Head to head · Compute gpu · October 2026 research run

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.

Which one, for what

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

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)

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.

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.

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

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