Modal (Modal Labs) is a New York-based, Python-native serverless cloud for AI workloads. It offers on-demand GPU/CPU compute, multi-node clusters with RDMA, managed LLM inference endpoints, and fast-booting sandboxes, including full VM sandboxes, which Modal says scale to 1M concurrent. It sells execution infrastructure, not RL environments. AI labs, agent companies and RL-data vendors use it to run RL rollouts, post-training and large fleets of parallel sandboxed environments. It integrates with Anthropic's Claude Managed Agents and Claude Science, Cognition's Devin Outposts and the OpenAI Agents SDK, and ships an open-source RL/SFT library (Modal Dojo).
Announced at Modal's Runtime conference in San Francisco. VM Sandboxes give agents a full Linux VM, with Docker-in-Docker, databases and graphical environments; Linear, Legora and Snorkel are named as users. Modal Clusters (GA) provide multi-node gang scheduling with RDMA up to 6.4 Tbps; Modal says Decagon runs post-training, 1X pre-training and Runway inference on them. Sticky Sessions (GA) send a client to the same container across requests. Endpoint Candidates (private beta) mirror production traffic to test alternative serving setups. The roundup lists Sandbox Sidecars as GA, but Modal's dedicated Sidecars post from the same day says beta.
Why it matters: Docker-capable VM sandboxes and multi-node RL training are now generally available. Snorkel, an RL-environment and data vendor, is named as running millions of agent simulations a month on Modal. All customer names are vendor claims.
Sidecars are isolated containers on the same host as a Sandbox. They can inject credentials, proxy traffic, redact data and monitor the agent. Modal reports a p50 latency of 0.58 ms per call between a Sandbox and its Sidecar, against 2.6 ms between separate Sandboxes. Ramp is named as running its own egress proxy for its coding agent. This post calls Sidecars beta.
Why it matters: Keeps credentials and harness logic away from code the agent generates, which matters for RL and agent environments that call outside services.
A single-source report that the round is nearly closed. The valuation would be more than 3x the $4.65B Series C from May 2026. TechCrunch also gives about 150 employees and names Cognition, Suno, Ramp and Substack as customers.
Why it matters: A strong sign of scale and staying power for a vendor that runs RL rollouts and sandboxes. Modal has not confirmed it.
A collaboration between 'inference researchers at Modal' and CMU database researchers, written up by Charles Frye and Shreya Shankar. Modal reports more than 1B tokens per minute per H100 and 1.84x the speed of vLLM (geometric mean across tasks on its AI-SQL benchmark). Code is at github.com/fsdatalab/quail.
Why it matters: Shows Modal publishing inference research with academic partners, beyond its infrastructure work.
Covers DFlash speculative decoding, TP4 tensor parallelism, FP8 KV cache and NVFP4 quantization, HiCache, and load-aware routing. Modal reports 2.8x per-user speed and 5.6x throughput. It says it serves hundreds of billions of tokens a day, including through OpenRouter and Vercel AI Gateway.
Why it matters: Modal is now a large-scale LLM inference provider, not only a place to run compute.
Endpoints can be filtered and tagged by authentication status. Deploying an unauthenticated URL now writes an audit-log event, and a workspace setting can block web endpoints from being deployed without proxy auth. RBAC user groups (with SCIM sync) and SSO metadata-file upload shipped the same day.
Why it matters: This hardening follows the July incident, which involved a customer's unauthenticated endpoint. Admins can now enforce authentication across a workspace.
Day-zero endpoints for Kimi K3, Qwen 3.8-2.4T-A95B, GLM-5.3 and GLM-5.3-Flash. Also: environment budgets, a regional pricing multiplier cut from 1.5x to 1.15x, Private Environments and Regional Proxy selection. Python SDK 1.5.5 and JS/Go SDK 0.10.0. Announced Runtime for 2026-10-01 in San Francisco. VM Sandboxes went GA on 2026-10-01.
Why it matters: Multi-container sandboxes and Docker-in-sandbox let teams run more complex RL and agent environments, such as an app plus its database.
London is Modal's second European office after Stockholm, which has an engineering team of nearly 20. Hugh Killingbeck-Jones joins as VP EMEA. Modal names Black Forest Labs and Legora as European customers and says it plans more data-residency and compliance support.
Why it matters: EU and UK buyers get local sales and support. Data residency is still on the roadmap.
The filesystem API is GA, and Modal is speeding up deprecation of the legacy file API. Directory snapshots went GA on 2026-08-12, and V2 Sandboxes became opt-in (MODAL_SANDBOX_V2=1) on 2026-08-14, both per modal.com/changelog. The public changelog's earliest entry is dated 2026-08-06 (environment budgets). Regional proxies followed on 2026-08-31.
Why it matters: GA file I/O and snapshots matter for saving and resuming environment state in RL rollouts.
Hugging Face disclosed an agent intrusion in which an attacker abused a Modal customer's public endpoint that ran user-submitted code in Modal Sandboxes. Modal says its platform and isolation were not compromised and no other customers were affected. It recommends authentication, IP allowlists, egress restrictions and treating all input as untrusted.
Why it matters: A reference point for anyone exposing sandboxes publicly. Modal attributes the incident to the customer's configuration, not to a failure of its isolation.
Devin sessions can run inside a customer's own Modal Sandboxes, including GPU-backed ones. Set up with `uvx modal-devin init`. modal-devin is open source.
Why it matters: Deepens the Cognition relationship. Devin's actions run in sandboxes the customer controls.
The new scheduler has no central database in the scheduling path. Modal reports creating 1M sandboxes in under 60 seconds, tens of thousands per second, with median startup under 500 ms. The new system is in beta and will eventually handle all sandbox scheduling.
Why it matters: This is the vendor's own scale claim, and it bears directly on large parallel RL rollouts.
Modal partnered with Thinking Machines on day-zero support, with token-based pricing and custom DFlash speculative decoding. Modal reports 250 tokens per second per user, 67% faster than the model's built-in speculative path.
Why it matters: A launch collaboration with a frontier lab.
Speeds up diffusion language models (arXiv 2605.18817). The Modal blog lists Yufeng Xu and Rahul Chalamala as Modal Research, with Yucheng Lu of NYU Shanghai's HeavyBall Research. The blog reports up to 1.56x throughput in SGLang; the arXiv abstract reports up to 1.4x lossless speedup, with +22.6 points on HumanEval and +17.7 on GSM8K under aggressive decoding.
Why it matters: Modal has a named research group that co-authors peer-style papers.
| Date | Round | Amount | Led by | |
|---|---|---|---|---|
| 28 Sep 2026 | undisclosed unsettled | $750M valuation $15.75B | Accel | source ↗ |
| Unannounced new round (stage not disclosed) · $750M (reported as nearly closed; Modal has not announced it) | ||||