How much does it cost to build an AI trading system?

Why cost ranges are so wide (and what actually drives them)

"AI trading system" spans everything from a single signal running against a $10m book to a full multi-strategy platform running institutional capital. Real costs are driven by four things: universe size, data sources, latency requirements, and whether the operator is a proprietary trader, a systematic fund, a discretionary fund adding AI, or a brokerage adding AI-driven flow.

The good news in 2026 is that the fixed-cost floor has fallen dramatically. Frontier LLMs are cheap enough per call to run over meaningful universes. Cloud data infrastructure is priced for the mid-market. Firm-owned model training against a fund's own book is genuinely achievable without a full research-lab investment.

What actually goes into the cost

• Initial build — Architecture, data pipelines, model layer, backtesting harness, evaluation, deployment and initial operational runbook. This is the one-off investment.
• Infrastructure — Warehouse, vector store, model gateway, backtesting compute, live-inference compute. Priced per month, scales with universe and volume.
• Data — Market data, fundamentals, alternative data, filings/transcripts corpus. Alt-data alone can dwarf every other line for large deployments.
• Model costs — Frontier LLM API calls, fine-tuned model hosting, embedding costs. Now the cheapest line for most systems.
• Team or operator — Internal build & run team (data scientists, ML engineers, quants, ops) or a delivery partner running under Operator engagement while your team takes over.
• Evaluation and compliance — Backtesting infrastructure, model governance, audit trail, provenance logging. Non-negotiable for any regulated deployment.

Realistic cost tiers in 2026

Ranges assume KJ Capital Build + Operator engagement; internal-only builds carry higher team cost.

Where the money actually goes in year 1

For a typical Build engagement (£150k – £600k), the split is usually 40–50% engineering (data pipelines, model layer, backtesting harness, deployment infrastructure), 20–30% data (market data + any alt-data corpus needed for the first system), 15–20% model and evaluation work (fine-tuning, prompt engineering, evaluation harness), and 10–15% operations (runbooks, on-call, incident response).

The single biggest saving vs a comparable in-house build is not the engineering itself — it is the year of hiring, evaluation and mistakes an internal-only team goes through before shipping their first production system.

Ongoing infrastructure economics

Ongoing costs are now dominated by data and compute, not by model API calls. A serious research automation platform running over a 500-name universe with overnight LLM reasoning, retrieval, backtesting and live inference typically lands £25k–£70k per month all-in — warehouse, vector store, model gateway, alt-data feeds and evaluation infrastructure combined.

The one line that can dominate everything else is alternative data. A single premium alt-data feed can run £100k+ per year. Serious funds are disciplined about which feeds justify the cost, and use AI reduction to squeeze more signal from fewer feeds rather than paying for more.

Total cost of ownership vs vendors

The apples-to-apples comparison for most firms is not "build vs do nothing" — it is "build vs continue paying vendor licences". A mid-sized hedge fund on Bloomberg + FactSet + a couple of premium alt-data feeds is typically already spending £1m–£3m a year on vendor stack. Redirecting a fraction of that into a firm-owned intelligence layer produces a proprietary asset that compounds every quarter, on top of (not instead of) the vendor stack.

The strategic difference at year three is enormous: one fund has renewed with its vendors again and owns nothing; the other owns a firm-specific research and portfolio-construction layer that its competitors cannot buy.