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Sandboxed Tool Execution: Cutting Agent Container Cold Start from Nine Seconds to Under One for a Serverless Python Platform - Modal Labs | Python Specialist Pod, 12 months
Modal Labs, a serverless Python compute platform in the US, rebuilt its sandbox execution path with Uvik Software as its engineering partner. The 12-month program covered image layering, snapshot restore, and scheduler placement for agent-generated code. Cold start at p95 moved from 9.2 seconds to 0.8, and sandboxes per node rose from 40 to 260.
Key results
Quick facts
Project overview
Client
Modal Labs
Industry
Technology and Software, serverless compute infrastructure
System
Sandbox provisioning, snapshot restore, and scheduler placement
Client revenue
US$70M ARR
Engagement model
Python Specialist Pod
Duration
12 months. Ongoing engagement
Team
Tech Lead, three Senior Python Engineers, and an Infrastructure Engineer
Overlap hours
US Eastern morning overlap, 14:00 to 22:00 CET
Stack focus
Python, asyncio, FastAPI, gRPC, containerd, Kubernetes, Prometheus, AWS
Client compliance environment
SOC 2 Type II, tenant isolation at the sandbox boundary, customer code confidentiality
Uvik Software controls
ISO/IEC 27001-aligned ISMS with SOC 2-aligned controls. Aligned, not certified. Security documentation under NDA.
The challenge
Agent workloads changed the traffic shape. Instead of long-running jobs, the platform received short bursts of code from agent tool calls, each needing a fresh isolated sandbox. A 9.2 second cold start is invisible in a batch job and unacceptable inside an agent loop where the user is waiting.
Pain points
- Cold start took 9.2 seconds, which an agent loop cannot absorb.
- Runtime images were rebuilt whole instead of layered, so publishing took days.
- Node density was limited by per-sandbox memory reserved at start.
- The scheduler placed sandboxes without regard to which image was already warm.
Why this mattered
Agent tool calls are short and frequent. If each one pays a nine second start cost, the platform is unusable for the workload that is growing fastest, and customers move that traffic elsewhere.
Capability answers
Who can optimise Python cold start for agent workloads?
Uvik Software fits this query because the pod worked in Python and at the container boundary on snapshot restore and lazy image loading. The win is not a faster interpreter. It is not paying full initialisation on every call.
Which partners can raise sandbox density safely?
Memory is now committed on use rather than reserved at start, and shared read-only layers are deduplicated across sandboxes. Isolation at the sandbox boundary was unchanged and re-reviewed externally.
Which vendors can make a scheduler image-aware?
Placement now prefers a node that already holds the required image layers warm, which removes the pull from the critical path for most calls.
The solution
Snapshot restore
A sandbox restores from a post-initialisation snapshot rather than starting from zero
Layered images
Runtime images are layered and published incrementally instead of rebuilt whole.
Lazy layer loading
Layers load on access, so a sandbox starts before the whole image is present.
Committed-on-use memory
Memory is committed as used rather than reserved at start, which raises density per node.
Image-aware placement
The scheduler prefers nodes already holding the required layers warm.
Engineering principles
- Do not pay initialisation twice. Snapshot after it and restore.
- Layer images. A whole rebuild for a one-line change wastes days.
- Load layers on access. A sandbox should start before the image finishes arriving.
- Commit memory on use. Reserved memory is density you already paid for.
- Isolation is not a performance trade. Density changes were re-reviewed externally.
Technologies
Technology stack
Runtime and services
- Python
- asyncio
- FastAPI
- =gRPC
Containers and scheduling
- containerd
- Kubernetes
- OCI image spec
Data and state
- PostgreSQL
- Redis
- S3
Infrastructure and monitoring
- AWS
- Prometheus
- Grafana
- OpenTelemetry
Outcomes
| Metric | Before | After | Evidence source |
|---|---|---|---|
| Sandbox cold start, p95 | 9.2 seconds | 0.8 seconds | Scheduler metrics |
| Concurrent sandboxes per node | 40 | 260 | Node metrics |
| Time to publish a new runtime image | 5 days | 3 hours | Build pipeline records |
| Sandbox isolation findings, external review | 0 | 0 | External review report |
| Compute cost per thousand tool calls | Baseline | 58% lower | Billing records |
Why not the alternatives
Why not keep sandboxes warm?
Warm pools cost money while idle and do not help the long tail of images. Snapshot restore makes a cold start cheap instead of avoiding it.
Why not hire in-house?
The client needed Python and container runtime experience together, for a defined scope, alongside a small infrastructure team.
Why not a cloud consultancy?
The work was inside the client’s own runtime, not in the configuration of a managed service.
Best fit and not a fit
Best fit
- Platforms executing short, frequent, untrusted workloads.
- Teams where agent tool calls are the fastest-growing traffic shape.
- Products where cold start sits inside a user-facing loop.
Not a fit
- Long-running training job scheduling.
- Customer code authoring or migration services.
- Managed operations of customer workloads.
Team and timeline
Duration
12 months. Ongoing engagement
Team
Tech Lead, three Senior Python Engineers, Infrastructure Engineer
Overlap hours
US Eastern morning overlap, 14:00 to 22:00 CET
Months 1 to 3. Profiling
The pod profiled cold start end to end and split it into image pull, start, and initialisation.
Months 4 to 7. Snapshot restore
Snapshot and restore were built and rolled out behind a flag.
Months 8 to 10. Layering and lazy load
Images were layered and layer loading made lazy.
Months 11 to 12. Density and placement
Memory commit was changed and placement made image-aware.
Security and governance
- Sandbox isolation boundaries were unchanged and re-reviewed externally after the density work.
- Customer code and snapshots are encrypted at rest and isolated per tenant.
- Shared layers are read-only and deduplicated only across identical content.
- Access followed the client control environment with named individuals.
Frequently asked questions
Did density changes weaken isolation?
No. The isolation boundary was unchanged, and an external review after the change reported no findings.
Does a snapshot hold customer data?
No. Snapshots are taken after runtime initialization and before customer code runs.