AI Tech Stack & Frameworks behind intelligent apps.
A comprehensive breakdown of the modern AI engineering stack: LangChain, LlamaIndex, PyTorch, vLLM, PostgreSQL pgvector, and cloud infrastructure.
AI frameworks change constantly; engineering fundamentals endure. We build modular, vendor-agnostic systems chosen for longevity, not novelty.
Selecting the wrong AI tech stack locks your organization into bloated GPU cloud bills and architectural dead-ends. Steigern Tech evaluates your specific concurrency, latency, and compliance requirements to engineer a lean, long-term tech stack.
Decoupled architecture
Abstracted service interfaces that allow replacing a model or vector database with zero disruption to frontend user interfaces.
Production stability
Prioritizing battle-tested open-source libraries that handle high-concurrency enterprise workloads reliably.
Zero vendor lock-in
Full architectural freedom to migrate from commercial cloud APIs to private self-hosted weights as your operational volume scales.
Six modular layers, built to scale.
Stack selection is an engineering trade-off: balancing framework ergonomics against runtime performance. We benchmark latency, memory footprint, and compute unit economics before writing production code.
Frontier LLM integration
OpenAI GPT-4o, Anthropic Claude 3.7, and Google Gemini 2.0 utilized for deep analytical reasoning and multimodal comprehension.
Private open-source weights
Meta Llama 3.3, Mistral Large, and DeepSeek running on private vLLM clusters for full privacy and sub-100ms latency.
Vector indexing & retrieval
PostgreSQL pgvector, Pinecone, and Weaviate delivering high-precision hybrid semantic and keyword retrieval.
Agent graphs & microservices
LangGraph, FastAPI, and Celery managing complex multi-agent state machines and asynchronous task queues.
Inference servers & acceleration
vLLM, Ollama, TensorRT, and CUDA optimization delivering high-throughput batching on cloud and edge hardware.
Observability & quality tracking
Prometheus, Grafana, MLflow, and OpenTelemetry monitoring production latency, cost, and prediction accuracy 24/7.
From architectural confusion to a proven tech stack, sized for enterprise scale.
We don't guess hardware requirements. We profile inference throughput, memory bandwidth, and token economics on real workloads.
Workload profiling
Benchmark expected request concurrency, input/output payload sizes, and SLA latency targets across your application surfaces.
Compute & hardware sizing
Calculate GPU VRAM requirements, CPU fallbacks, quantization trade-offs, and multi-tenant scaling models.
Framework tournament
Compare vLLM, TensorRT-LLM, Triton, and Ollama on identical hardware to identify maximum throughput per dollar.
Reference architecture POC
Deploy working benchmark harness demonstrating latency and token cost baselines on your sample queries.
Cost & cloud optimization
Negotiate cloud GPU spot instances, reserve pricing, or on-prem hardware clusters to cut inference bills by up to 60%.
Architecture contract handover
Deliver detailed architecture blueprints, container specifications, and Terraform provisioning scripts.
Workload profiling:Benchmark expected request concurrency, input/output payload sizes, and SLA latency targets across your application surfaces.
production stack provisioning, Terraform deployment, and cluster tuning
hardware pricing recalibration and framework throughput upgrades
Stack Outcomes that protect your investment.
A modular, future-proof engineering foundation that absorbs new model breakthroughs effortlessly without requiring code rewrites.
Zero vendor dependency
Seamlessly switch between OpenAI, Anthropic, or self-hosted open weights with simple configuration adjustments.
Minimal technical debt
Clear boundaries between frontend application layers, orchestration graphs, and underlying model inference servers.
10x Throughput scaling
High-performance asynchronous FastAPI microservices capable of serving thousands of concurrent user queries.
Predictable compute sizing
Right-sizing infrastructure to your actual user traffic patterns, preventing wasteful over-provisioning.
Universal data compatibility
Native connectors linking modern vector stores with legacy SQL, ERP, and CRM enterprise databases.
48-Hour model adoption
Integrate and benchmark newly released foundation models in production within 48 hours of public availability.
Benchmarked for maximum throughput and minimal TCO.
Built on Linux, CUDA, Docker, Kubernetes, Triton Inference Server, and vLLM — optimized for cost-effective enterprise scaling.
Idea to Market–Ready Product
We turn your idea into a secure, compliant, and test-ready product in weeks, not months.
Inquiry & Scoping
We rapidly decompose requirements, pressure-test commercial viability, and align technical scope before capital is committed.
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