Foundation Model Setup & Fine Tuning
Select, configure, and fine-tune the right foundation model for your use case. From model evaluation through production deployment — on your cloud, your terms.
Choosing the Wrong Model Costs More Than You Think
With dozens of foundation models available — each with different strengths, pricing, latency profiles, and compliance characteristics — model selection is one of the highest-stakes decisions in any AI project.
Organizations that default to the largest or most well-known model without a structured evaluation process routinely overspend by 2–5x and suffer unnecessary latency penalties. Generic models deployed without fine-tuning produce inconsistent outputs that erode user trust.
eLightInfo's Foundation Model service gives you a systematic, benchmark-driven approach to selecting and configuring the optimal model — and fine-tuning it to match your specific domain vocabulary, output format, and quality bar.
Benchmark-First Model Selection
Requirements Analysis
Define your use case requirements: task type (generation, classification, extraction, reasoning), quality thresholds, latency SLAs, context length needs, compliance constraints, and budget targets.
Model Candidate Selection
Identify a shortlist from the current model landscape: Claude 3.5/4 variants, GPT-4o, Gemini 1.5/2, Llama 3.x, Mistral, and specialized domain models. We continuously update our benchmarks as new models release.
Benchmark Evaluation
Run your actual production prompts through candidate models. Measure accuracy, consistency, output format compliance, latency percentiles (p50/p95/p99), and cost per 1,000 requests. Present results in a clear decision matrix.
Fine-Tuning (Where Warranted)
For domain-specific tasks, we apply parameter-efficient fine-tuning using LoRA, QLoRA, or PEFT techniques. Includes training data curation, hyperparameter optimization, evaluation on held-out test sets, and regression testing.
Production Deployment
Deploy the selected model to your preferred cloud provider — AWS Bedrock, GCP Vertex AI, or Azure OpenAI — with load balancing, rate limit management, fallback routing, and cost monitoring dashboards.
Your Cloud, Your Terms
AWS Bedrock
Managed access to Claude, Llama, Titan, and other models through AWS. Ideal for organizations already operating on AWS infrastructure with existing IAM, VPC, and data governance controls.
GCP Vertex AI
Google Cloud's fully-managed ML platform with Gemini, Claude via Model Garden, and open-source models. Best for organizations with strong GCP data warehousing and analytics infrastructure.
Azure OpenAI & AI Studio
Microsoft Azure's managed OpenAI service with enterprise SLAs, private networking, and compliance certifications. The preferred choice for enterprises with Microsoft 365 and Entra ID environments.