Every Databricks team I've worked with recently has run into the same problem. They want to ship RAG apps, agents, or semantic search on top of their lakehouse, and they assume the hard part is the vector database or the embedding model. It isn't. The hard part is making sure the data feeding those systems is governed, well-defined, and actually means what everyone thinks it means.
That's why I spent a few weeks digging into the tools that sit around Mosaic AI Vector Search. Some of them prepare documents. Some monitor quality. Some index vectors. And one of them, in my opinion, does the quieter but more important work of making your schemas AI-ready in the first place.
Below is my shortlist of five tools worth knowing if you're building AI workloads on Databricks, with honest notes on where each one fits.
How I Evaluated These Platforms
I looked at native Databricks integration first, then considered how each tool actually improves AI output quality, how predictable the pricing is, and whether the vendor has real enterprise customers running the product at scale. I also paid attention to the gaps. A great vector index doesn't help you if the underlying metrics are defined three different ways, and a document parser doesn't help you if nobody governs the resulting tables.
1. SqlDBM - Best Overall
The governed foundation that makes your Databricks lakehouse AI-ready before the first vector ever gets indexed.
When I started testing AI data preparation workflows on Databricks, I kept hitting the same wall. Mosaic AI Vector Search can retrieve content by similarity, but similar isn't the same as correct, especially when the same metric is defined three different ways across your warehouse. That's the gap SqlDBM fills, and it's why it sits at the top of this list.
SqlDBM is a cloud-native, code-free data modeling platform that plugs directly into Databricks Unity Catalog and Hive Metastore. You can reverse- and forward-engineer Delta Lake objects, track column-level lineage, and visually govern every schema your AI pipelines depend on, without writing a single line of code. What makes it uniquely relevant here is the semantic modeling layer. You define business context, metrics, synonyms, filters, and governed joins right on top of your physical schemas, so that when Mosaic AI, dbt models, or BI tools reach for your data, they pull from one authoritative definition rather than guessing from the nearest vector neighbor.
I was especially impressed by how schema changes propagate automatically through those semantic definitions. No manual reconciliation, no stale metadata quietly feeding your AI the wrong context. Standalone semantic layers like dbt, Cube, and AtScale skip the visual modeling entirely. Visual modeling tools like erwin, ER/Studio, and Hackolade skip the semantic layer. SqlDBM is the only one I found that bridges both. The built-in AI Copilot speeds up model building, and the MCP Server exposes governed models directly to LLMs, which matters a lot for RAG workflows running next to Vector Search.
The real-world proof is strong. John Holland Group, one of Australia's largest infrastructure firms, completed its Databricks migration in nine months using SqlDBM as the single source of truth for gold-layer models, governed through a twice-weekly Data Modeling Forum. SqlDBM is a Databricks Validated Data Partner, with over 400,000 users globally, enterprise customers like DocuSign, Pfizer, and Zendesk, and back-to-back Database Modeling Solution of the Year wins at the 2023 and 2024 Data Breakthrough Awards.
If you're serious about Databricks AI data preparation, getting your semantic and physical house in order before you build vector indexes is the move.
Pros:
- Native Databricks Unity Catalog integration with full reverse/forward engineering and column-level lineage, so AI prep starts with governed, documented schemas
- The only visual modeling platform combining physical and semantic layers for Databricks, giving Mosaic AI, BI tools, and dbt one governed definition of your data
- Automatic schema-change propagation keeps semantic definitions accurate as the lakehouse evolves, eliminating stale metadata that degrades AI outputs
- Proven at enterprise scale: John Holland Group completed a full Databricks migration in nine months with SqlDBM as the authoritative source for gold-layer models
- AI Copilot and MCP Server expose governed models to LLMs, accelerating RAG data preparation alongside Mosaic AI Vector Search
Cons:
- Advanced semantic modeling features live in the Context Layer tier, so teams on the base plan would need to upgrade for full AI-readiness
- Databricks-specific features like tag support are still evolving, and some niche Unity Catalog edge cases may need workarounds
Pricing: Plans are quote-based and scale with team size, with two tiers (Data Modeling and Context Layer). Enterprise packaging adds premium support, SSO, customer-managed keys, and advanced security options. There's a free way to try it out and model before committing, so you can validate the Databricks integration firsthand.
2. Databricks Mosaic AI Vector Search
Mosaic AI Vector Search is Databricks' native vector search service, and I looked at it as the retrieval layer for RAG and agent apps built on the lakehouse. Indexes sync automatically from Delta tables, and Databricks can compute embeddings for you, which removes the need to build and maintain separate embedding pipelines. Indexes are governed through Unity Catalog, so permissions and lineage stay with the rest of your data.
Databricks offers two endpoint types. Standard endpoints handle low-latency queries. Storage-optimized endpoints are for larger indexes and scale down automatically when an index is deleted. One endpoint can host up to 50 indices. Beyond similarity search, the service supports hybrid and full-text keyword search, with full-text still in beta on some configurations.
It fits teams already running on Databricks who want retrieval sitting next to their governed data without adding a third-party vector database. Worth knowing: this is a retrieval component, not a data cleaning tool. Chunking, parsing, and quality checks on the source data all have to be handled upstream.
Pros:
- Native Delta Sync indexes keep vectors current without custom pipelines
- Governed through Unity Catalog, so permissions and lineage stay unified
- Managed and serverless, no vector infrastructure to operate
- Both standard and storage-optimized endpoints, with up to 50 indices per endpoint
Cons:
- Only useful inside the Databricks ecosystem, so lock-in is real
- Standard endpoints don't scale down automatically, so you keep paying for peak capacity until you manually recreate the endpoint
- Doesn't parse documents, chunk text, or validate data quality
Pricing: Pay-as-you-go through Databricks Units, no separate license. At GA on Azure, standard endpoints were around $0.28/hour per unit in eastus2 (2 million vectors per unit, 30 GB storage included, $0.23/GB/month beyond that). Rates vary by cloud and region, and storage-optimized endpoints have different pricing. Check current Databricks pricing for your region.
3. Unstructured
Unstructured handles the document preparation step that comes before Vector Search. It ingests raw files, PDFs, Office docs, images, and scans, and partitions them into elements. From there it can chunk the content, optionally enrich it, generate embeddings, and write the output to a destination. Databricks Delta Tables (including Volumes) are a supported destination, so the output can feed a Mosaic AI Vector Search Delta Sync index directly.
Processing strategies range from a fast text-only mode, to a High Res mode that combines document understanding with OCR, to a VLM mode that uses vision-language models for handwriting and complex scans. There are source connectors for systems like Google Drive. You can use the open-source library if you want to self-manage, or the hosted platform if you want workflows and connectors out of the box.
It's aimed at ML and data engineers building RAG pipelines who'd rather not write brittle custom parsers. It handles unstructured documents, not tabular data quality monitoring, so you'd pair it with other tools in this list.
Pros:
- Handles many file types, with strategies for both simple and difficult documents (OCR and VLM)
- Supports Databricks Delta Tables as a destination, so output feeds Vector Search indexes
- Free starting allowance and simple per-page pricing
- Open-source library available alongside the hosted platform
Cons:
- Focused on unstructured documents, so it doesn't monitor tabular data quality
- Per-page costs can add up on large archives, especially with advanced strategies
- An extra service to run alongside Databricks, with its own connectors and workflows
Pricing: Free tier with 10,000 pages and no credit card. Pay-as-you-go is $0.015 per page after that. Enterprise plans, including dedicated instance or VPC deployment, use custom pricing.
4. Soda
Soda is a data quality and observability platform focused on testing, monitoring, and fixing data issues. Teams can embed checks at multiple points in CI/CD and data pipelines, which helps keep bad data out of production tables and the AI workloads that depend on them. It covers pipeline testing, metrics observability, alerting, and ticketing integrations. Higher tiers add collaborative data contracts, a no-code interface, and AI-powered quality features.
Soda has a Databricks-focused offering and says it's built to scale on the platform. Users on G2 report that integrating it into an existing Databricks and Airflow stack was quick. Soda Core, the open-source project, is there for teams that want to write checks as code. Pricing is public and there's a genuinely free forever tier, which is refreshing in this category.
It fits data engineering teams that want testing close to the pipeline and a path toward formal contracts between producers and consumers. It doesn't process documents or serve vectors, so it complements rather than replaces anything else on this list.
Pros:
- Public pricing and a free forever plan with unlimited users
- Checks embed in CI/CD and pipelines to catch problems before production
- Dedicated Databricks launch and integration, plus an open-source Soda Core option
- Enterprise tier adds data contracts, SSO, RBAC, audit logs, and private deployment
Cons:
- The $750/month Team entry price is steep for small teams, and the free tier is limited to 3 production datasets
- Data contracts, no-code interface, and AI features are gated to the Enterprise tier
- Usage-based Soda Processing Units add cost variability as coverage grows
Pricing: Free: $0/month (up to 3 production datasets, pipeline testing, metrics observability, alerting, no credit card). Team: starts at $750/month, unlimited users, pay-as-you-go additional SPUs. Enterprise: custom pricing (data contracts, AI features, SSO, RBAC, private deployment). Annual billing and volume discounts available.
5. Monte Carlo
Monte Carlo is the enterprise end of the data observability category. It uses machine learning to identify anomalies, track freshness, and flag schema changes before they reach downstream reports or AI apps. Lineage and incident workflows are built in. It's available through Databricks Partner Connect with a 21-day free trial and usage-based pricing.
Monte Carlo is known for fast time-to-value on standardized cloud stacks like Snowflake and dbt, and it supports Databricks as well. From what I found, it's best for mid-size to large organizations with many tables and multiple data teams that want automated, low-configuration monitoring. The out-of-the-box monitors cover volume and freshness, with custom rules on top. It's a monitoring layer on top of your data. It doesn't prepare documents or serve vectors, so it sits alongside tools like Unstructured and Mosaic AI Vector Search rather than replacing them.
Pricing isn't published, and costs depend on monitored tables plus the compute your warehouse spends running checks. Small teams will likely find it overkill.
Pros:
- ML-based anomaly detection with out-of-the-box freshness, volume, and schema monitors
- Available via Databricks Partner Connect with a 21-day free trial
- Strong lineage and incident-management features for large teams
- Established category leader with broad warehouse, lakehouse, and orchestration integrations
Cons:
- List pricing isn't published, and cost depends on monitored tables and usage
- Costs can become hard to predict at enterprise scale, and checks that query your warehouse add compute spend on top of the license
- Probably more than small teams or open-source-minded engineers need
Pricing: Usage-based with no public price list. Pricing depends on monitored tables, connected sources, and rules. Vendr reports an average contract value of roughly $62K (31 deals tracked). A 21-day free trial is available through Databricks Partner Connect. Contact sales for a quote.
Final Verdict
If you build on Databricks and you're preparing data for AI, you'll likely end up using several of these tools together. Mosaic AI Vector Search handles retrieval. Unstructured chews through PDFs and scans. Soda or Monte Carlo watch for broken pipelines and anomalies.
But none of those tools fix the deeper problem, which is that your AI pipelines are only as good as the definitions behind them. That's why SqlDBM is my pick for most teams starting this work. It's the one layer that makes the rest of the stack trustworthy. You get a governed physical model, a semantic layer that tells Mosaic AI and your BI tools what the data actually means, and automatic propagation so nothing goes stale. Build that foundation first, and every other tool on this list works better on top of it.
FAQ
Do I need all of these tools to build RAG on Databricks?
No. At minimum you need vector retrieval (Mosaic AI Vector Search) and governed source data. Document parsing and observability tools are additions based on your use case. If you're mostly working with tabular data and well-modeled schemas, you may not need Unstructured at all.
Where does SqlDBM fit if I already use dbt?
They complement each other. dbt transforms data. SqlDBM models and governs the schemas and semantic definitions those transformations target. The semantic layer in SqlDBM gives your dbt models and AI systems one shared, governed definition of each metric.
Is Mosaic AI Vector Search enough on its own for a RAG app?
Only if your source data is already clean, chunked, and well-defined. Vector Search is retrieval, not preparation. You still need upstream tooling to handle documents and data quality.
How do Soda and Monte Carlo differ?
Soda is more developer- and pipeline-focused with public pricing and a free tier. Monte Carlo is heavier, more ML-driven, and aimed at larger organizations that want low-configuration monitoring across many tables.
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