Here is a bet that will save you a lot of arguing: pick your durable execution engine on what you have to operate, not on what you have to write. T...
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The operational simplicity argument is convincing, but I wonder how the tradeoff changes once you run Restate at serious scale. Does the single binary still stay simpler when you need multi region recovery, upgrades, rebalancing, and failure testing? That would be an interesting follow up comparison.
Good question and I would not claim the article answers it. The architecture is built for this, segmented log for on the fly reconfiguration, quorum based replication that tolerates cross region latency, object store snapshots so nodes hold little state and recover quickly. But architecture being designed for something and a team having done it under pressure are different claims, and I am only comfortable making the first one.
Second person to ask about scale in this thread, so it is going on the list as a follow up.
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The observation that Temporal turns your app into two distinct services — your own workers plus the cluster you now have to operate — is the part teams consistently underestimate during prototyping, because the happy-path demo never surfaces it. I've watched the same thing happen with static analysis infrastructure: engineers pick the tool with the cleanest rule-authoring API, then a year later they're maintaining a separate lint server deployment and wishing they'd weighted operational surface higher from the start. The Elasticsearch correction is also worth highlighting — "not strictly required, but you'll probably want it eventually" is exactly the kind of nuance that gets collapsed into "always required" in architecture decision memos, and then someone provisions it on day one for a workload that doesn't need it for two years.
Good parallel, and you put your finger on why it keeps happening. A prototype tests the programming model and nothing else, so operational cost is invisible exactly when the decision gets made.
The Elasticsearch bit is the one I would underline too. The problem is not the fact being wrong, it is that architecture memos have no way to express not yet. The qualifier gets dropped on the second read, the dependency stays, and two years later it is just part of the baseline.
A really useful next article could compare Restate and Temporal using the exact same real world workload and then walk through what happens under pressure. For example, deploy both from scratch, kill a node during active workflows, recover from failures, scale traffic up, perform an upgrade, and then debug a workflow that failed halfway through. It would also be interesting to compare how much manual work each case requires and how easy it is to understand what happened. A practical failure scenario like this would probably reveal much more than another feature by feature comparison.
Great spec, and the debug a half failed workflow part is the one I would build the whole thing around. That is where the models actually diverge and where feature tables tell you nothing.
Doing it fairly takes real setup time, same workload and same failure injection on both, otherwise it is just a benchmark with a conclusion picked in advance. But yes, this is the article worth writing.
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One thing I am curious about is the debugging experience. When a workflow fails and gets replayed, which model makes it easier to understand what actually happened six hours later? Operational simplicity is great, but observability can become the hidden cost in durable systems.
Good question and the article does not answer it, which in hindsight is a gap. Short version, I think Temporal wins this one. The determinism constraint I complained about is what makes the event history so legible later, an ordered command log you can step through and replay locally against a worker. You pay for that upfront and collect it during an incident.
Restate has tooling for this too, but fewer people have publicly debugged hard failures with it, and that gap shows up exactly when you need it.
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Nice job! Really enjoyed this comparison. Restate seems like a great example of how simpler infrastructure can sometimes be the better engineering choice. I would be curious to see how the comparison changes at a much larger scale.
Thanks, glad you enjoyed it. Scale is the part I deliberately left out here, mostly because doing it properly means a different article rather than another section in this one. It deserves its own write up. Putting it on the list.
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The API plus worker split is an important detail that many Temporal comparisons barely mention. It can look simple at the code level while adding quite a bit of operational complexity behind the scenes.
Another interesting difference is the failure model. With a single binary approach, there are fewer moving parts to keep healthy, but the system boundary is also larger because one process owns more responsibilities. With a clustered architecture, you get stronger separation and scalability options, but you also inherit more coordination points where failures, retries, and operational decisions need to be handled. The tradeoff is not only about performance or features, but also about how much infrastructure complexity your team wants to own.
This is the best comment on the thread, thanks for it. One thing I would push back on gently. The single binary does not necessarily mean one process owning everything. In a distributed Restate deployment the same binary runs with different roles, worker, log server, metadata server, ingress, so you still get separation of concerns. What you avoid is separation of artifacts. One thing to build, one thing to version, one thing to upgrade, and the topology lives in config rather than in five deployment pipelines. That is a real difference from a cluster of distinct services, but it is a smaller one than the phrase single binary suggests.
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Great article and I like the simplicity argument. One thing I would love to see is more focus on failure scenarios. In production the number of components is not always the biggest problem. Recovery time, state migration and handling partial failures usually become the real challenges. A comparison under heavy load and during outages would be very interesting.
Thanks. I would frame it slightly differently though. Component count is not the goal, it is the input. Partial failures happen at boundaries, so the number of components is roughly the number of places where a partial failure can occur. Recovery time and state migration are the symptoms, and the topology is what sets the ceiling on how bad they get.
That said, you are right that the article stops at the diagram. Behaviour under load and during an outage is the next layer down
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Great comparison. I really liked the focus on operational complexity instead of just comparing feature lists. That part often gets ignored when choosing infrastructure.
Thanks. I think ops complexity gets skipped because it is hard to put in a table. Nobody wants to write a row that says this one needs a Postgres and a search cluster and a separate worker deployable. But that is the row you live with.
Thanks for sharing!
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“Battle-tested at the scale you're actually at” really resonates with me. It's easy to design for hypothetical future scale and end up paying the operational cost long before you need it. Choosing the system you actually want to operate is often more important than choosing the one with the most impressive architecture.
Thanks, glad that line landed.
Glad it did. The single-binary framing makes the operational trade-off concrete without pretending the clustered option is always wrong. I’m especially interested in where teams first outgrow the lighter deployment model in practice.
Interesting comparison. I have seen Temporal recommended almost automatically for durable workflows, but Restate looks much more practical for smaller teams. Have you used both in production?
Yes, both. Different projects, different scale, so treat it as two data points rather than a head to head. The short version is that the Temporal pain was operational and the Restate pain was ecosystem, fewer people to ask, fewer answers already on the internet when something is odd. That tradeoff is real and I probably underweighted it in the article.
Thanks for the great article and the thoughtful comparison. I really liked the way you framed the tradeoffs, especially the operational simplicity of Restate versus the more mature ecosystem around Temporal. The real world perspective was very helpful!
Thanks, appreciate you reading it. The ecosystem side is the tradeoff I keep going back and forth on, so glad it came across as a real tension rather than a verdict.
I also think Restate has an interesting approach for teams that want durable workflows without adding too much operational complexity. It feels like a nice balance between simplicity and reliability.
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This was a really useful breakdown. The idea of starting with the lighter option and only adding complexity when you can name exactly why you need it makes a lot of sense.
Glad that part stuck.
I would add one caveat I probably should have put in the article itself. Defaulting to the lighter option is not free either. The lighter tool is usually the younger one, with fewer people who have run it in anger and a smaller pool of engineers who already know it.
That is a real cost, just not one that shows up on an architecture diagram. So the rule still holds, you just pay somewhere else.
Exactly. Lighter tools don’t remove complexity, they move it into team knowledge and operational experience. Thanks for the great article!Really enjoyed the insights and the practical perspective.
Thank you for reading!
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Thanks for sharing this great comparison. I really like the idea of looking at the trade offs between a lightweight engine and a more established workflow platform. One thing I would add is that the best choice probably depends a lot on the workload and the operational requirements. A lightweight engine can be a perfect fit for many event driven systems and teams that want simplicity, but large enterprise workflows with multi region deployments and strict SLAs may have different priorities. A use case based comparison with real production scenarios would make this discussion even more valuable. Thanks again for writing this, it is a very interesting topic and I hope to see more deep dives like this in the future.
Thanks. Agreed on enterprise workloads being a different conversation, though I would resist landing on it depends, since that is where most comparisons stop and it does not help anyone choose. My version is that the heavier option should have to justify itself. Multi region and hard SLAs justify it easily, most checkout flows do not.
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I'm drawn to this post because I keep researching and thinking in a cousin pattern to what i see in the post - "what is the smallest reuseable primitive that captures the actual invariant?" I like this part -
"The heavier option should have to justify itself."
Finding the smallest reuseable thing that solves the real problem excites me. 🙂
Thanks, and that framing is better than mine. Smallest primitive that captures the actual invariant is exactly the question, I just came at it from the ops side.
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The operational burden framing hits. We ran into this exact fork building an AI video pipeline — multistep async jobs where journal and replay semantics matter but infra overhead is real at small team scale.
Went with Restate. The single binary thing is honestly underrated: dev loop alone, running the same binary locally that ships to prod, killed a whole class of issues we had with Temporal workers pointing at a shared cluster.
One thing for AI workloads: latency compounds. When you're chaining 15 model calls with tool use in between, task queue polling adds noticeable per hop delay that stacks up fast. Restate's invocation model stayed tighter.
Does the single binary hold once you need to scale the history service independently? That's the Temporal argument I still find legit.
Hello 👋
I think the biggest thing about your last paragraph, is if you're small-scale and need durable workflows, you use Restate, sure, but what's the cost of migration when you hit the scale you need to switch to Temporal? I somehow doubt they've built a migration tool to just 'hot-swap' the from Reshape to Temporal and that's the friction that causes vendor locking. Once you hit the scale you need to switch, you're too invested to do it effectively, without risking things breaking.
Fair hit, and no, there is no hot swap. A sleeping workflow is live state in a proprietary journal, so the only realistic move is a drain rather than a migration. Both engines up, new executions to the new one, wait out the old ones. Painful but it is a pattern people run.
Where I would push back is that this cuts both ways. Picking Temporal early does not escape lock in, it just chooses the vendor and makes you pay the operational premium for years against a switch that might never happen.
It does yes, but that's a standards problem, neither has a seamless migration system, nor do they have any migration system at all. Essentially they operate in the same space, with slightly different boilerplate, but they functionally do the same task at the same places (atleast from a buyer's perspective), the vendor lock-in issue is exactly why Samsung and Apple both pushed hard for the 'seamless transfer' experience, because if it wasnt for that, you'd stick to iPhone, or stick to Samsung and never even consider the other, because it's too much of a hassle to switch and you dont know if you'll get the same functionality, let alone how long it'll take. In a space where durability is the selling point, you'd really think a 'durable migration' system would just make sense? It allows you to pick Restate while small-scale and migrate to Temporal when you scale up, then scale back to Restate once userbase declines and it's not viable anymore, or to an alternative platform that just 'works better'.
Not saying I did it, or that it'll always work, but 1 of the things I was working on for it, is a migration tool, that converts existing temporal codebases over, so there's no friction and it's easier for side-by-side evaluation to see whether it makes a difference. Also added a codebase analyzer that finds patterns that could do with durable workflows and auto-implements it, so it's easier to manage. Though given it's Rust based and uses unmanaged memory slabs, I want to still see if I cant implement a hostile takeover system, so a process never actually dies, it's a seamless handover, though that'll likely have to come later.
The converter sounds useful, but I think it solves the easier half. Code you can translate. Executions you cannot. A workflow asleep for three weeks is a suspended computation with timers and pending promises, not source, so you still end up draining.
That is where the phone comparison loses me too. Contacts and photos are data at rest. This is state mid computation, more like live migrating a process between VMs with different instruction sets. The standard is missing because the models differ semantically, deterministic event replay on one side, journaled context calls on the other, and the incentives only favour whoever is smaller.
And that's the problem, if you hot-swap, you essentially need a graceful handover, which would need modifications on the providers' ends, which I doubt they'd do, and that would still require a reboot. It's technically possible with memory marshalling, but the handover would need to be instantaneous, else there's a risk of corruption or incomplete handovers
The instantaneous part is what makes me think memory is the wrong level. Marshalling memory means any gap is a corruption window and both vendors have to cooperate at runtime.
Replay does not have that shape. Durable execution already reconstructs position from a journal every time a worker dies, no memory involved. So the migration primitive is journal interchange, not memory handover. Stop accepting, seal, fenced lease, replay on the other side. Sequential and idempotent, safe to fail halfway. It is how distributed databases move a live partition, and nothing in it is atomic.
One thing I’d add is that operational complexity has a compounding cost. A single extra dependency may not matter during development, but across upgrades, incident response, backups, and onboarding, those small pieces of infrastructure start affecting engineering velocity. That makes “what will we have to operate?” a surprisingly important question when evaluating durable execution.
Any chance you'd be willing to test out mine once it's done? Short and sweet version, It's zero-allocation, doesnt use json and has O(1) resume latency, regardless of how long the task ran. Still needs a bit of polishing, but it should be done by tomorrow (hopefully).
Yeah, send it over. I can give it an evening rather than a real evaluation, but happy to kick the tires.
Most curious about O(1) resume. Replay based engines scale with history length, so I assume you are snapshotting state instead. Does resume cost then track state size rather than run duration? And what is the codec if not JSON.
Merkle root driven, custom format, NDA, which uses a triples structure, so no serialization needed. I initially designed it to be a better alternative to JSON for MCP servers, but it's proving effective at anything to do with JSON.
Thank you very much, I'll send you a link once I public it on Git
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