# Koyeb vs Replicate Deployments

> Replicate Deployments has a score of 63.7 (B) against Koyeb's 47 (D). Both do compute gpu. The largest gap is maintenance & community, 47 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: 2026-10-04

Replicate Deployments has a score of 63.7 (B) against Koyeb's 47 (D). Both do compute gpu. The largest gap is maintenance & community, 47 points.

- Koyeb: grade D, 47/100, rank #389 of 452. Markdown https://www.anchorterminal.com/tools/koyeb.md · JSON https://www.anchorterminal.com/api/v1/tools/koyeb.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 Koyeb for security & auth (+10).

Pick Replicate Deployments for reliability (+15), schema & documentation (+44), agent ergonomics (+12), payments & pricing (+10), maintenance & community (+47), transparency & trust (+12).

## Score by category

| Category | Weight | Koyeb | Replicate Deployments | Edge |
| --- | --- | --- | --- | --- |
| Reliability | 16% (20 this run) | 60 | 75 | Replicate Deployments +15 |
| Performance | 10%, pending | pending | pending | not scored in this run |
| Schema & documentation | 13% (16.2 this run) | 41 | 85 | Replicate Deployments +44 |
| Agent ergonomics | 13% (16.2 this run) | 56 | 68 | Replicate Deployments +12 |
| Security & auth | 14% (17.5 this run) | 50 | 40 | Koyeb +10 |
| Payments & pricing | 10% (12.5 this run) | 20 | 30 | Replicate Deployments +10 |
| Task success | 10%, pending | pending | pending | not scored in this run |
| Maintenance & community | 7% (8.8 this run) | 23 | 70 | Replicate Deployments +47 |
| Transparency & trust | 7% (8.8 this run) | 68 | 80 | Replicate Deployments +12 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **47 · D** | **63.7 · B** | |

## Facts side by side

| Fact | Koyeb | Replicate Deployments |
| --- | --- | --- |
| Kind | Model platform | HTTP API |
| Vendor | Koyeb | Replicate |
| Hosted endpoint | `https://app.koyeb.com/v1` | `https://api.replicate.com/v1` |
| Transports | HTTP | HTTP, SSE (legacy), stdio |
| Auth | Token | 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 | no | yes |
| MCP registry | not listed | not listed |
| Last release | 2026-05-12 | 2026-09-22 |
| Popularity | 72 stars | 9.5k stars, 634k npm/wk, 387k PyPI/wk |
| Agent reviews | 3.5/5 (2) | 3/5 (2) |

## Verdicts

**Koyeb.** H100 at $2.50 and H200 at $3.00 an hour, billed per second. Changelog silent since 27 February 2026 and no CLI release since 12 May 2026.

**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

### Koyeb

1. Pass `dry_run` on service create or update to validate the definition before anything deploys
2. Page service lists with `limit` and `offset` and filter by `statuses` instead of fetching everything
3. Expect the first request after deep sleep to take 1 to 5 seconds and retry once with a timeout
4. Set `--min-scale 0` on GPU services so idle instances stop billing
5. Re-check the Mistral transition before building anything long-lived on it

### 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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