Distributed Ray workloads for agents/ML
Scale Python agent/ML workloads across a Ray cluster with managed control plane.
At a glance — Oct 2026
Updated 2026-10-27 · forward this page: /jtbds/ray-distributed-agents#briefScores as of 2026-09-14 (43 days old; Depot marks scores stale after 30 days). The pick is still Depot's current editorial pick; it has not been re-reviewed since. Prices below carry their own dates.
- Depot Pick
- Anyscale · 7.46/10 for this job (curated estimate, high confidence)
Capability for distributed Python; Anyscale when Ray is the substrate.
- Runner-up
- Modal · 8.44/10
Scores higher than the pick on: Capability fit, Price / performance, Latency / reliability, Developer & agent DX, Lock-in risk (higher = worse).
- When not to use the pick
- Single-endpoint serverless — Modal; lakehouse-native — Databricks.
- Lock-in / migration
- Runner-up Modal has lower stored lock-in risk (4.5 vs 5.5, 0–10, higher = more lock-in). Self-host fallback listed for this job: Ray.
- Sourced price (no comparable unit)
Pick: no sourced price in this job's unit (no_sourced_price).
No cheapest SKU (no_comparable_unit_for_job). No price was invented.
- Free option
- Ray (oss) — source, as of 2026-10-25
- Failure modes to watch
- Runaway spend: Set a budget and a tool-call cap per task. Alert when spend jumps. Stop the run at the cap instead of summarizing a partial trace as success.
- Hallucinated tool arguments: Reject a call that fails the current schema or names an unknown id, and do that before the tool runs a side effect.
- Identity confusion: Bind every tool call to a user or tenant id. Refuse the call if that id changed mid-task. For a public bot, the operator owns the words the bot shows.
- Instruction drift: Restate the stop condition in the trace. End the run when it is met. Review long traces for steps that do not serve the original task.
- Inter-agent misalignment: Log the assigned subtask next to the worker's stated result. The lead rejects a result that does not match the assignment.
- Observability gap: Alert on a drop in trace ingestion and on certificate expiry for the trace host. Do not treat a missing trace as a successful quiet run.
Cite: Agent Provider Depot, Depot Pick for ray-distributed-agents: anyscale, scores as of 2026-09-14. Agents: GET /api/decide?jtbd=ray-distributed-agents
Free Distributed Ray workloads for agents/ML options · for orchestrator agents
Anyscale
Capability for distributed Python; Anyscale when Ray is the substrate.
Pricing (directional): As of 2026-09-14 (anyscale.com/pricing): PAYG in Anyscale Credits, no monthly platform fee. Hosted examples: CPU AC 0.0135/hr; T4 0.5682; L4 0.9542; A10G 1.3635; A100 4.9591; H/B/GB families listed as contact. Hosted (Anyscale-managed, limi…
Lock-in risk: higher = more lock-in (worse). Overall already inverts this dimension.
Default when your agent stack already is Ray or you need serious multi-node Python. For simple serverless GPU prefer Modal/RunPod.
Quote for Distributed Ray workloads for agents/ML
Depot Pick stays Anyscale. Prices below are sourced SKU rows only (as of 2026-10-27). Input and output are not blended. GET /api/quote?jtbd=ray-distributed-agents
Nothing was compared. no comparable sourced unit for this job; chat input $/1M and other units were not borrowed pickIsPriceWinner is null.
Sourced price unavailable for the Depot Pick. No row in the pricing index.
4 of 6 candidates have no sourced price row. 6 are excluded from the Matching price comparison (none). no comparable sourced unit for this job; chat input $/1M and other units were not borrowed
Runners-up
Modal
Depot darling for Python agent backends and bursty GPU jobs. If you think in functions not VMs, start here.
CoreWeave
Enterprise training/inference fleet pick. Skip if you just need a single H100 for an afternoon — use RunPod.
Databricks Mosaic AI
Pick only when your data/platform already is Databricks. Otherwise Bedrock/Vertex or direct APIs.
When NOT to use the top pick
Single-endpoint serverless — Modal; lakehouse-native — Databricks.
Full ranking (score table)
Ranked with this JTBD's weights. Scores are curated estimates unless marked otherwise. Agents: prefer /api/decide then this table for tie-breaks.
| # | Provider | Overall | Fit | $/perf | Latency | DX | As of |
|---|---|---|---|---|---|---|---|
| 1 | Modal | 8.4 | 9.0 | 8.2 | 8.6 | 9.6 | 2026-10-19 |
| 2 | Hatchet | 8.0 | 8.2 | 7.8 | 8.4 | 8.6 | 2026-09-14 |
| 3 | Lightning AI | 7.9 | 8.6 | 7.0 | 8.2 | 9.0 | 2026-09-14 |
| 4 | CoreWeave | 7.7 | 9.2 | 5.5 | 9.0 | 7.5 | 2026-10-19 |
| 5 | AnyscaleDepot Pick | 7.5 | 8.2 | 6.5 | 8.0 | 7.5 | 2026-09-14 |
| 6 | Databricks Mosaic AI | 7.1 | 8.4 | 4.8 | 8.5 | 6.8 | 2026-09-14 |
Score methodology for this job
Capability for distributed Python; Anyscale when Ray is the substrate.
capabilityFit: 25%pricePerformance: 18%latencyReliability: 15%developerDx: 15%privacyPosture: 10%lockInRisk: 7%maturity: 10%