- Products
- SolutionsUse Cases
Real-Time Analytics
Sub-second dashboards and data products on petabytes of data at any concurrency
Data Warehousing
Sub-second analytics on open lakehouse formats with no vendor lock-in
Observability in the AI Era
The most cost-effective alternative to Elasticsearch observability
Context Engineering
Hybrid search and fresh context for RAG, agents, and LLMs
IndustriesFinancial Services
Real-time analytics and fraud detection for banking, capital markets, and on-chain data
Ad & Media tech
Advertiser-facing reporting, content search, and game telemetry on one engine
Automobile & Transportation
Fleet telemetry, real-time recommendations, and multimodal autonomous driving search
- Docs
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- Pricing
- Contact us
Sub-second queries at high concurrency, on data that changes by the second
Point lookups, complex joins, log search, and vector retrieval run in one engine. Start with the workload you have now and add the next one without adding another database.
Four workloads, one engine
Every number below comes from a customer story published on this site. Follow any of them to the full write-up.
Real-Time Analytics
Interactive dashboards and customer-facing data products that answer in milliseconds, on data that is seconds old rather than hours stale. Multi-table joins and high-cardinality filters run without pre-aggregating or denormalizing first.
China's largest retailer by revenue runs live A/B testing and conversion-funnel dashboards on Doris, chosen after an evaluation against Druid, Elasticsearch, and ClickHouse. Peak ingestion reaches 600 million records in 10 minutes.
- 10B+
- Rows processed daily
- 10K
- QPS throughput
- 150ms
- Minimum query latency
Observability in the AI Era
Logs, metrics, and traces on one copy of the data, with inverted indexes serving keyword search from the same tables the aggregations read. The most cost-effective path off an Elasticsearch or Loki observability stack.
The AI company behind Talkie outgrew a Loki-based stack where regex queries scanned entire datasets and spiked compute. Its Doris-based platform now serves petabyte-scale log search, with 5:1 compression and tiered storage behind it.
- Under 2s
- Across 1B log records
- 10 GB/s
- Write throughput
- 70%
- Lower storage cost
Data Warehousing
Interactive analysis and reporting on open lakehouse formats. Query Iceberg, Hudi, and Paimon tables in place with Doris as the acceleration layer, and offload the batch warehouse without rewriting the data layout.
Global payment infrastructure that moved analytics off Snowflake. Payment data that took 5 to 10 minutes to become queryable now lands in 1 to 2 seconds, and 100 concurrent user dashboards average 1.2 seconds.
- 80%
- TCO reduction vs Snowflake
- 1.5s
- Multi-table JOIN response
- 3B
- Events per day
Context Engineering
Vector similarity, BM25 full-text, bitmap labels, and JSON filtering in a single SQL statement, over data that is fresh enough to answer an agent. One retrieval layer for RAG, agent memory, and training-set management.
Per-segment BM25 caused ranking instability on every segment merge for billion-vector talent matching. Global statistics with progressive filtering fixed it, and storage shrank from 10TB on 20 servers to 500GB on one.
- 94%
- Relevance, from 58%
- 400ms
- Latency at billion scale
- 384x
- Compression with IVPQ
Replacing something already in production?
Most teams arrive with an engine they have outgrown. These are the migrations they ran and what changed afterwards.
ClickHouse
Higher concurrency, more efficient joins, easier maintenance, and MySQL-compatible SQL, without the operational overhead.
- 64–90%
- Latency reduction
- Under 5%
- Slow queries, was 35%
- 3M/s
- Rows ingested per node
“We dropped slow query rate from 35% to under 5% and reduced point query latency from 250ms to 12ms. Real-time campaign attribution across 4,000 query templates and 700 fields now runs on one engine instead of three.”
Elasticsearch
Cut storage and write costs with high compression, and get full JOIN support with superior analytical query performance.
- 80%
- Lower operating cost
- 4x
- Write performance
- 72%
- Storage reduction
“By replacing our Elasticsearch cluster with Doris, we unified content search and analytics into a single platform. Write performance improved 4x, storage dropped 72%, and overall operational costs were cut by up to 80%.”
Trino / Presto
Unify your data warehouse and lakehouse query engine into one, and outperform a query-only engine.
- 3x
- Faster P95 latency
- 48%
- Hardware cost savings
- 96%
- Data cache hit rate
“Queries taking longer than 50 seconds dropped from 8% to just 1.5%. We migrated 100% of our ad-hoc and BI platform query workloads off Presto.”
Snowflake
Real-time analytics with higher concurrency and faster queries, at a fraction of the cost.
- 80%
- TCO reduction
- 1–2s
- Freshness, was 5–10 min
- 100
- Concurrent dashboards
“Payment data that took 5 to 10 minutes to become queryable on Snowflake now lands in 1 to 2 seconds. Multi-table JOINs return in 1.5 seconds, against 8 seconds on Snowflake.”
Redshift
Higher concurrency, a flexible storage-compute architecture, and seamless, non-disruptive scaling.
- Multi-compute, shared-data architecture isolates ingestion from serving
- Deploys on AWS, Azure, and GCP, or in your own VPC
- Scales without pausing writes or resizing a cluster
What data teams say after the migration
“We replaced separate Presto, Druid, and Spark clusters with one Doris engine over Paimon storage. Aggregation queries dropped from 40 seconds to 8 seconds. Concurrency handling scaled from 5 to 80 concurrent queries.”
“We evaluated HBase, Snowflake, ClickHouse, Hologres, GaussDB, and TiDB. VeloDB handled real-time analytics on 10TB+ of blockchain data at close to 1,000 QPS with P99 latency within 1 second, while cutting operating costs by more than 50%.”
“Engineers used to work across three independent pipelines for every analysis task. Now text retrieval, vector similarity, bitmap label operations, and JSON metadata filtering all run in a single SQL statement. Query latency dropped from minutes to seconds across petabytes of driving data.”
“More than 90% of our analysis workloads have migrated to Apache Doris, replacing the Hive and Kylin offline warehouse and the Spark and MySQL real-time stack that sat beside it.”
Or start from your industry
The same engine, framed around the workloads and constraints of a specific sector.
Financial Services
Real-time analytics, fraud detection, and on-chain data for banking, capital markets, and Web3.
Find out moreAd & Media tech
Advertiser-facing reporting, content search, and game telemetry on one engine.
Find out moreAutomobile & Transportation
Fleet telemetry, real-time recommendations, and multimodal autonomous driving search.
Find out moreStart with one workload
Spin up a cluster in under 60 seconds, or talk to an engineer about the workload you have in mind.
