DSPy vs sizeless: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of DSPy and sizeless — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
DSPy
Stanford University
A Python framework for programming foundation models with declarative, self-improving pipelines and automated prompt/parameter optimization.
Key features
- Declarative Module System: Define compositional Python modules with explicit inputs and outputs; DSPy compiles these declarations into prompt templates and executable model calls.
- Iterative Optimizers: Built-in optimizers (e.g., BootstrapFewShot, BetterTogether) automatically generate prompt/parameter variants, test them on examples, and retain the best-performing versions to improve accuracy and consistency over time.
- Evaluation API: Flexible evaluation framework with built-in metrics and support for custom metrics and datasets, enabling systematic measurement and comparison of module performance during development and optimization.
- RAG and Agent Support: First-class support for building Retrieval-Augmented Generation pipelines and agent loops, enabling complex multi-step workflows and tool-augmented agents.
- Modular Pipeline Composition: Easily compose classifiers, retrievers, and generators into end-to-end pipelines for rapid iteration and reuse of components across projects.
- Multi-backend Integration: Designed to work with external LLM APIs, retrieval systems, and tool integrations (examples and community repos demonstrate connectors to common model APIs and data sources).
- Installation and Packaging: Distributed via pip and conda-forge (pip install dspy / conda install dspy), with source and examples available on GitHub for easy adoption and extension.
- Self-improvement Workflows: Supports data-driven optimization loops where DSPy uses held-out examples and metrics to automatically refine prompts, weights, and configurations without manual prompt engineering.
- Declarative API to define inputs, outputs, and modular signatures instead of string prompts
- Compilation of declarative code into prompt templates and model calls
- Optimizers that iterate on prompts/parameters using example datasets and metrics (e.g., BootstrapFewShot, BetterTogether)
- Evaluation API with built‑in metrics and support for custom metrics
- Support for building RAG pipelines, classifiers, and agent loops
- Multi-language community ports (DSPy.ts for browser/TypeScript, DSPy.rb for Ruby)
- Installation via pip (pip install dspy or pip install dspy-ai) and source install from GitHub
- Examples, demos, and community repos for production patterns and multi‑agent systems
- Model-agnostic integrations across multiple providers and access to many models (via community adapters)
Best for
- Building robust classifiers: Declare expected inputs/outputs and use DSPy's optimizers to automatically refine prompts and parameters for high-accuracy text classification tasks.
- Developing RAG chatbots: Compose retriever and generator modules into a RAG pipeline, evaluate on QA datasets, and iterate prompts to improve faithfulness and answer quality.
- Constructing agent loops: Implement multi-step agent workflows (tool use, planning, and synthesis) with modular components and optimize their prompting and decision heuristics programmatically.
- Research prototyping: Rapidly test new prompting/optimization algorithms and evaluate them using DSPy's evaluation API and example-driven optimizers.
- Automated prompt tuning: Use DSPy's iterative optimizers to generate and validate prompt variations on sample datasets, automating what would otherwise be manual prompt engineering.
- Consistency and reliability testing: Run systematic evaluations across datasets and metrics to identify failure modes and let DSPy select improved prompt/parameter variants.
- Multi-agent coordination demos: Compose and coordinate multiple agent modules for collaborative tasks (e.g., research assistance or document drafting) using DSPy examples and community projects.
- Build reliable ML-powered classifiers by declaring types and examples and optimizing prompts/parameters
- Construct Retrieval-Augmented Generation (RAG) chatbots and QA systems
- Create agentic multi‑agent systems and orchestrated agent loops
- Automate prompt/template optimization to improve accuracy and consistency without manual tuning
- Evaluate and benchmark LLM pipelines using built‑in and custom metrics
sizeless
sizeless
Turns a smartphone video of an open trench into a centimetre-accurate 3D point cloud, CAD as-built plan and GIS-ready digital twin of buried utilities.
Key features
- Smartphone capture: Field crews record an open trench with a standard iPhone Pro — no specialist scanning hardware and no separate surveying appointment
- Centimetre-accurate point clouds: Reconstruction algorithms developed at ETH Zurich build a high-resolution 3D point cloud of the excavation from the video alone
- Standards-compliant CAD output: Generates as-built plans in DWG and DXF, with couplings and pipe runs identified and measurements simplified
- 3D digital twin and GIS export: Produces a model of the pipe route including building entries that drops into existing GIS systems
- Works without GPS: Captures basement sections and building entry points where GNSS-based surveying fails
- Immediate backfilling: Because capture takes minutes, trenches close right after filming instead of waiting on a survey crew
- Documentation in about 72 hours: Complete records arrive weeks earlier than conventional surveying, enabling prompt connection billing
- Third-party utility capture: Records crossing utilities and as-laid geometry as unbroken 3D evidence, replacing hand sketches
Best for
- A utility network operator documenting residential service connections without booking a surveyor for every site
- A contractor closing a trench the same day instead of leaving it open pending a survey appointment
- Capturing a building entry point in a basement where GPS-based surveying cannot get a fix
- A district heating project producing as-built DWG plans for regulatory sign-off
- Spotting a laying error in the 3D point cloud before backfilling, while the fix is still cheap
- Feeding as-built pipe geometry into a GIS system for long-term network maintenance planning