Infino vs Snowflake / Databricks
Infino can replace the read path for search, hybrid search, and selected SQL while Snowflake and Databricks continue to provide their broader data-platform ecosystem. Use Infino as a Parquet writer and optional indexed reader to reduce the operating cost of selective retrieval workloads, often by 10× or more.
infino explain superfile
A Parquet writer, an optional indexed reader
Infino writes spec-compliant Parquet with BM25 and vector index regions embedded before the standard footer. Other Parquet readers see ordinary columns; Infino also sees the retrieval indexes.
- An application can append JSON, Arrow, or vectors through Infino and use the resulting Parquet as an open data path readable by standard tools.
- Databricks and other direct Parquet readers can read those columns in place. Snowflake can query or load the Parquet; native Snowflake tables remain in Snowflake-managed storage.
- Infino can remain an optional reader, serving the retrieval queries that benefit from its indexes while the rest of the platform continues unchanged.
infino explain pruning
Why selective reads can touch fewer bytes
Infino makes retrieval indexes physical access paths inside DataFusion. The engine prunes files first, resolves candidate rows from the embedded index, and decodes only the columns needed by the remaining SQL plan.
| query shape | Infino read path | where it fits |
|---|---|---|
| selective text + scalar filters | manifest summaries skip files; the embedded FTS index produces candidate row IDs before columns are decoded | strong fit when a small fraction of the corpus matches |
| hybrid top-k + SQL | BM25 and vector retrieval produce a bounded relation; DataFusion applies filters, joins, and aggregates to those candidates | strong fit when retrieval sharply narrows the SQL input |
| broad scan or large aggregate | little can be skipped, so a large fraction of the columns still has to be read | usually a better fit for the existing warehouse or lakehouse path |
cat RETRIEVAL_COSTS.md
Where retrieval cost appears
Snowflake, Databricks, and Infino can all serve the workload. The difference is which storage, compute, and search resources are billed.
| workload | snowflake | databricks | infino |
|---|---|---|---|
| selected SQL | virtual warehouse compute + native table storage | Databricks SQL compute + object storage | storage + write tokens + read tokens + returned bytes |
| keyword + vector | Cortex Search serving and indexed data | AI Search index and serving endpoint | Parquet storage + retrieval work |
| hybrid query | warehouse SQL + Cortex Search | Databricks SQL + AI Search | one query path and retrieval meter |
infino cost --compare retrieval
Estimate the retrieval workload
Hybrid mode includes SQL plus Cortex Search or Databricks AI Search. SQL mode isolates the selected SQL shape. Each option receives the same corpus, write volume, query volume, and result size. Selective workloads that avoid broad scans or a separate search service can reduce serving cost by 10× or more.
Elastic + ClickHouse + Qdrant
Snowflake · Standard
Databricks SQL · Serverless
Postgres omitted at this scale i One RDS node cannot satisfy the RAM, vCPU, or storage requirement. Standard PostgreSQL does not shard horizontally. methodology →
Infino Cloud Pricing. methodology →
cat DEPLOYMENT.md
Evaluate one workload
Infino can be evaluated on one retrieval workload without changing existing Snowflake or Databricks workloads.
- The retrieval table contains only the text, vectors, and columns needed by the serving query.
- Representative queries can run through both paths to compare result quality, latency, freshness, and cost.
- Traffic can be routed independently for the retrieval path; other data-platform workloads are unaffected.
cat FAQ.md
Does Infino replace Snowflake or Databricks?
It can replace the read path for search, hybrid search, and selected SQL workloads. Snowflake and Databricks retain their broader ecosystem for governance, BI, notebooks, sharing, pipelines, and warehouse-scale analytics.
Where can Infino fit?
Infino can write the lakehouse Parquet and serve as the indexed reader for keyword, vector, hybrid, and selective SQL queries. Snowflake or Databricks can continue serving broad scans, large aggregations, and workflows that depend on their surrounding platform features.
What does the calculator include?
SQL mode includes storage and compute for the selected SQL workload. Hybrid mode adds Cortex Search or Databricks AI Search for full-text and vector retrieval. The same corpus, write volume, query volume, and result size are applied to each option.
Can Snowflake or Databricks serve the same retrieval workload?
Yes. Snowflake offers Cortex Search and Databricks offers AI Search, and both platforms can run SQL over their own data. The engineering choice depends on the required retrieval features, latency, freshness, operating model, and total cost for the workload.
Why can Infino use less compute for selected reads?
Infino uses scalar bounds, term filters, and vector centroids in the manifest to skip files before opening them. Within a surviving file, the embedded FTS or vector index identifies candidate rows and byte ranges before DataFusion decodes columns and runs the rest of the SQL plan. The benefit depends on selectivity; broad scans have less to prune.
How much can Infino reduce retrieval cost?
Selective retrieval workloads that benefit from index pruning or avoid a separate managed search service can see cost reductions of 10× or more. Actual savings depend on corpus size, query mix, selectivity, traffic, region, and vendor pricing. Use the calculator with your workload inputs.
Can other engines read files written by Infino?
Yes. Infino writes spec-compliant Parquet. Standard readers ignore the embedded retrieval-index regions and read the columns normally. Only Infino uses those regions for BM25 and vector access paths.
How can Infino be evaluated alongside the existing platform?
A bounded evaluation copies or streams the text, vectors, and columns required by retrieval into Infino, then runs representative queries through both paths. Result quality, latency, freshness, and cost can be compared before any traffic is routed differently.
Go deeper: architecture · vs postgres / clickhouse · pricing