DeepSeekReasoning AI DEVELOPER PLATFORM
Your ideas.
Open models.
Your next chapter.
Build with an OpenAI-compatible multi-model API, transparent token metering, and a unified workspace for data and model workflows. Hosted inference is available when a model is served by a connected worker.
THE INFERENCE ENGINE01 / ∞
Open models
Your application
SpeedInferBUILT TO FLOW
token → token → token
Less friction. More invention.↗
Platform ActiveHigh-throughput model serving · 33+ open models · Low-latency streamingDeveloper docs ↗
POWERED BY POSSIBILITY.NVIDIA TensorRT-LLMNVIDIA TritonCUDA 12+FlashAttention-3vLLMPyTorchOpenAI compatible API
01 — ONE API. MANY POSSIBILITIES.
A multi-model gateway,
built around your stack.
Explore hosted open models alongside external API families from OpenAI and Anthropic. SpeedInfer keeps one catalog and a familiar request format while showing clearly how each model is accessed.
YOUR APPLICATION
POST /v1/chatOpenAI SDK
INTELLIGENCE LAYER
SpeedInferGATEWAY
AUTHROUTEMETERFALLBACK
DeepSeekReasoning
QwenMultilingual
LlamaGeneral
GemmaEfficient
MistralMultimodal
NemotronAgentic
OpenAIExternal API
AnthropicExternal APIUNIFIED MODEL CATALOGHosted open models and external API families.Compare models ↗
01
Call one endpointKeep the SDK and request format your team already knows.
02
Resolve the modelThe registry routes requests to connected model workers.
03
Stream the resultTokens flow back through one metered, controlled gateway.
02 — INFERENCE PRICING
Transparent pricing.
Predictable compute.
Hosted/serverless inference is billed by input and output tokens. Managed model endpoint planning is an optional workflow within the platform, not raw GPU access.
SERVERLESS INFERENCE
Pay-As-You-Go API
API access to models configured by the deployment operator. Check the live catalog and readiness before sending requests.
$0.20
/ 1M input tokens
$0.60
/ 1M output tokens
- ✓ Measured latency; no fixed speed guarantee
- ✓ Published data handling and retention disclosures
- ✓ OpenAI-compatible text chat and SSE
- ✓ Configured models listed in /v1/models
OPTIONAL MODEL ENDPOINT
MANAGED MODEL ENDPOINT
Dedicated Model Endpoints
Plan a managed model deployment with separate datasets and weights. Custom endpoint provisioning is coordinated with dedicated infrastructure.
Custom
/ endpoint plan
Managed endpoint planning · dedicated orchestration
- ✓ Dedicated worker endpoints with custom parameter scaling
- ✓ Separate datasets and model weights
- ✓ Private networking subject to agreement
- ✓ No fixed service-level commitment
03 — THE PLATFORM
Everything between
an idea and intelligence.
A connected toolkit for teams building with open AI. Less infrastructure to piece together. More room to create.
01 / INFERENCE
Let your ideas run.
Serve text generation through authenticated, metered API requests across open-weights models (via connected workers) and proprietary models (via external providers).
Explore the engine ↗02 / CUSTOMIZATION
Make it your own.
Adapt models with LoRA and QLoRA. Track experiments, evaluate, and export your model.
Discover the workflow ↗03 / CONTROL
Keep the controls.
Manage API keys, monitor token usage, and set rate limits from one focused workspace.
04 — BUILT ON OPEN TECHNOLOGY
From your data.
To your deployment.
Organize datasets and weights, and plan model workflows in the workspace. Inference availability depends on connected model workers.
Read the API documentation ↗01
Adapt
LoRA / QLoRA fine-tuning
02
Evaluate
Benchmark & accuracy evaluation
03
Serve
High-throughput inference & metered API
05 — DEVELOPERS FIRST
Familiar API.
Fresh possibilities.
Use the supported text chat API through the OpenAI SDK. Create a key, add credit, and check model readiness before starting.
✓ OpenAI-compatible endpoints✓ Server-sent event streaming✓ Per-key access and usage tracking✓ Authoritative upstream token accounting
Explore documentation ↗inference.py
import os
from openai import OpenAI
# Select a model returned by client.models.list()
client = OpenAI(
base_url="https://speedinfer.com/v1",
api_key=os.environ["SPEEDINFER_API_KEY"],
)
stream = client.chat.completions.create(
model=client.models.list().data[0].id,
messages=[
{"role": "user", "content": "Explain quantum teleportation in two sentences."}
],
max_tokens=128,
stream=True,
)
for chunk in stream:
if chunk.choices:
print(chunk.choices[0].delta.content or "", end="", flush=True)Python / OpenAI SDK (text chat)Real endpoint: https://speedinfer.com/v1 ↗
06 — PURPOSE-BUILT FOR AI TEAMS
Real infrastructure.
A clear path to production.
SpeedInfer is building a managed model platform: authenticated inference, usage accounting, and a workspace for model workflows. Hosted inference depends on connected model workers.
One connected stack
The gateway verifies permissions, rate limits, and available credit before routing requests to a connected model worker.
Your app→SpeedInfer Gateway→Connected model worker
Built for your workload
Build conversational assistants, developer tools, and enterprise domain-specific applications. Retention behavior is disclosed per connected worker or provider.
Inspect the API ↗PLATFORM FOUNDATION
OpenAI-compatible API · Model catalog · Token usage accounting · Developer workspace
Specific model-serving technology and performance depend on the connected worker.07 — TECHNOLOGY & DOCUMENTATION
Explore the system.
Follow every request.
Move from the public API to the control plane, model workflows, connected workers, and NVIDIA runtime documentation through one organized path.
01 / SYSTEM
Architecture
See how authentication, routing, worker selection, streaming, and usage metering fit together.
Explore architecture ↗ 02 / ACCELERATIONNVIDIA integration
Review the implemented path for connected CUDA, Triton, and TensorRT-LLM workers.
View NVIDIA path ↗ 03 / OPERATIONSInference workers
Configure worker registration, compatible-model routing, health checks, and availability.
Open worker docs ↗ 04 / NVIDIA DOCSRuntime guides
Connect and verify CUDA, Triton Inference Server, and TensorRT-LLM runtimes.
Read runtime guides ↗ 05 / MODEL WORKFLOWSFine-tuning API
Create and monitor LoRA or QLoRA jobs using uploaded JSONL datasets and explicit training settings.
Open fine-tuning docs ↗ENTERPRISE TRUST & DATA PRIVACY
Built for enterprise security.
Zero compromises.
SpeedInfer is architected from the ground up to meet stringent security, privacy, and compliance requirements for high-growth startups and enterprise AI workloads.
🛡️
Deployment-aware retention
The SpeedInfer Gateway does not retain prompt or completion content by default. Retention behavior of connected workers and external providers varies by deployment and is disclosed per provider.
🔒
End-to-End Encryption
All data in transit is encrypted using modern TLS 1.3 with forward secrecy. Internal data, storage buckets, and API key salts are protected with AES-256 encryption at rest.
📜
GDPR & CCPA Compliant
Full compliance with international data privacy regulations. Developers and organizations retain 100% intellectual property ownership of inputs and generated completions.
🔑
Granular RBAC Scopes
Role-based API key access control. Provision scoped API keys with least-privilege permissions (e.g. read-only, inference-only, storage-only) to protect production systems.
Platform readiness is shown per connected worker
08 — LET’S BUILD TOGETHER
Big plans.
Start a conversation.
Tell us what you’re building, ask a technical question, or get help with your workspace. Our team follows up by email.
Talk to sales.
Discuss your models, expected traffic, private inference requirements, and commercial options.
[email protected] ↗Prefer a form? Share your requirements and receive a reply by email.We’re here to help.
Report an issue, get help connecting to the API, or ask a general question about SpeedInfer.
[email protected] ↗Include the request ID and error message. Keep API keys and passwords private.THE NEXT THING STARTS WITH YOU