Unprompt vs Vespa: Features, Pricing & Which Is Better (2026)
A side-by-side comparison of Unprompt and Vespa — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.
Unprompt
Polymorph
iPhone and Mac app that measures your AI usage in tokens, API-rate cost and water, without judging it.
Key features
- Automatic Measurement: Mac reads local AI tool logs and iPhone uses Screen Time, so there is nothing to count manually.
- Evening Check-In: A one-tap daily confirmation of personal versus work AI use, with adjustments allowed.
- Pixel Calendar: Each day becomes a pixel colored against your own average, with streaks and a full-year band.
- Usage and Cost (Premium): Tokens, the API-rate cost and estimated water use over today, 30 days and full history.
- Codex Quotas and Widgets (Premium): Quota countdowns plus USAGE and LIMITS widgets.
- Milestones and Friends: 62 milestones and friend trends via QR code or link with no account needed.
- Private by Design: Data stays on device and in your iCloud, with no conversation read and no ads or tracking.
Best for
- Self-Awareness: See how much you rely on AI day to day without being blocked or lectured.
- Cost Tracking: Estimate what your token use would cost at API rates.
- Environmental Awareness: View an estimate of the water behind your AI usage.
- Work-Life Separation: Separate personal AI use from work-hours use.
- Quota Planning: Watch Codex limits and reset countdowns to avoid wasting capacity.
- Group Accountability: Compare trends with friends without sharing exact numbers.
Vespa
Vespa.ai
Open-source big data serving engine for low-latency structured, text and vector search, ranking and real-time decisioning at scale.
Key features
- Low-Latency Serving: Distributed architecture that executes computations, ranking and retrieval at query time to deliver sub-second responses over very large datasets.
- Unified Data Types: Native support for structured fields, full-text search and dense vector representations, enabling hybrid search (text+vector) and combined relevance signals.
- Advanced Ranking & Relevance: Built-in ranking framework allowing custom ranking expressions, feature feeding, and real-time model scoring to produce highly relevant results and recommendations.
- Real-Time Personalization & Decisioning: Ability to serve personalized recommendations and targeting by computing signals at user-serving time with low latency.
- Managed Service & Self-Hosting Options: Core engine is Apache 2.0 open-source for self-hosting, plus a serverless managed offering (Vespa Cloud) for production deployment and operations.
- Developer Tooling & SDKs: Ecosystem tooling (pyvespa, Java APIs, CLI) for faster prototyping, deployment, feeding data, and integrating embeddings and RAG workflows.
- Streaming & Cost-Efficient Retrieval Modes: Supports streaming retrieval patterns and optimizations for cost-efficient use with external embedding providers and RAG pipelines.
- Extensible Sample Apps & Documentation: Rich examples and sample-apps (including end-to-end RAG examples) and active documentation to accelerate real-world integration.
- Store and serve large structured, text and vector datasets for online queries
- Low-latency computation and ranking at user-serving time
- Support for structured search, full-text search and dense-vector retrieval/ranking
- Real-time recommendation, personalization and targeting pipelines
- PyVespa: official Python API for creating, modifying, deploying and interacting with Vespa instances
- Vespa CLI wrapper available (included in pyvespa repo) for operational workflows
- Sample apps and documentation for RAG, dense vector ranking and embedding use cases
- Can be self-hosted (downloadable) or used as a serverless managed service at cloud.vespa.ai
- Open-source license (Apache 2.0) enabling community contributions and extensibility
Best for
- Hybrid Search: Implement production-grade search that combines text and vector embeddings to retrieve and rank results for e‑commerce, knowledge bases, or enterprise search.
- Retrieval-Augmented Generation (RAG): Host retrieval pipelines and vectors used to fetch relevant context for LLMs, including streaming retrieval and cost-efficient embedding use.
- Real-Time Recommendation & Personalization: Serve personalized recommendation lists and targeted content by computing user features and ranking in real time at request time.
- Large-Scale Ranking & Targeting: Perform at-scale ranking over millions to billions of items for ad-serving, content ranking, or personalized feeds with low-latency constraints.
- Operational ML Serving: Score models and combine online features with stored data at query time to make instant, data-driven decisions in production systems.
- Analytics-Driven Search Tuning: Iterate relevance tuning and ranking experiments using Vespa's ranking expressions and feature pipelines to improve search quality.
- Low-latency product or content search combining structured filters, text and vector similarity
- Real-time recommendation and personalization at scale
- Dense vector ranking for semantic search and retrieval
- Retrieval-augmented generation (RAG) workflows where retrieval and scoring run in the serving layer
- Building cost-efficient personal assistants by integrating streaming retrieval with Vespa