Tokens streaming along a conveyor belt into the output bin

INFERENCE INFRASTRUCTURE

Buildingthefoundationforthefuture.

Serve any open model at the lowest latency on the market. No cold starts, no babysitting, no surprises.

12ms

MEDIAN TIME-TO-FIRST-TOKEN

99.99%

MONTHLY UPTIME

200+

MODELS DEPLOYABLE

298,400,312

TOKENS SERVED TODAY

THE PLATFORM

One stack, from raw silicon to served tokens.

VIEW DOCS →

Inference

Serve any open model behind one endpoint with automatic batching and routing.

12ms median TTFT

OpenAI-compatible API

Autoscale to zero

Compute

On-demand H100 & B200 clusters with per-second billing and zero queue time.

H100 / B200 / MI300X

Per-second billing

Reserved or spot

Models

Fine-tune, evaluate and ship your own checkpoints from a managed registry.

LoRA & full fine-tunes

Versioned registry

One-click rollback

BENCHMARKS

Built for speed you can feel.

Median time-to-first-token across providers serving Fortis-L 70B, measured over 10k requests.

tokens / second · higher is better

FORTIS340

PROVIDER A182

PROVIDER B146

PROVIDER C94

MODEL LIBRARY

Deploy in one click.

MODEL
TYPE
PARAMS
CONTEXT
$ / M TOK

Fortis-L 70B

Text

70B

128K

$0.60DEPLOY ↗

Fortis-L 8B

Text

8B

128K

$0.10DEPLOY ↗

Fortis-Vision 34B

Vision

34B

64K

$0.80DEPLOY ↗

Fortis-Code 16B

Code

16B

256K

$0.25DEPLOY ↗

Fortis-Voice 4B

Audio

4B

32K

$0.15DEPLOY ↗

Fortis-Embed

Text

1.5B

8K

$0.02DEPLOY ↗

DEVELOPER API

Ship in three lines.

One OpenAI-compatible endpoint. Swap the base URL and your stack just got faster.

OpenAI-compatible REST & streaming

Python, TypeScript & Go SDKs

Sub-second autoscaling to zero

infer.py

# pip install fortis
from fortis import Client

client = Client(api_key="fp-•••••••")

out = client.infer(
    model="fortis-l-70b",
    prompt="Explain inference at scale",
    stream=True,
)

for tok in out:
    print(tok.text, end="")

Building the foundation for the future.

START BUILDING TALK TO SALES