Archil
Archil mounts S3, GCS, or NFS live at /mnt/archil and adds serverless sandboxes, tool calls, and forkable disks for AI agents.
Archil's argument is that storage should be the substrate agents run on, not a bucket they pull from. The Free tier proves it on a real workload — 5 disks of 10 GB, 1,000 tool calls, 30 sandbox minutes, no card. The $10/mo Developer minimum with external mounts and 20 concurrent sandboxes is the honest entry point. The $500/mo Team floor is the first genuine jump, and it's a usage minimum, not a subscription on top.
Verified 1h ago · liveness 73/100 · cite: rightaichoice.com/tools/archil
- AI agent platforms whose agents need to write to live S3, GCS, or NFS data, not just read it
- Teams running many parallel agent attempts that need forkable, versioned state to compare results
- ML training and research groups streaming datasets from S3 to GPU nodes through a shared SSD cache
- CI/CD pipelines that want build artifacts to survive between separate exec steps
- Individuals or small teams whose context fits in a prompt or a single bucket pull
- Anyone wanting a simple cloud drive for file sharing or backup
- Teams that want checkpoints and branching on their own bucket — that's Enterprise only
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Skip Archil if your agent's context fits in a single prompt or one bucket pull and you have no shared, versioned state to manage — a plain object store plus your own sandbox is enough.
Performance storage past the included amount is metered at $0.30/GB-mo on Developer and $0.20/GB-mo on Team, so a workspace that grows quietly adds a line to every bill.
The $0 Developer tier fits solo builders and prototypes; $500/mo on Team fits production workloads that need 1 TB performance storage, 1,000 sandbox minutes, unlimited file systems, and shared access across unlimited compute nodes. Below juicefs or S3-over-EFS on raw mount cost; above that, only if you'd otherwise build checkpointing, forkable sandboxes, and per-source permissions yourself.
In short
Archil — Archil mounts S3, GCS, or NFS live at /mnt/archil and adds serverless sandboxes, tool calls, and forkable disks for AI agents. Best for AI agent platforms whose agents need to write to live S3, GCS, or NFS data, not just read it, Teams running many parallel agent attempts that need forkable, versioned state to compare results, ML training and research groups streaming datasets from S3 to GPU nodes through a shared SSD cache. Free to start; paid plans from $10/mo.
What's new in Archil
Checked 5 days agoAcross the latest 5 updates: 1 launch, 1 changelog entry and 3 news mentions.
Archil introduces Persistent Sandboxes
Archil adds traditional, forkable sandboxes with persistent disk and the ability to host network services, extending the disk versioning model to long-running agent work.
Building the last WAL on S3
Engineering post arguing a single POSIX-speaking write-ahead log on S3 can unify databases, sandboxes, CI, and analytics storage.
How to store data safely
Archil details durability math, correlated failure, erasure coding, and copysets for storing customer data directly rather than delegating to S3.
Understanding file storage
Ground-up explanation of block devices and file systems, and how Archil makes shared remote file storage fast enough for git clone.
Context that agents understand
New Archil integrations for the Vercel AI SDK, eve, Mastra, and LangChain expose a persistent, shared, compute-enabled filesystem to agents.
What people actually say about Archil — is it worth it?
We ran a structured research pass across product reviews, community discussions, and post-purchase forum threads to surface the patterns vendors won't publish themselves. Below: the recurring strengths, the hidden costs people mention most, and the cohort that consistently regrets adopting this tool.
44 mentions across 2 sources (Hacker News, Lemmy) · researched Jul 3, 2026.
Average across the 2 sources that answered — each source counts once, not each post.
- +Custom protocol delivers higher performance than NFS-based solutions.
- +Designed specifically for AI data patterns (random reads, streaming).
- +POSIX-compatible interface works with standard tools and frameworks.
- +Cloud-native deployment via Kubernetes and Docker.
- +Supports multi-cloud and hybrid storage setups.
- −Very limited independent community feedback — mostly founder posts.
- −No real-world performance benchmarks or case studies available.
- −'Contact us' pricing may be expensive for small teams.
- −Proprietary protocol could lock users into the ecosystem.
- −No data on reliability or uptime at scale.
- • Cloud infrastructure costs for running Archil clusters.
- • Potential egress fees when moving data between clouds.
Viability Score
How well maintained and how widely used is Archil? Built from what the vendor actually publishes (docs, changelog, tutorials, integrations, pricing), whether the site is live, and how much real users discuss it. How we calculate this
Last calculated: October 2026
How we score →Key Features
- Mount S3, GCS, or NFS live at /mnt/archil as a POSIX filesystem with zero copies
- Serverless tool calls run Bash, Python, or Node against persistent files on demand
- Files written by one tool call are available to the next — no sandbox to manage
- Persistent sandboxes with persistent disk and hosted network services
- Forkable, versioned disks: checkpoint before a run, branch attempts, roll back writes
- One disk per user, session, or task, mounting on demand in about 100 ms
- Shared SSD cache in front of your bucket at 10 GB/s default throughput per disk
- Cached read and write latency under 1 ms for git clone and small-file workloads
- Strongly consistent S3 API so human-facing apps read the same files agents write
- POSIX mounts on Linux, macOS, containers, and Kubernetes
- TypeScript SDK with createDiskTools exposes the filesystem to agents as tool calls
- Python SDK for filesystem access from agent code
- Connect from outside Archil with external mounts and mount tokens
- Custom networking policy for sandboxes on paid plans
- Encrypted at rest and in transit; SOC 2 Type II and HIPAA on Enterprise
About Archil
Archil is a serverless runtime for AI agents: a durable POSIX filesystem that mounts your S3, GCS, or NFS data live at /mnt/archil, with serverless tool calls and persistent sandboxes running beside it. Instead of copying buckets into ephemeral compute, agents read and write files where they already sit — no ETL, no sync job. It's built for agent platforms, ML research and training teams, CI/CD pipelines, and analytics groups that keep hitting the same wall: context in one place, compute in another. The pieces are specific. A disk per user, session, or task mounts in about 100 ms, and each disk is versioned — checkpoint before a run, branch parallel attempts, roll back the write that broke things. Serverless tool calls execute Bash, Python, or Node against that disk on demand, and files written by one call are there for the next. Persistent sandboxes handle the long-running cases (preview servers, hosted network services), and the new Persistent Sandboxes release extends disk versioning to that long-lived work. Performance is where the pitch gets concrete: a shared SSD cache in front of your bucket feeds every node, rated at 10 GB/s default throughput per disk, with sub-millisecond cached reads and writes so git clone and npm install run at block-storage speed rather than NFS speed. Hot data stays on SSD, cold data stays in your own bucket at bucket prices. Your servers connect over POSIX and your applications over a strongly consistent S3 API, so humans and agents work on the same files. Agent integrations exist for the Vercel AI SDK, LangChain, Mastra, and eve via @archildata/ai-sdk. It is more than a mount — JuiceFS or S3 over EFS move files but leave sandboxing, checkpointing, and cross-node sharing to you.
Behind the Verdict
Most agent infrastructure fails at the seam between where data lives and where code runs. Archil's bet is to delete the seam: mount the bucket as a filesystem, run the code next to it, keep the files after the process exits. We'd reach for this the moment you're writing copy-down/copy-back glue around S3 for every agent run. Pick Archil when agents need to write, not just read. The interesting part isn't POSIX on object storage — it's that each disk is versioned, so a bad write from run 40 is a rollback instead of a debugging session. Fork a disk, let three agents attempt the same task in parallel, keep the winner. That pattern is hard to bolt on after the fact. The integrations landed in July — Vercel AI SDK, LangChain, Mastra, eve — make it a wiring exercise rather than a build: createDiskTools hands the filesystem to an agent as tool calls. If you're on one of those frameworks, the trial costs an afternoon. Where it bites: the pricing is a usage minimum, not a flat fee, and the meter is granular — $0.055 per vCPU-hour, $0.020 per GiB-hour, $0.05 per 1,000 tool calls, $0.07/GB egress. Sandbox-heavy workloads can push a Developer bill past $10 quickly, and the Team floor at $500/mo is a real commitment for a team still proving the pattern. Free's hard caps (10 GB per disk, 5 concurrent sandboxes) are for evaluation, not production. Checkpoints and branching for your own bucket are Enterprise only, so BYOB users on Developer or Team lose the feature they came for. Long-running exec steps are bounded — up to 5 minutes per step — so plan around it. The closest alternative is JuiceFS: solid POSIX-on-object-storage, but you supply the compute, the sandboxing, and the versioning. If you already have a frozen sandbox layer you like (E2B, Daytona, Modal, Vercel Sandbox
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Real-world workflow fit
Concrete scenarios for the personas Archil actually fits — and what changes day-one when you adopt it.
You mount the production S3 bucket at /mnt/archil, compose a read-only skills layer with read/write customer data, and call createDiskTools so the agent gets the filesystem as tool calls.
Outcome: The agent reads and writes live files with zero copies, and you keep per-source permissions instead of flattening everything into one credential.
Before each agent run you checkpoint the disk, fork the three approaches you want to try in parallel, and promote the branch that passed review.
Outcome: A bad write gets rolled back instead of corrupting the workspace, and parallel attempts don't collide because each fork has its own line of history.
You run Bash, Python, and Node steps inside the 2 vCPU / 4 GB serverless sandbox against mounted data, letting the sandbox sit idle between steps.
Outcome: Only active compute is billed at $0.18/hr on Team with idle time at $0, and artifacts land in the strongly consistent S3 API with no sandbox in the loop.
Use Cases
- Give agents in-place read and write access to S3, GCS, or NFS data without copying it
- Run Bash, Python, or Node code against mounted enterprise context in a serverless sandbox
- Compose per-customer, per-agent, and per-business context layers with separate permissions
- Checkpoint and fork agent runs, then keep the attempt that worked
- Serve agent-generated artifacts to a human app through a strongly consistent S3 API
- Share one live filesystem across many agents and unlimited compute nodes
- Keep cold data in your own bucket while hot data runs on Archil performance storage
- Operate agent workloads under HIPAA and SOC 2 with BAA and DPA available
Models Under the Hood
as of 2026-10-18
Limitations
- Archil is infrastructure — a serverless POSIX filesystem, sandbox, and disk-versioning layer for AI agents — not an AI model or consumer app; LLM inference itself isn't included.
- Paid plans carry a monthly minimum (Developer $10, Team $500) with usage counting toward it, and usage beyond included amounts is metered, with external mounts and outbound transfer billed separately on paid tiers.
- The free tier is capped at 5 file systems, 10 GB per disk, 1,000 tool calls/month, and 30 minutes of sandboxes with up to 5 concurrent sandboxes, and external mounts are excluded.
- It assumes data already lives in S3, GCS, or NFS and that you're comfortable with filesystem, container, and agent-SDK operations.
as of 2026-10-13
Verification history
We have re-verified Archil 10 times since . Each pass re-reads the vendor's own pages and re-checks every listed field against that evidence; passes where nothing had changed are marked as such.
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
- — re-verified summary, description, our verdict, our analysis, pricing model, pricing tiers, features, integrations, who it suits, who should skip it
Showing the 6 most recent of 10 verification passes.
Free to cite with attribution — this page re-verifies continuously.
12-month cost
Project the real annual outlay, including the implied monthly cost when only an annual tier is published.
Vendor list price only. Add-on usage, seat overages, and contract minimums are surfaced under Hidden costs & gotchas.
Plans compared
For each published Archil tier: who it actually fits, and what it adds vs. the previous tier. Cross-reference the cost calculator above for projected annual outlay.
Free
$0/month
Developer
$10/month minimum
Ideal for
A developer or small team proving that an agent can read and write live S3, GCS, or NFS data before committing budget.
What this tier adds
Starting tier — $0/mo with 10 GB performance storage, 30 sandbox minutes, up to 5 file systems, and no credit card required.
Team
$500/month minimum
Ideal for
A production agent platform or software factory running many stateful agents that need shared, versioned filesystem state.
What this tier adds
Adds 1 TB performance storage and 1,000 sandbox minutes included, unlimited file systems, shared access across unlimited compute nodes, plus BAA and DPA availability.
Enterprise
Custom
Ideal for
Regulated or large-scale deployments that need BYOC or on-premises placement, SSO/SCIM, and a contractual availability SLA.
What this tier adds
Adds unlimited storage with volume pricing, BYOC or on-premises deployment, 24/7 dedicated support, SSO and SCIM provisioning, and an availability SLA.
Where the pricing makes sense
The company stage and team size where Archil's pricing actually pencils out — and where peers do it cheaper.
The $0 Developer tier fits solo builders and prototypes; $500/mo on Team fits production workloads that need 1 TB performance storage, 1,000 sandbox minutes, unlimited file systems, and shared access across unlimited compute nodes. Below juicefs or S3-over-EFS on raw mount cost; above that, only if you'd otherwise build checkpointing, forkable sandboxes, and per-source permissions yourself.
Setup time & first value
How long it actually takes to get something useful out of Archil — broken out by persona, not the marketing-page minute.
Developer-tier proof of concept: minutes — create a disk, install @archildata/ai-sdk or the Python SDK, pass createDiskTools to your agent, and you're reading mounted files. Production rollout on Team takes longer, mostly spent mapping per-source permissions and deciding which layers are read-only versus read/write.
Switching to or from Archil
How to bring data in from common predecessors and how to get it back out — written for the switcher, not the buyer.
- →From raw S3 access in agent code: mount the bucket at /mnt/archil and let Bash, Python, or Node operate on it as a filesystem instead of SDK calls.
- →From a self-managed JuiceFS or S3-over-EFS mount: keep the same data and add Archil's sandbox compute, checkpoints, and forked branches on top.
- →From locally copied bucket snapshots: drop the sync job and mount the source of truth live with per-source permissions inherited.
- →From a framework-specific agent memory layer: replace it with composed context mounts plus a versioned disk so state survives the framework.
- ↗To a plain mount layer (JuiceFS, S3 over EFS): keep the files, lose the forkable sandboxes, checkpoints, and strongly consistent S3 artifact API.
- ↗To a general cloud sandbox provider: keep the compute, rebuild the context layer, per-source permissions, and versioned disks yourself.
- ↗To your own container platform: possible if you accept running the POSIX mount, sandbox lifecycle, and disk versioning as your own operational burden.
Integrations
Resources & Guides
Tutorials & Learning
YouTube returned 6 videos for “Archil”, and we withheld 6: 6 could not be judged, because “Archil” is a single word that other videos use for other things. We are showing none, because we could not prove any of them are about Archil.
Official links
Tools that pair well with Archil
Common stack mates teams adopt alongside Archil, with the specific reason each pairing earns its keep.
Mem0
Mem0 is an AI memory layer that gives agents and apps persistent context across sessions
Granica AI
Granica is an efficiency layer for enterprise AI that cuts data lake storage costs and keeps long-running agent state alive.
Distill
Distill trims LLM agent context with deterministic semantic dedup and compression in ~12ms — no LLM calls, same input every time.
Featured Head-to-Head Comparisons
Archil vs Spider Cloud
Choose Archil if you need a high-performance file system for AI training on large datasets in a self-managed cloud/HPC environment. Choose Spider Cloud if you want a fast, low-cost scraping API to feed web data into AI agents or RAG pipelines, especially with recent Browser AI commands and data connectors.
Archil vs Screenplayiq
Archil and ScreenplayIQ serve completely distinct markets — Archil is an infrastructure tool for AI/ML teams needing high-performance data access, while ScreenplayIQ is a niche creative analytics tool for film professionals. Choose Archil if you're an engineer managing large-scale AI training data; choose ScreenplayIQ if you're a screenwriter or producer seeking data-driven feedback on a feature film script.
Archil vs Temporal Ai
Archil and Temporal AI solve different problems. Choose Archil if your primary bottleneck is fast, scalable data access for AI training – it's a high-performance file system for petabyte-scale datasets. Choose Temporal AI if you need to orchestrate durable, fault-tolerant workflows and AI agents that survive crashes, with built-in retries, human-in-the-loop, and full execution visibility. Temporal's open-source freemium model lowers upfront cost, while Archil's contact-sales pricing suits enterprise infrastructure.
Alternatives to Archil
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Mem0 is an AI memory layer that gives agents and apps persistent context across sessions
Granica AI
Granica is an efficiency layer for enterprise AI that cuts data lake storage costs and keeps long-running agent state alive.
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