Agentic AI Comparison:
Tilores vs Vanna AI

Tilores: 8

Tilores achieves a high degree of data management autonomy in the specific domain of identity and entity resolution. Once configured with matching rules and data sources, it automatically ingests records, performs fuzzy matching, and maintains a continuously updated entity graph, minimizing the need for ongoing manual deduplication or record reconciliation. Its graph-based entity resolution and APIs allow downstream applications (fraud engines, CRM, marketing tools, etc.) to rely on a single source of truth without embedding matching logic themselves. Tilores also supports no-code configuration and a managed cloud model, which further reduces operational overhead for customers. However, it is not a general-purpose autonomous AI agent; its autonomy is focused on identity resolution workflows rather than arbitrary tool orchestration or conversational automation, which is why it does not receive a maximum score.

Vanna AI: 7.5

Vanna AI provides strong autonomy in the domain of natural-language data access and analytics: users can ask questions in plain language and the system automatically generates SQL queries, executes them, and returns tables or charts, drastically reducing reliance on data analysts for routine questions. Its agents orchestrate tools (SQL execution, chart generation, custom functions) under the hood, and Vanna 2.0 introduces user-aware security and context propagation so that permission logic and tool execution are enforced automatically without manual wiring. The inclusion of Tool Memory and a hosted vector database enables the system to self-improve over time based on previous successful queries, enhancing autonomous behavior in query generation and refinement. However, Vanna’s autonomy is largely confined to data querying and analytics rather than broad process automation, and effective use still requires initial configuration of database connections, schemas, and LLM providers.

Tilores: 7

Tilores emphasizes a no-code identity resolution platform and a studio experience, allowing users to start with a free plan and configure identity resolution without deep engineering work. It offers clear GraphQL entry points (search, entity by ID, entityByRecord) to access resolved entities, which simplifies integration compared to building custom entity-resolution logic. Documentation and marketing material highlight low-code configuration and managed cloud deployment, reducing the need to operate infrastructure directly. However, setting up effective entity resolution still inherently requires understanding of data models, matching rules, and domain-specific thresholds, which can be complex for non-technical users. Integrating multiple data sources and designing appropriate matching strategies also demands some technical and data engineering expertise. Thus, Tilores is easier to use than building in-house entity resolution, but remains data-infrastructure oriented rather than purely plug-and-play for non-technical end users.

Vanna AI: 8.5

Vanna AI is designed to let end users query data with natural language, making it very approachable for non-SQL users. Once connected to a database and LLM provider, users can ask questions in plain English and receive SQL, tables, and charts, which significantly lowers the barrier compared to writing SQL directly. Vanna offers multiple integration options—Jupyter, Streamlit, web UI built on FastAPI, Teams bot, embeddable widgets—so developers can expose a user-friendly interface in existing tools. The presence of a hosted admin layer for observability, access control, and configuration further simplifies production use for teams that opt for managed plans. That said, initial setup (connecting databases, configuring schemas, choosing LLM providers, and tuning agents) requires developer involvement and understanding of infrastructure components. Overall, because day-to-day interaction is conversational and GUI-based for most users, Vanna scores higher on ease of use for end-user analytics compared to Tilores’ more backend/infrastructure-centric UX.

Tilores: 7.5

Tilores is flexible within the identity resolution and knowledge graph domain: it can ingest multi-source datasets, build entity-resolved knowledge graphs, and support use cases ranging from fraud detection to customer 360 and AI/ML enrichment. Its graph is exposed via GraphQL and APIs, allowing integration with many downstream tools and custom applications. Pricing and deployment options emphasize a managed cloud service with tiers based on Unified Customer Records, and there is mention of a community version and free plan for low volumes. However, Tilores is specialized: it focuses on resolving and maintaining entities rather than general-purpose data transformation, arbitrary workflow automation, or broad AI agent orchestration. Customization revolves around matching logic, data schemas, and graph queries rather than arbitrary tool chains or plug-in ecosystems. This specialization constrains general flexibility but provides depth in its niche.

Vanna AI: 9

Vanna AI’s architecture is explicitly oriented toward flexibility: it is database-agnostic and supports major relational databases such as PostgreSQL, MySQL, BigQuery, Snowflake, and SQLite, as well as additional backends via its modular storage layer. It is also LLM-agnostic, supporting multiple providers (OpenAI, Anthropic, Gemini, Ollama/local models), making it easy to switch or combine models for different cost and latency profiles. The framework is open source and designed to let developers define custom tools, add new storage backends, and integrate with diverse deployment contexts (Jupyter, Streamlit, web, APIs, enterprise bots), giving substantial flexibility in how agents are wired into existing systems. Vanna 2.0 adds enterprise features like user-aware security, row-level access control, and streaming responses, which expand its applicability from experimentation to production enterprise scenarios. This combination of open-source modularity, LLM/database agnosticism, and multiple deployment patterns yields a very high flexibility score.

Tilores: 7.5

Tilores uses a per–Unified Customer Record (UCR) pricing model, where customers pay based on the number of unified entities rather than raw records, meaning each customer counts once regardless of record duplication. It offers a free plan for up to 1,000 UCRs for both commercial and non-commercial use, with no credit card required, and tiered plans such as Starter (up to 200,000 UCRs), Growth (up to 2 million UCRs), and Enterprise for unlimited UCRs. This structure can be cost-efficient for organizations with significant duplication because the billing unit is the resolved entity rather than all source records, and it facilitates predictable scaling as customer bases grow. However, detailed pricing for higher tiers is not publicly specified and typically requires contacting sales, which introduces opacity for cost comparisons and budget planning. Additionally, Tilores is a specialized infrastructure product; compared to purely open-source stacks, total cost will include subscription fees, though these may be justified by reduced engineering and maintenance overhead.

Vanna AI: 8

Vanna AI offers a free open-source Community Edition that can be self-hosted, allowing organizations to avoid direct licensing costs if they are willing to manage infrastructure and LLM costs themselves. For managed offerings, Vanna provides several tiers: a Community/open-source tier at zero license cost, an Explorer plan priced around $40/month, a Team plan at around $400/month, and an Enterprise tier with custom pricing and advanced features like guardrails and custom knowledge graphs. Some hosted options include complimentary LLM calls, reducing initial experimentation costs. Because Vanna is LLM-agnostic and can run with local models via Ollama, organizations can also control inference costs by selecting cheaper or on-prem models. The tradeoff is that self-hosting open-source Vanna requires engineering time and infrastructure resources, and total cost will also depend on LLM usage patterns, database sizes, and monitoring/observability requirements. Nonetheless, the availability of a powerful free tier and transparent entry-level pricing yields a slightly higher cost score.

Tilores: 6.5

Tilores appears to be a specialized B2B identity resolution platform with a focus on fraud prevention, customer-data unification, and knowledge graphs, marketed primarily through its own site and solution pages. Available information emphasizes its feature set and pricing tiers but does not highlight broad open-source adoption or large community ecosystems, suggesting it is more of a niche, enterprise-focused product than a widely adopted developer tool. External reviews and listings describe Tilores as one option among other identity resolution and customer data tools, indicating some market presence but not mass popularity in general developer or AI communities. Given the limited signals on community size, GitHub stars, or widespread grassroots usage relative to widely known open-source AI frameworks, Tilores likely has a moderate but focused adoption primarily among organizations with specific identity resolution needs.

Vanna AI: 8.5

Vanna AI has significant visibility as an open-source text-to-SQL/AI SQL agent: it has been featured on platforms like Hacker News as an open-sourced text-to-SQL project, with emphasis on flexibility, local or hosted deployment, and complimentary hosted options. Its GitHub repository positions it as a personalized AI SQL agent with a modular architecture and clear documentation, which typically correlates with active developer interest and contributions. Vanna’s presence in AI agent knowledge bases and tool directories, along with mention of v2.0 enterprise features and multi-LLM support, further suggests growing popularity in the AI and data engineering community. The combination of open-source availability, multi-database/LLM support, and a strong developer story (Jupyter, Streamlit, FastAPI, Teams integration) tends to drive broad experimentation and community usage beyond individual enterprises. These factors justify a higher popularity score compared to a specialized, closed-source identity resolution SaaS.