evaluations on a saved dataset before release to catch quality drops after model or prompt changes
- RAG apps: Attach a knowledge base and test retrieval behavior with representative questions and strict document scope rules
- Stakeholder demos: Publish hosted agent apps so product and compliance reviewers can test behavior without local setup steps
- Model selection: Evaluate providers and self hosted options with the same tasks to choose the best cost and latency mix for production
Perfect For
Python developers, ML engineers and researchers who need structured multimodal containers and fast, predictable transport across models, vector stores and services
product managers, ML engineers, software engineers, data scientists, AI platform teams, prompt engineers, QA and reliability teams, startups building LLM features, teams shipping agent workflows
Capabilities
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