What an inference API should do

What an inference API should do

What an inference API should do

Pioneer's API routes every task to the right model and learns from your production traffic.

Achieve frontier performance at a fraction of the costs.

Pioneer's API routes every task to the right model and learns from your production traffic.

Achieve frontier performance at a fraction of the costs.

Get started

Credit redeems on

claude-opus-4.8

gpt-5.5

nemotron

gemini-2.5-pro

~/ pioneer · zsh

# Drop-in replacement for OpenAI. Bring any OSS model.

# Drop-in replacement for OpenAI. Bring any OSS model.

$ curl https://api.pioneer.ai/v1/chat/completions

$ curl https://api.pioneer.ai/v1/chat/completions

-H Authorization: Bearer $PIONEER_KEY

-H Authorization: Bearer $PIONEER_KEY

-H Content-Type: application/json

-H Content-Type: application/json

-d ‘{

-d ‘{

“model”: “gemma”,

“model”: “gemma”,

“messages” [{“role”:”user”,”content”:”hi”}],

“messages” [{“role”:”user”,”content”:”hi”}],

“adaptive”: true

“adaptive”: true

}’

}’

✓ 200 OK 114ms tokens=18

✓ 200 OK 114ms tokens=18

{

{

“id”: “infr_8f2a…”,

“id”: “infr_8f2a…”,

“choices”: [{ “message”: { “content”: “Hi there.” } }],

“choices”: [{ “message”: { “content”: “Hi there.” } }],

“adaptive_score”: 0.94

“adaptive_score”: 0.94

}

}

WHY PIONEER

Everything your inference stack is missing.

Everything your inference stack is missing.

Everything your inference stack is missing.

app.py

# Already use the OpenAI SDK? Change one line.

# Already use the OpenAI SDK? Change one line.

from openai import OpenAI

from openai import OpenAI

client = OpenAI(

client = OpenAI(

base_url=https://api.pioneer.ai/v1,

base_url=https://api.pioneer.ai/v1,

api_key=os.environ[PIONEER_KEY],

api_key=os.environ[PIONEER_KEY],

)

)

resp = client.chat.completions.create(

resp = client.chat.completions.create(

model=gemma,

model=gemma,

extra_body={adaptive: True},

extra_body={adaptive: True},

)

)

01 SHIP FAST

From curl to production in 30 seconds.

A single OpenAI and Claude-compatible endpoint. Change one line of code and you’re live.

+ 70+ frontier and open source models including Qwen, DeepSeek, Gemma, Nemotron, and GLiNER
+ Industry-leading tokens per second, sub-200ms p50 latency
+ 99.99% uptime SLA, production-grade from day one
+ Streaming, tool calls, and structured outputs included

View quickstart ↗

02 SEE CLEARLY

Know exactly where your model fails.

Every response is automatically clustered by task and failure mode. See what’s breaking and why across every model you deploy.

+ Auto-clustered failure modes and task breakdowns across every request
+ Supported on all 40+ models on Pioneer
+ Drill into any cluster to see example inputs, outputs, and failure patterns
+ Use failure clusters to drive automatic model improvement

Tour the dashboard ↗

Dashboard view showing model monitoring and failure clusters
Retraining dashboard showing accuracy improvements

03 GET SMARTER

Your model retrains itself while you sleep.

Your endpoint gets smarter on its own. Adaptive Inference mines failures for high-signal examples and surfaces an improved model behind the same URL.

+ Continuous retraining on live production traffic
+ Download your weights and training datasets at any time
+ Every auto agent run generates a full PDF report
+ Bring your own evals or use ours

Read the paper ↗

+30% avg

+30% avg

+30% avg

accuracy lift on classification & extraction tasks vs. base Gemma

~7 days

~7 days

~7 days

until your first auto-improvement run lands in production

0 lines

0 lines

0 lines

of fine-tuning code you have to write, ever

$0/retrain

$0/retrain

$0/retrain

starting price. Pay for inference, the improvement is included

MODELS

Call any model in 30 seconds.

Call any model in 30 seconds.

Call any model in 30 seconds.

From frontier proprietary to fine-tunable open-source models. Pick your model, keep the same API.

From frontier proprietary to fine-tunable open-source models. Pick your model, keep the same API.

From frontier proprietary to fine-tunable open-source models. Pick your model, keep the same API.

Claude Sonnet 5

New

Anthropic

Inference only

Latest Sonnet model for coding, reasoning, and agentic tool use.

$2.00 / $10.00 per 1M tokens

Gemma 4 12B IT

New

Google

Trainable

Lightweight open model for fast, low-latency coding tasks.

$0.25 / $0.25 per 1M tokens

GPT-5.5

OpenAI

Inference only

Frontier coding and multi-step agentic workflows.

$5.00 / $30.00 per 1M tokens

Nemotron 3 Ultra

NVIDIA

Inference only

Open frontier reasoning and agentic orchestration.

$0.50 / $2.50 per 1M tokens

Qwen3 32B

Alibaba

Trainable

Coding, math, and reasoning with thinking-mode support.

$0.90 / $0.90 per 1M tokens

GLiNER2 Large

Fastino

Trainable

Fast, lightweight entity recognition for code and structured data extraction.

$0.20 / $0.20 per 1M tokens

Claude Opus 4.7

Anthropic · Proprietary

Deploy →

DeepSeek V4 Pro

DeepSeek · Proprietary

Deploy →

Kimi K2.6

Moonshot · Modified MIT · 256K ctx

Deploy →

GLiGuard 300M

Fastino · Apache 2.0

Deploy →

GLiNER2-PII

Fastino · Apache 2.0

Deploy →

Stop babysitting your inference stack.

Stop babysitting your inference stack.

Stop babysitting your inference stack.

Take Pioneer for a spin. If your model isn’t measurably smarter after a week of traffic, the fox owes you a coffee.

Take Pioneer for a spin. If your model isn’t measurably smarter after a week of traffic, the fox owes you a coffee.

Take Pioneer for a spin. If your model isn’t measurably smarter after a week of traffic, the fox owes you a coffee.

psst. Spend the credits on Opus 4.8, GPT-5.5, Gemini, Nemotron, anything you want.

psst. Spend the credits on Opus 4.8, GPT-5.5, Gemini, Nemotron, anything you want.