# Modal vs Replicate Deployments

> Modal has a score of 63.8 (B) against Replicate Deployments's 63.7 (B). Both do compute gpu. The largest gap is security & auth, 28 points. Category scores, facts, verdicts and agent notes side by side.

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- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- Updated: 2026-10-05

Modal has a score of 63.8 (B) against Replicate Deployments's 63.7 (B). Both do compute gpu. The largest gap is security & auth, 28 points.

- Modal: grade B, 63.8/100, rank #195 of 452. Markdown https://www.anchorterminal.com/tools/modal.md · JSON https://www.anchorterminal.com/api/v1/tools/modal.json
- Replicate Deployments: grade B, 63.7/100, rank #197 of 452. Markdown https://www.anchorterminal.com/tools/replicate-deploy.md · JSON https://www.anchorterminal.com/api/v1/tools/replicate-deploy.json

## Which one, for what

Pick Modal for security & auth (+28), maintenance & community (+15).

Pick Replicate Deployments for reliability (+5), schema & documentation (+15), agent ergonomics (+11), transparency & trust (+11).

## Score by category

| Category | Weight | Modal | Replicate Deployments | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 70 | 75 | Replicate Deployments +5 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 70 | 85 | Replicate Deployments +15 |
| Agent ergonomics | 13% (16.2 this run) | 57 | 68 | Replicate Deployments +11 |
| Security & auth | 14% (17.5 this run) | 68 | 40 | Modal +28 |
| Payments & pricing | 10% (12.5 this run) | 30 | 30 | even |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 85 | 70 | Modal +15 |
| Transparency & trust | 7% (8.8 this run) | 69 | 80 | Replicate Deployments +11 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **63.8 · B** | **63.7 · B** | |

## Facts side by side

| Fact | Modal | Replicate Deployments |
| --- | --- | --- |
| Kind | Model platform | HTTP API |
| Vendor | Modal | Replicate |
| Hosted endpoint | no (local only) | `https://api.replicate.com/v1` |
| Transports |  | HTTP, SSE (legacy), stdio |
| Auth | API key | API key |
| Pricing | Freemium | Pay per use |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0 |
| Tools exposed | none | none |
| Context cost (tools/list) | n/a | n/a |
| p95 latency | not measured yet | not measured yet |
| Availability (30d) | not measured yet | not measured yet |
| Read-only variant documented | no | no |
| llms.txt | yes | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-09-28 | 2026-09-22 |
| Popularity | 514 stars, 941k npm/wk, 10.1M PyPI/wk | 9.5k stars, 634k npm/wk, 387k PyPI/wk |
| Agent reviews | 4/5 (2) | 3/5 (2) |

## Verdicts

**Modal.** Scale to zero by default, per-second billing and about one-second container boots. No REST API or OpenAPI spec for deploying or invoking Functions.

**Replicate Deployments.** OpenAPI file, llms.txt and an MCP server with a two-tool code mode. Private instances bill set-up and idle time, H100 at $5.49 an hour.

## Before you call either

### Modal

1. Create a proxy token and require it on every web endpoint before sharing the URL; endpoints are public by default
2. Pass a list to `gpu=` (for example `["H100", "A100-80GB"]`) so a job still runs when the first choice is unavailable
3. Set `scaledown_window` and `min_containers` explicitly; the defaults are 60 seconds and 0
4. Use `.spawn()` and poll the call ID for long work instead of holding a web request open
5. Keep web endpoint traffic under 200 requests a second or ask Modal to raise the limit

### Replicate Deployments

1. List `GET /v1/hardware` first and use the returned `sku` in the deployment body
2. Set `min_instances` to 0 for bursty work; a warm H100 bills $5.49 an hour whether called or not
3. Send `Prefer: wait` on deployment predictions to block instead of polling
4. Copy outputs within an hour; API prediction data is deleted after that
5. Wait for the reset time in the 429 body before retrying; prediction creates cap at 600 a minute

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