Open-Source Inference, Instant Intelligence.
Blazing-fast inference for Llama, Qwen, Mistral and more. Full-stack RAG pipelines with hybrid search and grounded citations. Deploy in seconds.
from tensoras import Tensoras
# One line to instant intelligence
client = Tensoras()
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[{"role": "user", "content": "Hello!"}]
)Trusted by teams building with
Deployment
Deploy anywhere, your way
Choose the deployment model that matches your compliance, latency, and cost requirements.
Cloud API
Start in seconds with our global edge network. No infrastructure to manage.
- Pay-per-token pricing
- Auto-scaling to zero
- Global edge PoPs
- 99.9% uptime SLA
- OpenAI-compatible endpoint
Dedicated Cluster
Reserved GPU capacity with guaranteed throughput and single-tenant isolation.
- Reserved A100 / H100 GPUs
- Custom model fine-tuning
- Single-tenant isolation
- Private networking (VPC)
- Dedicated support engineer
Self-Hosted
Run the Tensoras engine in your own cloud or on-prem with Docker & Kubernetes.
- Docker & Helm charts
- Air-gapped deployments
- Full data sovereignty
- Bring your own GPUs
- Community + Enterprise support
Capabilities
Everything you need for production AI
From low-latency inference to knowledge-grounded retrieval, Tensoras covers the full stack.
Instant Answers
Sub-200ms time-to-first-token with speculative decoding and continuous batching. Stream responses the moment they start generating.
Agents That Never Stall
Built for agentic loops with tool-use, function calling, and structured JSON output. Keep chains of thought flowing without timeouts.
Code at Speed of Thought
Optimized serving for code models with fill-in-the-middle, multi-file context, and inline completions. Perfect for AI-assisted development.
RAG Pipelines in Minutes
Ingest from 15+ data sources, auto-chunk with smart strategies, embed with state-of-the-art models, and retrieve with hybrid search.
Hybrid Search Built-In
Combine dense vector similarity with BM25 keyword search and reciprocal rank fusion. No external search engine required.
Citations & Grounding
Every RAG response comes with source citations, chunk references, and confidence scores. Verify claims with a single click.
Performance
Benchmarked against the fastest
Output tokens per second on standard chat workloads. Higher is better.
Measured on standard chat completion workload, 256 input / 512 output tokens
tokens / secRAG Pipeline
From raw data to grounded answers
A fully managed pipeline that ingests, embeds, indexes, retrieves, and generates -- with citations on every response.
Data Sources
S3, Postgres, Confluence, Notion, Kafka
Ingestion
Parse, clean, smart chunking
Embeddings
BGE, E5, Cohere, OpenAI
Vector Store
Built-in hybrid index
Retrieval
Semantic + BM25 + RRF
LLM
Tensoras inference
Citations
Grounded, verifiable output
Supported data sources
Testimonials
Loved by engineers
“We migrated from our DIY vLLM setup to Tensoras and cut our P95 latency by 60%. The OpenAI-compatible API made the switch trivial.”
Sarah Chen
Head of AI, Dataflow Labs