The main Holistics alternatives in 2026 fall into two groups: dbt-native, code-first tools such as Lightdash, Preset, and Mode, and broader BI platforms such as Metabase, Looker, Power BI, and Knowi, which add native NoSQL and REST querying alongside SQL.
Quick Summary (TL;DR)
- Holistics is a proprietary, cloud-only platform built around AMQL, a code-based modeling language with Git version control, aimed at analytics-engineering teams.
- Its closest peers are other dbt-adjacent, code-first tools: Lightdash, Preset, and Mode, not general enterprise BI platforms.
- Holistics has no self-hosted edition and no native connector for MongoDB, Elasticsearch, or general REST APIs.
- Lightdash and Preset both have open-source, self-hostable cores; Mode is SaaS-only since its 2023 acquisition by ThoughtSpot.
- AI and natural language query shipped across nearly the whole category in 2025-2026, but almost all of it runs on a third-party cloud LLM (OpenAI, Anthropic, or Google models), not a private or local model.
- Knowi is the platform in this set that queries SQL and NoSQL sources plus REST APIs natively, and its AI runs on Knowi’s own models rather than a third-party LLM in self-hosted deployments.
- For a small team that wants to keep metrics defined in code and stay close to dbt, Lightdash or Preset is often a closer match than a larger platform like Knowi.
Why Teams Look Beyond Holistics
Holistics is a proprietary, cloud-only analytics platform built around AMQL (its analytics-model query language), a code-based modeling layer with Git version control. Its own positioning and independent reviewer commentary describe the typical buyer as an analytics-engineering-led team: small to mid-sized, comfortable writing SQL, and looking to define metrics once in code rather than clicking through a drag-and-drop model builder.
That profile matters because it points switchers toward a different shortlist than a typical enterprise BI search. Holistics competes most directly with other dbt-adjacent, code-first tools such as Lightdash, Preset, and Mode, even though general “Holistics alternatives” roundups often lead with larger, drag-and-drop platforms instead.
Common switch triggers
Holistics has no self-hosted edition, so teams that want to run analytics inside their own infrastructure have to look elsewhere. Its pricing is flat-rate rather than usage-based: current published tiers start around $960 per month for the Entry plan (10 users), $1,200 per month for Standard, and $2,400 per month for Security, with per-user add-ons on top.
Holistics is also SQL-warehouse-only. It has no native connector for MongoDB, Elasticsearch, or general REST APIs, so a team with a document store, search index, or third-party API in its stack needs a separate pipeline before Holistics can query it. Embedded analytics is a paid add-on, and multi-tenant row-level security requires an enterprise plan, which is a common friction point for SaaS teams that eventually want to put dashboards in front of their own customers.
Evaluation Criteria for a Holistics Switch
The right framework for a Holistics switcher looks different from an enterprise BI shortlist, mostly because the buyer and the budget are smaller. These are the factors that matter most:
- SQL/dbt modeling continuity: can you keep metrics defined in code, ideally synced from an existing dbt project, instead of rebuilding them in a GUI?
- NoSQL and REST coverage: does the tool query MongoDB, Elasticsearch, or an API directly, or does everything have to land in a SQL warehouse first?
- Team size and pricing fit: is pricing scaled for a 10-to-50-person team, or built around enterprise seat counts and annual contracts?
- Self-hosting: is there a free or open-source edition your team can run itself?
- AI posture: does natural language query run on a third-party cloud LLM, a bring-your-own-key model, or a private or local model?
- Embedding and multi-tenancy: how mature is customer-facing embedding, since it’s usually a secondary need for a Holistics-sized team rather than the primary one?
- Security and compliance: is SOC 2 or HIPAA documented, and at which plan tier does it apply?
How the Top Holistics Alternatives Compare
The table below covers the tools that most consistently surface alongside Holistics in current comparisons and search results, from its closest code-first peers to the larger BI platforms buyers also consider.
| Tool | Data architecture | Modeling approach | AI / NLQ | Deployment & cost signal |
|---|---|---|---|---|
| Lightdash | SQL data warehouse only, runs on top of an existing dbt project; no native NoSQL or REST source. | Code-based: auto-ingests dbt models into a metrics layer, or YAML if you’re not on dbt. | AI Agents on paid Cloud plans, connecting to external LLMs (OpenAI, Claude) via bring-your-own key; no on-prem LLM. | MIT-licensed core, free to self-host; Cloud Pro runs about $3,000/month for unlimited users. |
| Preset | SQL databases and engines via SQLAlchemy (including Druid, Trino); no native NoSQL or REST connector in core. | UI-defined datasets and metrics, with a dbt-sync feature to pull in dbt models. | Preset Chatbot on Enterprise plans, bring-your-own LLM via OpenRouter; supports Superset’s Model Context Protocol. | Apache-licensed open-source core, free to self-host; Preset Cloud is paid with no public pricing. |
| Mode | SQL warehouses only (Snowflake, Redshift, BigQuery); no NoSQL or REST ingestion. | Saved-query “Datasets” and views, with no formal modeling language; metric logic lives outside the tool. | AI Assist autocompletes and suggests SQL in the editor using GPT; it is an analyst aid, not an end-user NLQ chat. | Proprietary SaaS only since Mode’s 2023 acquisition by ThoughtSpot; per-seat pricing not public, free single-user Studio tier. |
| Metabase | Primarily SQL databases; MongoDB support is limited to paid tiers, no general REST ingestion. | Data Studio semantic layer (GUI or YAML), reaching general availability in early 2026. | Metabot runs queries in your own warehouse using a bring-your-own third-party LLM key; no private LLM option. | Apache-licensed open-source core, free to self-host; Metabase Cloud has a free tier for small teams plus paid plans. |
| Looker | SQL data warehouses (BigQuery, Snowflake); no direct NoSQL support beyond what the warehouse itself offers. | LookML: a proprietary, fully code-driven semantic layer defined once and reused across queries. | Looker Agents use Google’s Gemini for conversational analytics, grounded in the LookML model; cloud-based via Google. | SaaS on Google Cloud or on-prem via Kubernetes; enterprise/custom pricing sold through Google sales. |
| Power BI | Primarily tabular SQL sources; limited native NoSQL connectors (some Azure Cosmos DB support via Synapse). | GUI-driven tabular data model built in Power BI Desktop, with DAX and Power Query for transformations. | Copilot on Premium capacity, built on Azure OpenAI; uses your semantic model when one exists. | Free desktop app, Pro about $10/user/month; Premium capacity pricing runs roughly $5,000 to $20,000+/month. |
| Knowi | Native SQL and NoSQL querying (MongoDB, Elasticsearch, Cassandra) plus REST APIs, queried in place with no data movement. | AI-managed semantic layer: metrics and dashboards can be generated by AI agents, then reused across the platform. | Runs on Knowi’s own AI models; on self-hosted and on-prem deployments, no data is sent to a third-party LLM. | Cloud or on-prem (Docker/Kubernetes); enterprise commercial pricing, no free tier, trial available. |
Vendor Verdicts: dbt-Native Peers vs General BI Platforms
The closest peers: Lightdash, Preset, and Mode
Lightdash is the tool most structurally similar to Holistics: it auto-ingests an existing dbt project into a metrics layer, keeps modeling entirely in code, and offers a free, MIT-licensed self-hosted option that Holistics doesn’t have. Its trade-off is that Cloud Pro pricing, at roughly $3,000 per month for unlimited users, can land higher than Holistics’ tiers for a very small team, and its embedding support is newer than more established platforms.
Preset (the commercial layer on Apache Superset) is the more open, more connector-rich option: the core is Apache-licensed and free to self-host, and its dbt-sync feature pulls in dbt models without requiring a full rebuild. Its semantic layer is thinner than Lightdash’s or Holistics’ own AMQL, so teams that want strongly governed, centrally defined metrics may find it needs more manual discipline.
Mode fits teams doing SQL- and notebook-heavy exploratory analysis rather than teams that want a governed metrics layer at all. Since its 2023 acquisition by ThoughtSpot it is SaaS-only with no self-hosted edition, and its AI Assist helps analysts write SQL rather than letting end users ask questions in plain English.
General BI platforms often suggested alongside Holistics: Metabase, Looker, and Power BI
Metabase is the most common “easier” recommendation: it’s open-source, self-hostable for free, and its newer Data Studio semantic layer (general availability in early 2026) is closing the gap with code-based tools, though it’s GUI-first rather than code-first and its NoSQL support is limited to a paid-tier MongoDB connector. For a deeper look at how it stacks up against other tools, see this Metabase alternatives comparison.
Looker and Power BI both show up in general “Holistics alternatives” searches, but they represent a real step up in scope and cost: Looker’s LookML is code-based and governed but Google Cloud-centric and enterprise-priced, and Power BI’s tabular model is GUI-driven rather than SQL/dbt-native, which breaks the code-first workflow a Holistics switcher usually wants to keep.
Where Knowi Fits Best
Knowi is built for a different center of gravity than Holistics: teams whose data doesn’t live entirely in a SQL warehouse, and who need to query MongoDB or Elasticsearch alongside SQL databases and REST APIs without an ETL pipeline in between. For a Holistics switcher whose stack has grown to include a document store, a search index, or third-party API data, that’s a real gap none of Holistics’ closer peers, Lightdash and Preset included, currently close natively.
The modeling philosophy is also different. Instead of hand-writing every metric in AMQL or dbt YAML, Knowi’s AI-managed semantic layer generates and reuses metric and dashboard definitions across sources, which trades some of the git-diffable control an analytics engineer gets from a code-first modeling approach for less manual model maintenance. Teams that want every metric defined and reviewed as code, the way Holistics or Lightdash work, may find that trade-off unwelcome; see how the terminology differs in semantic layer vs. semantic model.
On deployment, Knowi’s AI runs on its own models rather than a third-party LLM when self-hosted or run on-premises, which matters for teams with data residency or compliance constraints. Be honest about fit either way: it is a larger, more commercially priced platform than Holistics, with no free tier, so a small analytics-engineering team that just needs code-based modeling on a SQL warehouse may still be better served by Lightdash or Preset.
Agentic Analytics Platform for SQL, NoSQL, and APIs.
If your team already writes SQL and version-controls metrics in code, Knowi extends that workflow across SQL and NoSQL data instead of a warehouse-only source. Connect MongoDB, Elasticsearch, REST APIs, and SQL databases directly, join across them without ETL, and let an AI-managed semantic layer handle the metric definitions you don’t want to hand-write for every source.
What you can do with Knowi:
- Query SQL and NoSQL sources like MongoDB and Elasticsearch alongside REST APIs, without a warehouse.
- Join across sources for a single metric without building a separate ETL pipeline.
- Let an AI-managed semantic layer generate and reuse metric definitions instead of hand-coding every model.
- Ask questions in natural language and get answers backed by the underlying query, not a cached summary.
- Embed dashboards and AI-powered analytics into your own product when you’re ready for customer-facing use cases.
- Deploy in the cloud or self-hosted, with on-prem deployments running Knowi’s own AI models instead of a third-party LLM.
Used by SaaS, healthcare, and data platform teams that need analytics across more than one type of data source without standing up a full BI engineering practice to get there.
Frequently Asked Questions
Is Holistics open source?
No. Holistics is a proprietary, cloud-only SaaS platform with no open-source or self-hosted edition, which distinguishes it from alternatives like Lightdash and Preset, both of which have open-source cores.
Does Holistics support MongoDB or other NoSQL databases?
No. Holistics is SQL-warehouse-only and has no native connector for MongoDB, Elasticsearch, or general REST APIs, so those sources need to be piped into a SQL warehouse first. Knowi is one of the few tools in this comparison set that queries MongoDB and Elasticsearch natively.
Can I self-host Holistics?
No. Holistics is cloud-only, though it offers multi-region hosting options. Teams that need to self-host will need to look at Lightdash or Preset, both of which have free, self-hostable open-source cores.
How much does Holistics cost?
Holistics uses flat-rate published tiers rather than usage-based pricing: Entry runs about $960 per month for 10 users, Standard about $1,200 per month, and Security about $2,400 per month, with per-user add-ons on top. No public pay-as-you-go pricing is offered.
What is AMQL, Holistics’ modeling language?
AMQL (Analytics Model Query Language) is Holistics’ code-based language for defining metrics and models, with Git version control built in. It’s the same category of tool as Lightdash’s YAML/dbt-driven metrics layer or Looker’s LookML, all of which define business logic once in code and reuse it across queries.
Which Holistics alternative fits a small analytics-engineering team best?
For a team that wants to stay closest to its existing dbt workflow with the option to self-host, Lightdash and Preset are the two most structurally similar tools evaluated here. A platform like Knowi becomes more relevant once that team’s data extends beyond a SQL warehouse into NoSQL or API sources, or once it needs to embed analytics for external customers.