Deploy and scale AI inference with sub-50ms latency. Access 200+ open-source models through a single API, or bring your own. Built on custom silicon optimized for transformer workloads.
Trusted by 50,000+ developers at companies like NVIDIA, Stripe, Shopify, and Notion
Powering AI at the world's most innovative companies
From prototyping to production at scale. InferGrove handles the infrastructure so you can focus on building.
Custom CUDA kernels and speculative decoding deliver 2-5x faster inference than alternatives. Sub-50ms time-to-first-token on all models.
Pay only for tokens consumed. Our optimized infrastructure means lower costs per token than running your own GPUs. Batch processing at 50% off.
OpenAI-compatible API for seamless migration. SDKs for Python, TypeScript, Go, Rust, and Java. Comprehensive docs and examples.
12 regions worldwide with intelligent routing. Your requests are served from the nearest cluster for minimal latency. Auto-failover included.
SOC 2 Type II, HIPAA, and GDPR compliant. Zero data retention — your prompts are never stored. VPC peering for dedicated clusters.
Real-time dashboards, request tracing, cost analytics, and alerting. OpenTelemetry export for custom observability stacks.
Watch how InferGrove makes AI inference effortless — deploy models, scale instantly, and monitor everything.
Our infrastructure processes millions of requests per second across 12 global regions.
Six core products that cover the entire AI development lifecycle — from experimentation to production at scale.
No infrastructure to manage. Send a request, get a response. Our serverless platform auto-scales from zero to thousands of GPUs in seconds, so you only pay for what you use. Compatible with OpenAI's API format for seamless migration.
from infergrove import InferGrove client = InferGrove(api_key="ig-...") # Run inference on any model response = client.chat.completions.create( model="meta-llama/Llama-4-Maverick-400B", messages=[ {"role": "user", "content": "Explain quantum computing"} ], max_tokens=512, temperature=0.7, stream=True ) for chunk in response: print(chunk.choices[0].delta.content, end="")
For production workloads that demand guaranteed capacity and isolation. Deploy models on dedicated NVIDIA H100 or H200 clusters with custom configurations, private networking, and enterprise-grade SLAs.
┌─────────────────────────────────────────────┐ │ Dedicated Cluster: prod-llama-4 │ ├─────────────────────────────────────────────┤ │ Status: ● Running │ │ GPUs: 64x H100 80GB │ │ Model: Llama-4-Maverick-400B │ │ Throughput: 48,200 tok/s │ │ Latency: 23ms p50 / 41ms p99 │ │ Uptime: 99.997% (30d) │ │ Region: us-east-1 │ │ │ │ GPU Utilization ████████████░░ 87% │ │ Memory Usage █████████████░ 92% │ │ Request Queue ██░░░░░░░░░░░░ 12 │ └─────────────────────────────────────────────┘
Fine-tune any open-source model on your data with just a few lines of code. Our platform handles distributed training, hyperparameter optimization, and automatic evaluation — deploy your custom model instantly after training.
from infergrove import InferGrove client = InferGrove(api_key="ig-...") # Start a fine-tuning job job = client.fine_tuning.create( model="meta-llama/Llama-4-Scout-109B", training_file="file-abc123", method="lora", hyperparameters={ "learning_rate": 2e-5, "epochs": 3, "lora_rank": 64, "batch_size": 32, }, evaluation={ "benchmarks": ["mmlu", "humaneval"], "eval_steps": 100 } ) print(f"Job started: {job.id}") # Status: training... 67% complete
Access the most comprehensive library of optimized open-source models. From Llama 4 to Mixtral, DeepSeek to Stable Diffusion — every model is pre-optimized with custom CUDA kernels for maximum throughput.
Popular Models Latency Throughput meta-llama/Llama-4-Maverick-400B 38ms 120 tok/s meta-llama/Llama-4-Scout-109B 18ms 340 tok/s deepseek-ai/DeepSeek-V3-685B 45ms 95 tok/s mistralai/Mixtral-8x22B-v0.3 12ms 480 tok/s Qwen/Qwen3-72B-Instruct 15ms 410 tok/s google/gemma-3-27b-it 9ms 620 tok/s stabilityai/stable-diffusion-4 1.2s — black-forest/FLUX.2-pro 0.8s — Showing 8 of 214 models →
Process millions of requests asynchronously with our batch API. Perfect for data labeling, content generation, embeddings at scale, and offline evaluation. Get 50% cost savings compared to real-time inference.
# Submit a batch job for async processing batch = client.batches.create( model="meta-llama/Llama-4-Scout-109B", input_file="file-batch-10M.jsonl", endpoint="/v1/chat/completions", completion_window="24h", metadata={ "project": "content-classification", "priority": "high" } ) # Check status status = client.batches.retrieve(batch.id) print(f"Progress: {status.completed}/{status.total}") # Progress: 7,234,891/10,000,000 # ETA: 2h 14m remaining # Cost: $142.67 (50% savings applied)
Create sophisticated AI agents that can reason, plan, and execute multi-step tasks. Our agent framework provides tool calling, memory management, and orchestration primitives — all running on our optimized inference stack.
from infergrove.agents import Agent, Tool # Define an autonomous research agent agent = Agent( model="meta-llama/Llama-4-Maverick-400B", name="Research Assistant", instructions="""You are a research agent. Analyze papers, summarize findings, and provide citations.""", tools=[ Tool.web_search(), Tool.code_interpreter(), Tool.file_reader(), Tool.vector_store("papers-db"), ], memory=True, max_steps=20 ) result = agent.run( "Find recent papers on KV-cache optimization for long-context LLMs" ) # Agent: Searching... Found 12 papers # Agent: Analyzing methodologies... # Agent: Summary ready with citations
Get started in minutes, not weeks. Our platform handles all the complexity of GPU orchestration, model optimization, and scaling.
Sign up for free and get your API key in seconds. No credit card required. $25 in free credits to start.
Browse 200+ optimized models or bring your own. Every model is pre-optimized with custom CUDA kernels for maximum throughput.
Make your first API call. Our OpenAI-compatible API means you can migrate existing code in minutes. Scale to millions of requests.
import { InferGrove } from '@infergrove/sdk'; const client = new InferGrove({ apiKey: 'ig-...' }); const response = await client.chat.completions.create({ model: 'meta-llama/Llama-4-Scout-109B', messages: [{ role: 'user', content: 'Hello, world!' }] }); console.log(response.choices[0].message.content);
From chatbots to code generation, content creation to data analysis — InferGrove powers it all.
Build conversational AI with streaming responses and function calling. Sub-50ms latency for real-time interactions.
Power IDE extensions, code review tools, and automated refactoring with specialized code models.
Generate marketing copy, blog posts, product descriptions, and creative content at scale.
Build retrieval-augmented generation systems with our embedding models and structured output.
Classify, tag, and annotate millions of data points with batch processing at 50% lower cost.
Create product images, marketing assets, and creative visuals with Stable Diffusion and FLUX models.
Translate content across 100+ languages with multilingual models. Preserve tone and context.
Extract structured data from documents, emails, and web pages with guaranteed JSON output.
Comprehensive tooling that makes building with AI a joy. From SDKs to CLI tools to playground.
Full-featured Python SDK with async support, streaming, type hints, and automatic retries. pip install infergrove.
First-class TypeScript support with full type safety, streaming helpers, and Edge Runtime compatibility.
High-performance Rust client for latency-critical applications. Zero-copy deserialization and async/await.
Manage models, deployments, and fine-tuning jobs from the command line. Scriptable and CI/CD friendly.
Interactive web playground to test models, compare outputs, and experiment with parameters before writing code.
Real-time monitoring, usage analytics, cost tracking, and team management in a beautiful web interface.
Independent benchmarks show InferGrove delivers 2-5x lower latency than major providers on equivalent models.
Lower is better. Measured at P50 with 1000-token prompts.
Higher is better. Measured with 512-token generation.
See why thousands of companies choose InferGrove for their AI infrastructure.
Our research team publishes cutting-edge work on model optimization, serving systems, and AI efficiency.
A novel approach to KV-cache management that enables 1M+ token contexts with minimal quality degradation on consumer hardware.
Our production system for speculative decoding that dynamically selects draft models based on prompt characteristics, achieving 2.8x speedup.
A calibration-free quantization method that achieves FP16-equivalent quality at INT4 precision across 50+ model architectures.
InferGrove integrates seamlessly with the most popular AI frameworks and development tools.
Native provider
Full integration
Streaming support
Agent framework
Model hub
Observability
Experiment tracking
Infrastructure as code
Pay only for what you use. No hidden fees, no minimum commitments. Start free and scale to millions of requests.
For experimentation
For production workloads
For large-scale deployments
Join 50,000+ developers using InferGrove to power their AI applications. Start free, scale infinitely.
No credit card required · $25 free credits · OpenAI-compatible API
🔒 SOC 2 Type II Certified
🏥 HIPAA Compliant
🇪🇺 GDPR Compliant
🛡️ Zero Data Retention