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Tracked since2026
0 reviews trackedThe Bottom Line
Entry price
Free plan available, paid tiers above
Biggest pro
Significantly reduces the burden on data teams by automating responses to common queries.
Biggest con
Complex analytical questions can consume credits quickly, potentially increasing costs.
TL;DR - Upsolve AI
- Build and deploy AI analytics agents that provide trusted, context-verified data answers.
- Integrates with 30+ data sources and existing dbt projects to leverage institutional knowledge.
- Reduces data team workload by automating responses to common analytical questions and improving data accessibility.
Pricing: Free plan available
Best for: Growing teams
What is Upsolve AI?
Upsolve AI is a platform designed for data teams to build and deploy AI-powered analytics agents. These agents enable anyone in an organization to get relevant, trusted answers from their data without waiting for data team assistance. The platform addresses common challenges faced by data teams, such as overwhelming insight requests, the unreliability of AI models in production due to lack of context, and the difficulty of orchestrating scattered institutional knowledge.
The platform functions as a two-sided solution: data builders get a fully observable agent-building studio, while end-users receive visual insights on demand. It integrates with various data sources, including over 30 SQL database connectors and existing dbt projects. Upsolve AI focuses on ensuring accuracy and trust by encoding institutional context, verifying KPIs, matching SQL patterns, and providing full data lineage. This allows for rapid deployment of reliable agents, with accuracy improving over time through conversational interactions.
Pros & Cons
Pros
- Significantly reduces the burden on data teams by automating responses to common queries.
- Ensures high accuracy and trust in AI-generated insights through robust context management and verification.
- Accelerates time to insight for business users, allowing them to self-serve data questions.
- Offers flexible deployment options including cloud-hosted, on-prem, and VPC for Enterprise customers.
- Provides a comprehensive suite of tools for building, managing, and observing AI agents.
Cons
- Complex analytical questions can consume credits quickly, potentially increasing costs.
- Requires initial setup and integration with existing data infrastructure and institutional knowledge.
- Reliance on AI models means potential for misinterpretation if context is not perfectly encoded.
Key Features
Analytics agent building studio30+ SQL database connectorsdbt project integrationSemantic models, metrics, dimensions, and definitionsContext management suite for institutional knowledgeKPI verification and SQL pattern matchingFull data lineage trackingVisual insights and charts for end-users
Pricing Plans
Free TrialPricing checked Jun 29, 2026
Free
Free
- Full test drive of Upsolve with all Pro features unlocked
- 2,000 one-time credits (~200 analytical questions)
- All Pro features unlocked
- Community support
- Test drive and see results
- 50+ data connections
- Unlimited agents
- Charts & visualization
Starter Pro
$500 / mo
- For teams getting started with AI-powered analytics.
- 2,000 credits/month included (~200 analytical questions/mo)
- $0.05 per credit overage
- 50+ data connections
- Unlimited agents
- Charts & visualization
- Context management suite
- Full observability & eval suite
Team
$2,000 / mo
- Go live with embedding, RBAC, and your semantic layer.
- 10,000 credits/month included (~1000 analytical questions/mo)
- $0.05 per credit overage
- Everything in Pro, plus
- Row-level security / RBAC
- Embed Upsolve in your product
- Multi-tenant support
- AI Cockpit for data modeling and semantic layer generation
Enterprise
Custom
- Your models, your network, your rules. We deploy alongside you.
- Base platform fee + credits or BYOM + Upsolve surcharge
- Everything in Team, plus
- On-prem / self-host / VPC deployment
- SAML SSO, HIPAA, SOC 2
- Model routing (BYOM)
- Forward Deployed Engineering for semantic layer & data modeling
- Forward Deployed Engineering for context management
Reviews
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Upsolve AI FAQ
How does Upsolve AI help data teams manage insight requests?
Upsolve AI significantly reduces the burden on data teams by automating responses to common data queries. It allows organizations to deploy AI-powered analytics agents that provide relevant, trusted answers to end-users, freeing up data team resources.
What kind of user benefits most from Upsolve AI?
Upsolve AI is designed for data builders who need a studio to create and manage AI agents, and for business users who require visual insights on demand. It accelerates time to insight for business users, enabling them to self-serve data questions without waiting for data team assistance.
How does Upsolve AI ensure the accuracy of its AI-generated insights?
Upsolve AI ensures high accuracy and trust by encoding institutional context, verifying KPIs, and matching SQL patterns. It also provides full data lineage, and its accuracy improves over time through conversational interactions with the agents.
Can Upsolve AI integrate with existing data infrastructure?
Yes, Upsolve AI integrates with various data sources, including over 30 SQL database connectors and existing dbt projects. This allows for rapid deployment of reliable agents within an organization's current data ecosystem.
How is Upsolve AI priced?
Upsolve AI is available on a free tier, allowing users to get started with its features. For more extensive usage and advanced functionalities, paid plans are offered.
What are the primary limitations of using Upsolve AI?
A primary limitation is that complex analytical questions can consume credits quickly, potentially increasing costs. Additionally, it requires initial setup and integration with existing data infrastructure and institutional knowledge.
How does Upsolve AI compare to Julius AI for data analytics?
Upsolve AI focuses on providing a two-sided solution where data builders create observable agents for end-users to get trusted, AI-driven data answers. It emphasizes encoding institutional context and verifying KPIs for accuracy, whereas Julius AI offers a different approach to AI-powered data analysis.
Source: upsolve.ai