Build vs Buy a Query Engine: When Does Rust Beat SaaS Analytics?

When does building a custom Rust query engine beat SaaS analytics?

Build a custom query engine once your annual vendor spend clears roughly $2-5 million and your query patterns are predictable enough for a small team to own them. Below that line, renting SaaS analytics is still the rational choice. Above it, the Rust ecosystem has made "build" cheap enough that not building starts to look like the risky option.

That's a new sentence to be able to write. For a decade, build-vs-buy in analytics infrastructure was a lopsided fight. Buy won almost by default, because building a query engine from scratch meant years of systems programming just to reach feature parity with what a vendor shipped on day one. That's no longer true. A handful of open-source Rust projects turned "write your own analytics engine" from a multi-year moonshot into a few quarters of integration work.

What triggers the build-vs-buy conversation?

Every build-vs-buy conversation in this space starts the same way: someone opens an invoice and stops breathing for a second.

Datadog's CFO confirmed in 2021 that one customer paid $65 million for the year. That customer was later identified as Coinbase. Coinbase responded by building an in-house observability stack on Grafana, Prometheus, and ClickHouse to get off Datadog entirely. Snowflake and Databricks bills tell a smaller but more common version of the same story: mid-sized companies routinely pay $2,000 to $10,000 a month, and enterprises push past $15,000-$50,000, for query volume that a single well-tuned machine could often handle. Vantage modeled a mid-sized BI workload, 10TB storage with 12 hours a day of active compute, at $8,870 a month on Snowflake versus $2,456 on ClickHouse Cloud, a 72.3% gap. Numbers like that are why the build question gets asked at all.

Why did Rust change the math?

The honest answer to "why now" is a stack, not a single tool. Apache Arrow gives you a columnar in-memory format every modern engine already speaks. Parquet gives you cheap, portable storage. DataFusion, a Rust-native SQL query engine, gives you the actual execution layer, the query planner, and the optimizer, all as a library you embed rather than a service you stand up. Together, people call this the FDAP stack: Flight, DataFusion, Arrow, Parquet.

DataFusion itself is the clearest proof this isn't a fringe bet. It was donated to the Apache Software Foundation and later promoted to a Top-Level Project, which happens when a project shows broad, independent, multi-company adoption rather than one company's pet framework. InfluxData spent four and a half years rebuilding InfluxDB's core around this exact stack, moving off a Go-based engine that couldn't handle unlimited cardinality or cheap object storage at the scale their customers needed. Quickwit took the same approach for search, building a Rust engine that separates storage from compute specifically to kill the idle-CPU cost that makes Elasticsearch expensive for low-QPS, high-storage log search. Our earlier look at the open-source infrastructure boom covers how this pattern shows up well beyond query engines.

The upshot: teams building custom engines today aren't writing a SQL parser and a vectorized executor from zero. They're wiring together battle-tested components and writing the 20% that's actually specific to their workload.

What's the real total-cost-of-ownership equation?

The Pragmatic Engineer's newsletter lays out the calculus plainly: bringing a vendor in-house starts to pencil out once vendor spend passes roughly $2-5 million a year, because a dedicated platform team of four to six engineers plus a manager typically costs $1-5 million a year depending on location and seniority. Below that spend line, you're paying a team salary to save less than the team costs. Above it, the math flips.

That's the entire decision framework, distilled to one formula: infra cost plus platform team cost has to beat vendor cost, measured over a real multi-year horizon, not a single renewal cycle.

When did building win in real case studies?

The wins are concrete and independently documented, not vendor marketing math.

• A NASDAQ-listed B2B SaaS company with 68,000+ customers adopted e6data's query engine and cut total cost of ownership by roughly 60%, worth more than $3 million a year, while improving p95 dashboard latency from 30 seconds to 1.5-2 seconds at 60 queries per second peak, and consolidating web and non-web analytics onto one lakehouse instead of a vendor engine whose compute cost doubled every 18 months.
• Definite migrated its production analytics stack from Snowflake to DuckDB in May 2024, cutting infrastructure cost by more than 70% and speeding typical queries 5-10x, while doubling engineering velocity because the query engine became an embeddable library instead of a network-dependent external service.
• InfluxData spent four and a half years rewriting InfluxDB's core in Rust to get unlimited cardinality, cheaper storage, and SQL support the original engine couldn't provide.
• Coinbase built its own Grafana/Prometheus/ClickHouse observability stack in direct response to that $65 million bill.
• Quickwit built a cost-driven Rust search engine because Elasticsearch's cost model didn't fit low-QPS, high-storage private search workloads.

The common thread across all five: predictable query shapes, high enough volume that unit economics matter, and staff-level engineers already on the team who could own systems code without a multi-year ramp. That last point matters more than people admit. Our practical framework for building vs. buying database infrastructure with AI covers how AI coding agents are lowering that bar further, but the case studies above predate that shift and still worked on hard engineering fundamentals alone.

When did buying still win?

The balancing case is Coinbase itself. After building the in-house alternative, Coinbase ultimately stayed on Datadog once the vendor restructured the contract, in part because replicating a similarly seamless developer experience in-house would have taken "tens of engineering years." The sticker price wasn't the only variable. The integration surface, the alerting maturity, and the years of accumulated tooling were real costs that don't show up in a spend comparison.

SigNoz makes the sharper version of this argument: the real justification for an observability or analytics bill isn't the line item, it's the cost of not having the capability, meaning the downtime and slow incident response you avoid. A custom engine that's cheaper on paper but less mature operationally can end up costing more the first time it fails silently during an incident.

What's the hidden cost nobody puts in the spreadsheet?

Every TCO model above assumes you can staff the platform team. That assumption gets expensive fast. Rust developers in the US typically earn $150,000-$220,000, with a $30,000-$70,000 premium per level over Python roles and a noticeably smaller hiring pool. If you don't already have systems engineers on staff, the breakeven point shifts higher than the vendor-spend threshold suggests, because you're not just funding a platform team, you're funding a harder, slower hiring process before that team even exists.

What five questions should you ask before you rewrite your engine?

Work through these in order before committing to a custom build:

1. Is the query engine actually differentiating? If your product's value has nothing to do with query latency or cost structure, a vendor's roadmap is doing free R&D for you.
2. What's the real three-to-five-year TCO, not the first-year number? Vendor discounts, egress costs, and platform-team salaries all change shape over time. Model the full window, not the renewal quarter.
3. Are your query patterns predictable? Engines built for one dominant workload, dashboards, log search, time series, are far cheaper to hand-roll than engines that need to handle arbitrary ad hoc SQL.
4. Do you already have staff-level systems engineers, or are you hiring into a scarce, expensive Rust market? The cost of the build is the cost of the hires, not just the code.
5. What's your actual vendor lock-in risk if you don't act? If the vendor relationship is stable, well-priced, and improving, that's a legitimate reason to keep renting, not a failure of nerve.

What does this mean for the software-ownership thesis?

The Arrow ecosystem didn't just make custom query engines cheaper. It made owning the query layer a legitimate asset decision instead of a desperate cost-cutting move. A team that builds on DataFusion or embeds DuckDB isn't writing a research project. It's assembling infrastructure that other serious companies already run in production. That's the same shift we've tracked across self-hosted alternatives to entire SaaS categories: the code you own outright compounds in value, the code you rent compounds in cost. The math still depends on your numbers, not the vendor's. But for the first time, most teams above that $2-5 million line can actually run the math instead of assuming the answer.