Key Takeaways
- Capital markets workloads are outgrowing general-purpose and open-source analytics stacks as data volumes rise, decision windows shrink, and latency tolerance falls.
- The open-source ceiling shows up in production through higher compute use, wider latency gaps, more data movement, and more engineering time spent maintaining the stack.
- KDB-X outperformed open-source alternatives in 58 of 64 TSBS benchmark scenarios while using a fraction of the available CPU and memory.
- KDB-X gives quant, trading, platform, AI, risk, and compliance teams one environment for time-series analytics, open formats, Python, SQL, q, vector search, GPU workloads, and audit.
- The best way to evaluate KDB-X is to run it against the real workload already slowing the team down: a query path, backtest, aggregation, replay process, TCA workflow, or AI retrieval use case.
The firms pulling ahead are running analytics, research, risk, and execution on infrastructure built for these conditions.
Open-source infrastructure has a ceiling
Open-source tools are part of the modern quant stack for good reasons. They are flexible, familiar, and supported by large developer communities. For exploratory analysis, feature engineering, reporting, and general data workloads, they can be the right choice.
Capital markets workloads put different pressure on infrastructure.
Market data is high-volume, high-cardinality, and time-sensitive. It has to support tick capture, historical replay, as-of joins, aggregation, signal validation, execution analytics, risk, surveillance, and audit.
The issue usually appears slowly.
Storage grows faster than planned. Queries need more tuning. Wide scans consume more CPU. Worst-case latency gets harder to control. More tools get added around the core system. More engineering time goes into keeping the stack fast enough.
That is the open-source ceiling.
It shows up when the team has moved beyond experimentation and needs stable performance across full-history, multi-asset, production workloads.
Market data is different from generic time-series data
Market data is mostly append-only and time-ordered, with late arrivals, corrections, and out-of-order events that production systems have to handle.
It is high-cardinality across symbols, venues, instruments, and identifiers. It is queried across seconds, days, and years. It often needs to be joined with executions, reference data, order books, news, research, and historical state.
For some desks, the relevant unit is microseconds. For others, it is milliseconds or seconds. The workload pressure stays the same: more data, more history, more joins, more users, and less tolerance for delay.
General-purpose systems can store this data. The harder test is whether they can keep performance consistent as the workload grows.
When they cannot, the cost appears in four places:
- More compute
- More memory
- More data movement
- More engineering time
That cost compounds.
The benchmark results
We ran the TSBS DevOps benchmark suite against KDB-X, QuestDB, ClickHouse, TimescaleDB, and InfluxDB.
Each system ingested the same dataset and ran the same query definitions on identical hardware.
KDB-X ran in Community Edition mode: one q process, 16 GB of memory, and four execution threads. That used about 1.5% of available CPU threads and 8% of system memory. Competing systems ran in default open-source configurations with access to full system resources.
The results:
- KDB-X outperformed competitors in 58 of 64 benchmark scenarios.
- The closest competitor averaged 3.4× slower across all queries.
- Worst-case queries showed much larger latency gaps.
- The gap held across short-range and multi-year datasets.
Benchmarks do not replace workload-specific testing. Teams should test against their own data, query paths, and operating constraints.
These results matter because the tested workload types are common in capital markets: aggregation, filtering, group-by queries, and multi-year analytics.
Those are the operations behind research, signal generation, execution analysis, risk, and surveillance.
Performance changes the economics
Infrastructure cost is driven by runtime, memory footprint, CPU usage, data movement, and the number of systems needed to support one workflow.
If a query runs 3× slower and uses more memory, cost rises in more than one place. If the same workflow also needs a time-series store, vector database, Python environment, dashboard layer, GPU layer, and custom glue, the cost rises again.
Every extra system adds monitoring, access control, reconciliation, and failure points.
KDB-X reduces those handoffs by putting high-performance time-series analytics, open formats, Python, SQL, q, vector search, AI libraries, dashboards, REST, object storage, and GPU acceleration in one environment.
Teams can run research, historical analysis, live analytics, and AI workflows closer to the same data. That reduces duplication, shortens validation cycles, and cuts the engineering work needed to move from idea to production.
Research throughput depends on infrastructure
Quant teams compete on how quickly they can test good ideas without lowering standards.
If full-history backtests take too long, fewer ideas get tested. If research code has to be rewritten for production, more assumptions change. If historical and live systems behave differently, teams spend time reconciling instead of improving strategies.
KDB-X gives teams one environment for research, production, and analysis. Python, SQL, and q can work over the same data. Teams can start in familiar tools and use q where performance matters.
That changes who can work with the data. The platform is no longer limited to q specialists. Quants, data scientists, engineers, and analysts can work closer to the same workflows without creating a separate stack for each group.
The result is a shorter path from idea to live workflow.
AI raises the bar again
AI adds more demand to the data layer.
Capital markets AI needs live, governed, time-aware data. It needs access to trading data. It needs to retrieve context, respect time, run close to production data, and leave an audit trail.
A fragmented stack makes this harder.
KDB-X supports AI where the data already lives. It brings time-series and vector workloads into the same environment, with AI libraries, MCP Server access, and GPU support for heavier workloads.
That means AI workflows can use the same data layer that supports research, execution analytics, risk, and oversight.
Fast, governed, time-aware data comes first. AI depends on it.
How to evaluate the stack
Most firms already have open-source tools, cloud platforms, internal pipelines, and legacy systems in place.
Start where the current stack is costing too much time, money, or control.
Look at:
- Storage growth under multi-year tick retention
- Compute usage during wide scans and aggregations
- Worst-case latency under load
- Number of systems involved in one workflow
- Rewrites between research and production
- Data movement between historical, live, vector, and AI systems
- Audit and replay requirements
- GPU plans for heavy analytics, simulation, and AI workloads
These are the places where architecture becomes a business constraint.
A stack that slows as data grows limits research. A stack that needs new tooling for every new workload slows engineering. A stack that separates AI from production data limits what AI can do.
KDB-X is built for this workload
KDB-X is the next generation of kdb+, built for capital markets workloads where speed, scale, and production trust matter.
It keeps the performance heritage of kdb+ and extends it with Python, SQL, q, open formats, object storage, dashboards, AI libraries, MCP Server, and GPU acceleration.
| What the workload needs | KDB-X capability | Why it matters |
|---|---|---|
| Fast analytics on high-volume market data | Columnar, vectorised time-series engine | Supports wide scans, joins, aggregations, and historical analysis across large market datasets. |
| One path from research to production | Python, SQL, and q on the same runtime | Quants can work in familiar tools, then move performance-critical workflows into production without rebuilding the logic in another system. |
| Fewer systems in the analytics stack | Time-series, vector, streaming, historical, and AI workloads in one environment | Reduces the need to maintain separate stores for tick data, vector search, dashboards, APIs, and AI workflows. |
| Access to modern data formats | Native Parquet and object storage support | Teams can query data where it sits and reduce the pipeline work needed to move data between lakehouse, research, and production environments. |
| Heavy workload acceleration | GPU support for compute-intensive operations | Large backtests, joins, simulations, aggregations, and AI workloads can run faster without forcing teams to rewrite everything around a separate GPU stack. |
| AI on governed market data | AI libraries, vector search, and MCP Server | AI workflows can work closer to live and historical trading data, with the same governance and audit context as the rest of the platform. |
| Broader access to analytics | Dashboards, REST, SQL, Python, and pgwire | More users can query, visualise, and build on the data without every request going through a small group of q specialists. |
| Production control | Replay, audit, real-time and historical processing in one environment | Trading, risk, compliance, and research teams can work from the same data model instead of reconciling separate systems. |
For quant teams, KDB-X reduces the time between hypothesis, backtest, validation, and production.
For trading teams, it supports analytics at the speed required by tighter execution windows.
For platform teams, it reduces the number of systems needed to support research, analytics, AI, and production workflows.
For AI teams, it gives models access to governed, time-aware production data.
For risk and compliance teams, it keeps replay and audit closer to the same data used by the front office.
Open-source tools will remain part of capital markets technology. The workload should decide where they fit.
For the data and analytics workloads that carry the most volume, the most latency pressure, and the most business value, firms need infrastructure built for the market they trade in now.
The market never waits. Your infrastructure should not either.
Start with the workload that hurts most
The best evaluation starts with a real workload.
Pick the query path, backtest, batch job, replay process, aggregation, TCA workflow, or AI retrieval use case that slows the team today.
Try KDB-X Community Edition and see how it performs against your own data and workflow: Download KDB-X Community Edition
Want to map KDB-X to a production workload? Book a technical briefing.
