简体中文
English
Website review
This review may include AI-generated content and is not individually verified. Domain, certificate and other facts come from the observed data on this page.
What is Rivet?
Rivet is an open-source, self-hostable orchestrator for agentic workloads. Its core idea is the Actor: a durable process that an agent, workflow, or sandbox lives inside. Instead of giving every task its own container or VM, Rivet packs many Actors into worker processes you already run, which is how it claims very high workload density (its page cites 2,829× more workloads per server, 72 KB per Actor, and 12 ms cold starts). You can try it via Rivet.
What it actually runs
Rivet groups agent needs into a few workload types:
- Actors — a durable process per agent that can survive restarts and resume where it left off.
- Workflows — multi-step operations that replay rather than restarting from scratch.
- Sandboxes — a filesystem, shell, and network for running code you did not write.
- Dynamic Apps — a per-user backend deployed at the moment it is generated.
Who it is for
- Coding-agent builders who need long-running tasks to survive crashes and restarts.
- Teams building agent app builders, where each generated app needs its own backend.
- Enterprises that want agents running inside their own VPC so data does not leave their cloud.
- Developers building personal or realtime agents that hold memory and sync state across users.
How to decide
If your agents are short-lived and stateless, a plain job queue may be enough. Rivet's value shows up when you need durability, per-user isolation, and high density at the same time. A practical next step is to start with one workload type — say, a Workflow that must resume after a restart — and measure whether the Actor model removes the container-per-task overhead you currently pay.
How do Rivet Actors differ from containers or serverless functions for running AI agents?
Rivet Actors are durable, stateful processes that live inside a worker you already run, rather than each getting its own container or VM. Rivet describes them as the unit that gives "every agent a durable process to live in," with figures like 12 ms cold starts and roughly 72 KB of memory per running Actor. The practical consequence: where a server might host hundreds of pods, Rivet claims it can host hundreds of thousands of Actors on the same machine.
How the three models differ
| Container / VM per agent | Serverless function | Rivet Actor | |
|---|---|---|---|
| Isolation unit | Whole OS process or machine | Ephemeral invocation | Lightweight process inside a shared worker |
| State | You persist it yourself | Stateless by default | Durable, built in |
| Start cost | Seconds to minutes | Cold-start dependent | ~12 ms per Rivet's figures |
| Density | Hundreds per server | Varies | Hundreds of thousands per server |
| Long-running agent loops | Natural fit, but heavy | Poor fit — timeouts and no memory between calls | Natural fit |
Why this matters for AI agents specifically
Agents are awkward for both older models. They run long, they pause waiting on tools or humans, and they need to remember what happened. A serverless function tends to lose context between invocations and can time out mid-task; a container preserves state but costs you a VM's worth of overhead per agent, which gets expensive fast when you have thousands of users each wanting their own agent.
Rivet's page frames the Actor as the answer to "whatever the workload, there's an Actor for it," covering four shapes: plain Actors for durable agent processes, Workflows that replay multi-step operations instead of restarting, Sandboxes giving a filesystem/shell/network to code you didn't write, and Dynamic Apps that spin up a per-user backend the moment it's generated. That last one is the clearest illustration of the density argument — a backend per user only makes sense if each backend is cheap enough to create on demand.
A concrete decision path
If you're building a personal assistant that wakes on a schedule, holds memories, and survives restarts, the container-per-user approach will price you out and the serverless approach will forget things. If you're running a single batch job that finishes in 30 seconds, none of this matters and a function is fine. The middle ground — coding agents that need to resume after a restart, as Rivet's example shows with a test-fixing agent that "resumed after 2 restarts" — is where the Actor model earns its keep.
Trade-off to weigh
Embedding many Actors in one worker is efficient but changes your failure and isolation story: you're sharing a process, so the operational model is closer to an actor runtime than to Kubernetes. Rivet is open-source and self-hostable, which matters if agents touch your own data — one of its examples shows a company-specific agent running in your VPC with "no data leaves your cloud." If you need hard per-tenant isolation for compliance, containers still have the clearer story.
A reasonable next step is to prototype one long-running agent as an Actor and measure two things yourself: memory per idle agent, and how often it resumes correctly after a forced restart. Those numbers will tell you faster than any benchmark whether the density claim translates to your workload.
Can I self-host Rivet or is it only available as a managed cloud service?
Rivet is open-source and self-hostable, so you are not limited to a managed cloud service. The homepage explicitly describes it as "Open-source and self-hostable," and the enterprise example shows an agent running inside "Your VPC · no data leaves your cloud," which is the self-hosted deployment pattern.
What self-hosting means here
Rivet is an orchestrator for agentic workloads: agents, workflows, and sandboxes run as durable Actors. The architecture matters for self-hosting decisions — an Actor lives inside a worker process you already run rather than in its own container or VM. That is the basis for the density claim on the page (a server that held hundreds of pods holding hundreds of thousands of Actors) and the low per-Actor memory figure it cites. For a self-hoster, the practical implication is that you are adding a runtime layer to infrastructure you control, not provisioning a separate cluster of per-agent containers.
Who should pick which
| Situation | Better fit |
|---|---|
| Data must stay in your own VPC or cloud account | Self-hosted |
| You want to inspect, modify, or extend the orchestrator | Self-hosted (open source) |
| Small team without ops capacity | Managed cloud |
| Fastest path to a prototype | Managed cloud |
| Regulated or internal-only workloads | Self-hosted |
A concrete scenario
Suppose you are building a support-triage agent that reads tickets, calls an internal Postgres database, and issues refunds through an orders API. If company policy forbids customer data leaving your cloud, self-hosting is the deciding factor: you run the orchestrator inside your own environment and keep those three systems connected there. If instead you are a two-person team validating the idea, the managed route removes the operational burden while you learn whether the agent is useful at all.
Next step
Check the pricing page for what the managed option includes versus what you would operate yourself: Rivet. Then decide based on one question — does your data need to stay inside infrastructure you control? If yes, plan for self-hosting; if no, compare the managed tier against your own ops time.
How does Rivet handle long-running workflows that need to survive crashes or restarts?
Rivet treats a long-running workflow as a durable Actor: a process that keeps its state and can be resumed after a crash or restart rather than restarting from scratch. The page describes workflows as "multi-step operations that replay instead of starting over," and shows an example agent that resumed after two restarts. So durability here is about replaying recorded steps and continuing, not about keeping a single process alive forever.
What that means in practice
- Each workflow is backed by an Actor with its own durable state, so a failure in the host process does not erase the workflow's progress.
- On restart, the workflow replays its history and picks up at the last completed step, which is what the "resumed after 2 restarts" example illustrates.
- Because Actors are described as living inside worker processes you already run, recovery is a matter of rescheduling the Actor rather than recreating a dedicated container or VM.
- The page cites fast cold starts and a small per-Actor memory footprint, which matters for restart-heavy workflows: bringing many Actors back at once is cheaper than restarting many containers.
Where the trade-off sits
Replay-based durability assumes your steps are deterministic or that side effects are recorded and not repeated. If a step sends an email or charges a card, you need idempotency or explicit state checks so replay does not duplicate the action. Long-running workflows also accumulate history, so you should think about how much state each Actor retains and how often you checkpoint.
A concrete scenario
A support-triage workflow connects a helpdesk, an orders API and Postgres, then issues a refund. If the worker crashes mid-run, the Actor replays the completed steps and continues at the refund. To make that safe, mark the refund step idempotent and record its result before moving on.
Next step
List each step in your workflow and mark whether it is safe to replay. For any step that is not, add an idempotency key or a state check before you rely on automatic recovery. If you want to compare approaches, Temporal is a well-known durable-execution engine, and Cloudflare documents Durable Objects for stateful coordination. Rivet's own details and pricing are at Rivet.
What are Rivet Sandboxes used for when running code generated by an AI agent?
Rivet Sandboxes give AI-generated code a place to actually run: a filesystem, a shell, and a network, provided as a durable Actor. The point is isolation with continuity. Code an agent writes is untrusted and unpredictable, so you don't want it loose on your host, but you also don't want it thrown away when the process restarts.
Concretely, that combination supports:
- Executing generated code safely. The sandbox supplies the shell and filesystem the code expects, separated from your own environment.
- Iterating on failures. Because the sandbox is a durable Actor, an agent can run code, read the error, edit files, and re-run without rebuilding the environment from scratch.
- Keeping state across restarts. The work in progress — files, installed dependencies, partial output — persists rather than resetting.
- Handing over something inspectable. A developer can look at the files and commands an agent produced, not just a summary of them.
The trade-off is the usual one for isolated execution: you gain safety and reproducibility, and you take on the cost of provisioning and managing that environment. Rivet's density claim — Actors living inside a worker process rather than each getting a container or VM — is aimed squarely at that cost, since a sandbox per agent task gets expensive fast if every one needs its own container.
A practical way to decide: if your agent only produces text or a diff you apply yourself, you don't need a sandbox. If it runs tests, installs packages, or executes code you haven't reviewed, you do — and the question becomes whether your sandbox preserves state between runs. That's the difference between an agent that converges on a working result and one that restarts from zero each attempt.
Next step: check whether Rivet's sandbox fits your existing runtime, since the density argument assumes you already run worker processes to host Actors. Start with Rivet and its sandbox documentation before comparing alternatives.
How does Rivet's pricing work as my number of Actors scales?
Rivet's page does not publish a pricing model tied to Actor count. The only concrete signal is a link to its pricing page, Rivet, so check there for the current structure before you plan a budget. What the page does tell you is why Actor count may matter less than you expect: an Actor runs inside a worker process you already operate rather than in its own container or VM, and the page cites roughly 72 KB of memory per running Actor and 12 ms cold starts. If that holds for your workload, the cost driver is closer to how many worker machines you run than to how many Actors you define.
What to verify on the pricing page
- The unit of billing. Is it per server, per vCPU, per Actor, per invocation, or per seat? This single answer changes your model more than any other.
- Self-hosting terms. The page describes Rivet as open-source and self-hostable, so your cost may be infrastructure plus operations rather than a subscription. Confirm what the hosted option includes that self-hosting does not.
- Idle and sleeping Actors. Actors are durable and can resume after restarts, so ask whether sleeping Actors are billed and what wakes them.
- Sandbox and workflow usage. Sandboxes and multi-step workflows may consume resources differently from a simple Actor, so check whether they are priced separately.
A practical way to decide
If your Actors are numerous but mostly idle, per-Actor pricing would be punishing and per-server pricing would favour you. If your Actors are few but each does heavy compute, the reverse is true. Ask the vendor which of these two shapes your workload resembles, then compare that answer against your own traffic.
As a next step, take your ten busiest Actors, note their peak concurrency and idle time, and bring those numbers to the pricing page or a sales conversation. That turns a vague scaling question into a specific quote.
Cloudflare
Google Workspace
User reviews (0)