Why Rust for Solana MEV: Comparing the Alternatives

The Question That Refuses to Stay Settled

Every time the bot misbehaves, the same question surfaces in the back of my head: did I pick the right language? It is the kind of doubt that follows any individual developer competing against well-capitalized teams. The answer I keep arriving at — Rust — is not the answer that would minimize my development friction. Python would do that. The answer is the one that minimizes the gap between how fast I think my bot is and how fast it actually is, every single iteration of every single block.

This post is the version of that argument I would hand to someone standing where I stood a few months ago, staring at a blank Cargo.toml and wondering whether they really need to put themselves through the borrow checker. The short version: for a Solana arbitrage bot, the choice of language is not about productivity. It is about whether your worst case is something you can reason about.

What This Bot Is Actually Doing

Before comparing languages, it helps to be honest about what an on-chain arbitrage bot does on every block. It listens to a stream of state updates from the network. It decodes binary account data into pool reserves and tick arrays. It runs simulations across many candidate routes — many of which share intermediate hops, so caching matters. It scores each candidate against current fees and tip levels. It builds and signs a transaction, attaches a tip, and submits it in a tip-priority competition. And then it does this again, hundreds of milliseconds later, for the next block.

Most of that work is not network-bound. Most of it is CPU-bound, allocator-bound, and dominated by tail latency. It is closer to a high-stakes arbitrage runner that has to decide, in the time it takes a block to confirm, whether to commit capital. The runner who hesitates loses. The runner who guesses wrong about how long their own decision will take also loses, because they will commit late.

This is the framing that turns the language question from a matter of taste into a matter of mechanics.

The Python Case, Stated Honestly

Python is not a strawman here. It is the most productive general-purpose language I have used. The Solana ecosystem has reasonable Python tooling for RPC interaction. A first prototype of an arbitrage scanner could plausibly be standing up and decoding mainnet pools within an afternoon. That is real value, and I am not interested in pretending otherwise.

The trouble shows up when you measure.

The Solid Quant team published a careful benchmark comparing JavaScript, Python, and Rust implementations of the same MEV bot operations (Solid Quant). The numbers are worth sitting with. Creating an HTTP provider — a step that happens at startup but also during reconnect — took roughly 1,100 microseconds in Python and around 8 microseconds in Rust, per Solid Quant's measurements. A batch multicall of 3,774 calls took roughly 1,600 milliseconds in Python and about 170 milliseconds in Rust in the same benchmark. Retrieving touched pools on a block update took roughly 20 milliseconds in Python versus around 10 milliseconds in Rust.

The ratios vary by workload, but the pattern is consistent: the closer the operation gets to pure CPU work or to tight serialization loops, the larger the gap. And these are not microbenchmarks of arithmetic in a vacuum. These are the actual operations a bot performs every block.

For a single run, a 1.5-second batch call sounds tolerable. On a fast block cadence, it is a death sentence. By the time the Python implementation finishes scanning, the opportunity has been seen, simulated, packaged, and landed by someone else.

The Worse Problem: Variance, Not Mean

All of that, however, is the easier half of the case against Python. The harder half is variance.

A developer named frankdotdev wrote one of the most useful first-person accounts of migrating a trading engine from Python to Rust (DEV Community). The headline numbers are striking on their own: average tick-to-trade latency dropped from around 12 milliseconds in Python to roughly 40 microseconds in Rust, which the author describes as roughly a 300x improvement in average latency. But the mean is not what made the migration urgent. It was the spikes. Python latency would occasionally jump up to about 80 milliseconds, seemingly at random, in the same case study.

The reason is the garbage collector. Python manages memory by periodically pausing your program to clean up. As frankdotdev puts it: "In web development, a 50-millisecond pause is unnoticeable. In trading, a 50-millisecond pause is the difference between a profit and a loss." The pauses are not a Python-specific defect; they are the predictable cost of any language whose runtime owns memory on your behalf.

The contrast with Rust is captured in one line from the same migration story: "If my loop takes 5 microseconds to run, it takes 5 microseconds every single time." That is the property that matters. Determinism. Not speed for its own sake, but the freedom to know how slow you are, every iteration.

For an MEV bot, that property is the entire game. A bot that scans in a few milliseconds with occasional 80-millisecond stalls is not a fast bot. It is an 80-millisecond bot at the worst possible moments, because the spike sample tends to land inside a tip-priority competition where the runner-up wins nothing.

But What About asyncio?

The natural objection from the Python side is asyncio. If the problem is concurrent I/O, surely an event loop solves it. This was my own reflexive response. The data does not support it.

Cal Paterson published a detailed benchmark of Python web frameworks comparing synchronous and asynchronous implementations (calpaterson.com). The headline is awkward for the async camp. Synchronous frameworks like Gunicorn-with-meinheld served around 5,500 to 5,800 requests per second with P99 latencies in the 31 to 32 millisecond range. Async frameworks served fewer requests per second — Uvicorn-with-Starlette at around 4,952, AIOHTTP at around 4,501, Sanic at around 4,687 — and posted P99 latencies of 75, 76, and 85 milliseconds respectively, per Paterson's measurements. The Daphne-with-Starlette combination posted a P99 of around 364 milliseconds. Paterson also notes that "every single async implementation managed to fall over" during testing, while the synchronous frameworks remained stable.

The explanation is structural. Asyncio's concurrency is cooperative: tasks yield voluntarily. "In async Python, the multi-threading is co-operative," Paterson writes; one task that does not yield can starve every other task on the loop. There is no preemption to rescue you. The throughput wins that async claims usually come not from the event loop itself but from "how much Python code has been replaced with native code," which is a different argument entirely.

For a bot, this matters because the workload is exactly the kind that triggers this pathology. A long-running pure-CPU computation — a route search, a pool simulation, a numeric solver — does not yield to the event loop. It just runs. Meanwhile every other task that should be observing new state updates, tracking tip levels, or sending the next transaction is queued behind it. Asyncio does not solve the GC problem and adds a new one of its own.

The JavaScript Branch

A reasonable question is why I did not simply use JavaScript or TypeScript, given that the Solana ecosystem has decent SDK support there. The benchmark numbers from Solid Quant put JavaScript somewhere between Python and Rust on most operations: HTTP provider creation around 60 to 70 microseconds, large batch multicalls in the neighborhood of 1,100 milliseconds, and pending-transaction streaming with what Solid Quant measured as roughly an 18-millisecond average delay relative to the Rust implementation. JavaScript is genuinely fast for many workloads.

But JavaScript has its own variance problem. V8's garbage collector is more sophisticated than CPython's, but it is still a garbage collector. Tail latency under sustained allocation pressure is not something the JIT can rescue you from. And the developer story for parsing binary on-chain data — accounts, instructions, error codes — is uncomfortable in a way that has nothing to do with performance and everything to do with the type system. When the wire format is a packed C-style struct, languages with first-class structs and zero-cost serialization libraries have a real advantage that is hard to give up once you have used them.

A practitioner's experience matches this. The author of pawelurbanek.com wrote about migrating his MEV bot off the open-source simple-arbitrage JavaScript template: "When the bot's complexity grew, I found myself spending a lot of time debugging runtime errors, and it got me into learning Rust" (pawelurbanek.com). His final architecture ended up around ten thousand lines of Rust on the client side, with only roughly five hundred lines of Solidity for on-chain logic. The complexity does not live where many newcomers assume it lives.

The other consideration is ecosystem. On Solana, virtually all of the production MEV tooling — including the Jito client — is written in Rust (Calibraint guide). On Ethereum, Flashbots' own block builder, rbuilder, is described as "a blazingly fast block builder written in Rust" (Flashbots). When the firms that define the infrastructure pick a language, that picks the language for everyone interacting with that infrastructure at the lowest layers. Reusing types, reusing serialization code, reusing client libraries — these matter more than the abstract performance benchmarks once you are debugging at three in the morning.

What About C++?

If the goal is the lowest possible latency, the historical answer is C++. Most of high-frequency trading still runs on C++. So why not here?

The performance gap between Rust and C++ is now small enough that it stops being decisive. A 2026 evaluation of Rust in HFT systems on dasroot.net measured Rust order execution latency at approximately 120 nanoseconds against C++ at approximately 110 nanoseconds — what the author characterizes as Rust achieving roughly 98.7% of C++'s latency performance (dasroot.net). The same evaluation reported roughly 99.99% fewer memory-related errors in the Rust implementation. Whatever you make of the precise figures, the directional claim is consistent across many sources: Rust gives up almost nothing to C++ on hot paths and gives back a great deal in safety guarantees.

That safety guarantee is not abstract. It is the difference between a use-after-free that corrupts a transaction signature and a compile error. It is the difference between a data race in a hot loop that silently posts wrong tip levels and a Send/Sync rejection at build time. For an individual developer with no team, no QA, and no rollback budget, the borrow checker is a free senior engineer who reviews every change.

There is also a softer point. Solana's own validator client and most of the on-chain program tooling is Rust. Picking C++ for the bot side means writing your own bridge to a Rust ecosystem on the other side. Picking Rust means using the same types the validator uses. That alignment is worth more than a few nanoseconds.

What Rust Actually Buys You

It is worth being explicit about what "Rust gives you" actually means in the context of a Solana MEV bot, because the marketing version of the language pitch — speed, safety, concurrency — is too generic to be useful.

The first thing it buys is predictable allocation. Memory is freed when the value goes out of scope. There is no background thread that wakes up at an inconvenient moment. The cost of allocation is paid where you allocate; the cost of freeing is paid where you free. This is what makes "5 microseconds, every single time" a sentence you can write about your code.

The second is zero-cost abstractions. Iterator chains, option types, error propagation — all of the high-level constructs that make code readable in modern Rust compile down to the same assembly you would write by hand. You do not pay for the abstraction at runtime. This matters because an MEV bot has a lot of layers — fetch, decode, simulate, score, build, sign, submit — and you want each of them to be expressed clearly without compounding costs at every boundary.

The third is structural concurrency primitives in the form of the tokio runtime. Tokio's design includes automatic cooperative task yielding, which directly addresses the starvation problem that asyncio has. Tokio's official posts on the scheduler and on cooperative yielding describe a runtime designed to keep one long-running task from blocking everything else (Tokio). Independent evaluations measure Tokio adding on the order of 8 microseconds of latency on localhost and 10 microseconds over the network (Zenoh). For comparison, that is a far lower additional cost than the average tick-to-trade latency of the Python implementation in frankdotdev's case study.

The fourth is a serialization story that fits the wire format. Solana account data is laid out as packed binary. Rust libraries map onto that layout with little or no copying. Decoding a pool account becomes pointer arithmetic dressed up as a typed read. In Python, the same operation is a stack of object construction. The difference is not 30% — it is multiple orders of magnitude on hot paths.

The fifth, which is harder to quantify but the most important in practice, is a compiler that refuses to ship subtly wrong concurrent code. Most of the bugs that cost real money in trading systems are not algorithmic. They are aliasing bugs, lifetime bugs, ordering bugs. Rust's type system catches the entire class of these at build time. This does not mean a Rust bot has no bugs. It means the bugs it has are about logic, not about memory.

The Honest Costs

None of this is free. The borrow checker has a learning curve, and the curve is steep enough that anyone who tells you otherwise is selling something. Iteration speed during early prototyping is genuinely slower than in Python. Compile times on a laptop are not enjoyable. Refactoring across crate boundaries is painful in ways that Python or TypeScript developers do not encounter.

There is also a real risk of over-engineering. The temptation to push every concept into a generic, every dependency behind a trait, every error into a custom enum, is constant. Most of those moves are speculative abstractions that do not pay off. Writing simple, direct, sometimes ugly Rust beats writing pristine, generic Rust that takes three weeks to compile through.

And in the early phases, when the bot is mostly losing money and the work is mostly understanding the protocol, much of the speed advantage is wasted. A poorly-targeted bot in Rust loses to network latency in the same way a poorly-targeted bot in Python does. The language only starts paying for itself once the strategy is correct enough that the difference between landing in slot N and slot N+1 actually matters.

For anyone deciding right now, the practical answer is probably: prototype in whatever you know, but do the production rewrite in Rust before the strategy starts working, not after. Rewriting a working bot is much harder than building a slow one.

The Determinism Argument, One More Time

The single sentence I keep coming back to from the case studies is the one about the loop that takes the same five microseconds every time. It is easy to read it as a claim about speed. It is actually a claim about epistemology. The thing that makes a Rust loop useful is that you can know what it does. You can budget around it. You can reason about its worst case the same way you reason about its average case, because the two are close.

A Python loop, even a fast one, denies you that knowledge. The mean tells you nothing about the tail, and the tail is what kills the bot. An asyncio loop denies it differently — by introducing a scheduler whose decisions you cannot predict. A JavaScript loop denies it through V8's GC. Each denial is its own story, and each one ends with a bot that runs fine in development and stalls in production.

This is also why the language argument is not really about performance benchmarks at all. The benchmarks are evidence, but they are not the case. The case is that an MEV bot is a feedback loop where you are competing on tail latency, and tail latency is the metric the GC-based languages are worst at. Picking Rust is picking the runtime model that makes the metric you compete on a thing you can control.

What This Decision Looks Like Right Now

From where I am sitting, with a working Rust scanner and an evolving on-chain program, the language decision feels less like a choice I made and more like a choice the problem made for me. Every time I have been tempted to drop into a higher-level language for speed of prototyping — and the temptation is real, especially during the long stretches of debugging account layouts — the work eventually loops back to a place where I need to know what my code does, exactly, every iteration. Rust gives that. Nothing else I tried did.

I do not pretend the bot is profitable. The honest report is what every individual MEV developer's report seems to be: it is hard, the margins are thin, the competition is sophisticated, and most of the work is in places that do not show up in benchmarks. The language choice does not turn any of that around. What it does is keep open the possibility that the bot can become competitive at all. A Python version of the same code, by the time it had stopped being faster to write, would have closed that possibility. The benchmarks make that argument quantitatively. The borrow checker, once you have lived inside it, makes it experientially. The two converge on the same answer, which is why I keep landing on it.

Key Takeaways

• Solana MEV bots are dominated by tail latency, not average latency, and garbage-collected languages cannot offer a tight tail because the GC schedules pauses on its own timeline.
• Real benchmarks back this up: HTTP provider creation at roughly 1,100 microseconds in Python versus around 8 microseconds in Rust (Solid Quant); a migration case study reporting Python tick-to-trade latency around 12 milliseconds dropping to roughly 40 microseconds in Rust, with Python spikes up to about 80 milliseconds (DEV Community).
• Asyncio is not an escape hatch: synchronous Python frameworks posted P99 latencies in the low 30-millisecond range, while async frameworks ranged from roughly 75 to 364 milliseconds in the same benchmark (calpaterson.com).
• Rust gives up almost nothing to C++ — about 98.7% of C++ latency performance in one 2026 HFT evaluation — while eliminating the memory-error class entirely at compile time (dasroot.net).
• The decisive property is determinism: "If my loop takes 5 microseconds to run, it takes 5 microseconds every single time." That is what makes a bot's behavior something you can reason about, and reasoning is what an individual developer competing against teams has left to work with.