Decision comparison
Marqo vs Weaviate
Marqo and Weaviate meet only when a team needs product search, and then the choice is build or buy. Weaviate is an open-source vector database: a team that builds store search on it chooses the embedding models, writes the ranking and merchandising logic, and runs the database or pays for Weaviate's managed cloud. Marqo is the buy option: a hosted AI ecommerce search and product discovery platform that learns ranking from the store's catalog and shopper behavior. For anything outside commerce, such as RAG or agentic applications over documents, Weaviate is the only fit.
Architecture choice. These take different approaches to the same problem. Read the table as a fit question rather than a feature race.
These are different kinds of product — Search Engine and Vector Database.
Quick Comparison
| Decision factor | Marqo | Weaviate |
|---|---|---|
| Best For | Online retailers that want to buy hosted product search and discovery instead of building it on a vector database | AI engineers building search, RAG, and agentic applications at scale |
| Pricing Model | Marqo's open-source engine, under the Apache-2.0 license, is deprecated and no longer receives updates. Marqo's current product is an AI ecommerce search and product discovery platform. Marqo publishes no prices on its website; its homepage offers a demo and a talk with a search expert. Verified 2026-10-26 against https://www.marqo.ai/ and https://github.com/marqo-ai/marqo. | Open source for self-hosting; Weaviate Cloud Free is always $0/month. Flex starts at $45/month (monthly pay-as-you-go); Premium starts at $400/month (prepaid). Flex and Premium minimums include the baseline cluster, vector dimensions, and storage; backups are additional. Weaviate Cloud Flex starts at $45 per month, with rates from $0.00465 per 1M vector dimensions, $0.12 per GiB of storage and $0.03036 per GiB of backup storage. Premium (Shared and Dedicated) is priced through sales; Premium Shared rates start from $0.003875 per 1M vector dimensions, $0.10 per GiB of storage and $0.021 per GiB of backups; vector-dimension list prices vary by cloud provider and region. Verified 2026-10-05 against https://weaviate.io/pricing. |
| Search Approach | A dedicated model trained on the store's catalog and on shopper clicks, carts and purchases ranks results, with semantic relevance, typo tolerance and multilingual comprehension | Hybrid search combining vector and BM25 keyword search with advanced filtering |
| Deployment Options | Hosted by Marqo; connected via API or one-click integrations for Shopify, Adobe Commerce and Salesforce Commerce Cloud | Open-source self-hosted, managed cloud, or Kubernetes in your VPC |
| Embedding Generation | Not a step the team runs: Marqo trains a dedicated model for each store (MarqTune) and states it uses no synthetic data and no shared LLM | Vectorizer modules with 20+ ML model integrations or bring your own embeddings |
| Enterprise Readiness | Commerce-focused, with brand-specific models and AI-automated merchandising; its homepage lists no compliance certifications or SLA, so ask for them in the demo | SOC 2, HIPAA compliance, RBAC, native multi-tenancy, 99.95% SLA on Premium |
Marqo
- Best For:
- Online retailers that want to buy hosted product search and discovery instead of building it on a vector database
- Pricing Model:
- Marqo's open-source engine, under the Apache-2.0 license, is deprecated and no longer receives updates. Marqo's current product is an AI ecommerce search and product discovery platform. Marqo publishes no prices on its website; its homepage offers a demo and a talk with a search expert. Verified 2026-10-26 against https://www.marqo.ai/ and https://github.com/marqo-ai/marqo.
- Search Approach:
- A dedicated model trained on the store's catalog and on shopper clicks, carts and purchases ranks results, with semantic relevance, typo tolerance and multilingual comprehension
- Deployment Options:
- Hosted by Marqo; connected via API or one-click integrations for Shopify, Adobe Commerce and Salesforce Commerce Cloud
- Embedding Generation:
- Not a step the team runs: Marqo trains a dedicated model for each store (MarqTune) and states it uses no synthetic data and no shared LLM
- Enterprise Readiness:
- Commerce-focused, with brand-specific models and AI-automated merchandising; its homepage lists no compliance certifications or SLA, so ask for them in the demo
Weaviate
- Best For:
- AI engineers building search, RAG, and agentic applications at scale
- Pricing Model:
- Open source for self-hosting; Weaviate Cloud Free is always $0/month. Flex starts at $45/month (monthly pay-as-you-go); Premium starts at $400/month (prepaid). Flex and Premium minimums include the baseline cluster, vector dimensions, and storage; backups are additional. Weaviate Cloud Flex starts at $45 per month, with rates from $0.00465 per 1M vector dimensions, $0.12 per GiB of storage and $0.03036 per GiB of backup storage. Premium (Shared and Dedicated) is priced through sales; Premium Shared rates start from $0.003875 per 1M vector dimensions, $0.10 per GiB of storage and $0.021 per GiB of backups; vector-dimension list prices vary by cloud provider and region. Verified 2026-10-05 against https://weaviate.io/pricing.
- Search Approach:
- Hybrid search combining vector and BM25 keyword search with advanced filtering
- Deployment Options:
- Open-source self-hosted, managed cloud, or Kubernetes in your VPC
- Embedding Generation:
- Vectorizer modules with 20+ ML model integrations or bring your own embeddings
- Enterprise Readiness:
- SOC 2, HIPAA compliance, RBAC, native multi-tenancy, 99.95% SLA on Premium
Public signals
Verified factual signals only. Bars appear only for like-for-like metrics with five weekly assessments for every tool; missing evidence stays explicit. These signals do not establish enterprise adoption, product quality, or total cost.
| Metric | Marqo | Weaviate |
|---|---|---|
| Docker Hub pulls(Product adoption) | 159.5k | 34.8M |
| GitHub commits, 90d(Developer adoption) | 0 | Not available |
| GitHub stars(Developer adoption) | 5,000+ | Not available |
| Search interest(Market interest) | Unavailable | 1 |
| Hugging Face downloads(Product adoption) | 52.7k | 161 |
| Hugging Face likes(Product adoption) | 130 | 13 |
| Product Hunt comments(Community interest) | 8 | 4 |
| Product Hunt rating(Community interest) | Unavailable | 4.9/5 |
| Product Hunt reviews(Community interest) | 0 | 13 |
| Product Hunt votes(Community interest) | 141 | 12 |
| PyPI weekly downloads(Developer adoption) | 13.1k | 1.1M |
| Stack Overflow questions(Community interest) | 11 | 160 |
| GitHub commits, 90d(Product adoption) | Not available | 3.6k |
| GitHub stars(Product adoption) | Not available | 16,000+ |
| Hacker News mentions, 90d(Community interest) | Not available | 5 |
| npm weekly downloads(Developer adoption) | Not available | 458.7k |
As of November 9, 2026 — updated weekly.
Health & risk evidence
Observed public-source checks for mapped package versions and repositories.
Marqo
November 9, 2026Package vulnerabilities
PyPI · marqo@3.18.2
0 vulnerabilities
across 1 package
Repository security score
Not available
Weaviate
November 9, 2026Package vulnerabilities
npm · weaviate-client@3.15.3 · PyPI · weaviate-client@4.24.1
0 vulnerabilities
across 2 packages
Repository security score
Not available
Interface Preview
Marqo
Weaviate
Feature Comparison
| Feature | Marqo | Weaviate |
|---|---|---|
| Search Capabilities | ||
| Hybrid Search | Semantic search with instant indexing, typo tolerance, and multilingual support | Built-in hybrid search merging vector and BM25 keyword algorithms with re-ranking |
| Multimodal Search | LLM-based image search integrated directly into the search model | Supported through vectorizer modules and model integrations |
| Advanced Filtering | AI-driven automated ranking, boosts, filters, and collection generation | Complex filters across large datasets in milliseconds with flexible operators |
| AI & ML Integration | ||
| Embedding Generation | No embedding pipeline to run: Marqo trains a dedicated model on the store's catalog and shopper behavior (MarqTune) | Vectorizer modules supporting 20+ ML models and frameworks with built-in embedding service |
| RAG Support | Not a stated use case; Marqo covers store search, browse, recommendations and an agentic storefront | Out-of-the-box RAG for securely interacting with ML models using proprietary data |
| Agentic AI | Agentic product discovery with adaptive journeys and conversational search | Database agents and agentic AI workflows for knowledge-based applications |
| Scalability & Architecture | ||
| Multi-Tenancy | Not a buyer concern: Marqo hosts the platform and trains a dedicated model for each store | Native multi-tenancy with horizontal scaling and tenant isolation for resource efficiency |
| Vector Compression | Not a buyer concern: Marqo runs the index and the model, so the store does not tune vector storage | Vector index compression reducing memory footprint with HNSW graph index and rotational quantization |
| Backup & Recovery | Handled by Marqo as the hosting vendor; its homepage lists no backup or SLA terms, so ask in the demo | Configurable backups with zero downtime, 7-day retention on Flex, 45-day on Premium |
| Deployment & Integration | ||
| Self-Hosted Option | None maintained: the open-source engine (Apache-2.0) is deprecated and no longer receives updates | Full open-source database deployable via Docker, Kubernetes, or bare metal at no cost |
| Cloud Deployment | Hosted ecommerce search platform, deployed via API or one-click commerce integrations | Managed cloud on GCP with shared or dedicated deployment options and BYOC on Premium |
| Platform Integrations | One-click integrations for Shopify, Adobe Commerce, and Salesforce Commerce Cloud | SDKs for Python, Go, TypeScript, JavaScript plus GraphQL and REST APIs |
| E-commerce & Business | ||
| Conversion Optimization | Optimizes using click-stream, purchase, and event data with reported +17.7% conversion uplift | General-purpose vector search without built-in e-commerce conversion features |
| Personalization | Brand-specific model trained on the store's catalog and shopper behavior (MarqTune) | Personalization achievable through custom vectorizer modules and application-level logic |
| Recommendations | Recommendations served by the same store-trained model as search and browse | Recommendation systems buildable using vector similarity search and filtering |
Search Capabilities
Hybrid Search
Multimodal Search
Advanced Filtering
AI & ML Integration
Embedding Generation
RAG Support
Agentic AI
Scalability & Architecture
Multi-Tenancy
Vector Compression
Backup & Recovery
Deployment & Integration
Self-Hosted Option
Cloud Deployment
Platform Integrations
E-commerce & Business
Conversion Optimization
Personalization
Recommendations
Which approach fits
Marqo and Weaviate meet only when a team needs product search, and then the choice is build or buy. Weaviate is an open-source vector database: a team that builds store search on it chooses the embedding models, writes the ranking and merchandising logic, and runs the database or pays for Weaviate's managed cloud. Marqo is the buy option: a hosted AI ecommerce search and product discovery platform that learns ranking from the store's catalog and shopper behavior. For anything outside commerce, such as RAG or agentic applications over documents, Weaviate is the only fit.
When each approach fits
Choose Marqo if:
Choose Marqo if you run an online store and want to buy product search rather than build it on a vector database. A one-line pixel records clicks, carts and purchases, and Marqo trains a dedicated model on your catalog and that behavior, then serves Search, Browse, Recommendations and an Agentic Storefront from the same model. AI automates ranking, boosts, filters and collections, and one-click integrations for Shopify, Adobe Commerce and Salesforce Commerce Cloud shorten the rollout. Marqo loses to Weaviate when you need search beyond products, want to choose and own your models, or need published prices and compliance documents up front: it lists no prices and routes buyers to a demo.
Choose Weaviate if:
Choose Weaviate if you are building general-purpose AI applications such as semantic search, retrieval-augmented generation, or agentic workflows. Weaviate is the stronger choice when you need transparent pricing starting at $45/mo for Flex, a full open-source self-hosted option, native multi-tenancy at billion-scale, and enterprise compliance features like SOC 2, HIPAA, and RBAC across multiple deployment models. For store search, Weaviate is the build path: your team owns the embeddings, the ranking and the shopper-behavior loop that Marqo sells as a hosted service.
These scenarios reflect the available product evidence. Your requirements, existing stack, and team expertise should guide the final decision.
Frequently Asked Questions
What is the main difference between Marqo and Weaviate?
Weaviate is a general-purpose open-source vector database for search, RAG and agentic AI applications, where the team brings or configures the embedding models and builds the application on top. Marqo is a hosted AI ecommerce search and product discovery platform: a one-line pixel records clicks, carts and purchases, and Marqo trains a dedicated model on the store's catalog and that behavior to rank results. Marqo's earlier open-source engine is deprecated, so the two no longer compete as databases. They compete only when a store decides whether to build product search on Weaviate or buy it from Marqo.
How does pricing compare between Marqo and Weaviate?
Weaviate offers transparent tiered pricing starting with an always-free managed plan, Flex at $45/mo minimum with pay-as-you-go billing, and Premium at $400/mo for enterprise deployments. Weaviate also provides a free open-source self-hosted option. Marqo publishes no prices; its website routes buyers to a demo or a talk with a search expert. The fair comparison is not Marqo's quote against Weaviate's plan. It is Marqo's quote against Weaviate's bill plus the engineering time to build embeddings, event tracking, ranking and merchandising rules for product search.
Which handles image and multimodal product search better?
Marqo describes LLM-based image search integrated directly into its search model, with brand-specific models for domains such as fashion, beauty and furniture, so a store gets image-aware product search without choosing a model. Weaviate supports multimodal search through its vectorizer module ecosystem, letting you connect various ML models for different data types including text and images. Weaviate gives you the choice of model and how scores combine; Marqo gives you a finished product search with the vendor's model.
Which is better for a startup building an AI application?
For a startup building a general AI application, Weaviate is the better starting point. Weaviate's free open-source version lets you self-host at zero licensing cost, the 14-day sandbox allows quick evaluation, and the Flex plan at $45/mo provides an affordable path to production. An ecommerce startup should weigh traffic before choosing Marqo: its model learns from clicks, carts and purchases, so a store with little traffic gives it little to learn from. Marqo makes sense once search drives a meaningful share of revenue and the team does not want to staff the build path.