Head to head · Compute gpu · October 2026 research run

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.

Which one, for what

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

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)

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.

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.

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

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