Better than Fine-Tuning.
Smarter than Vector RAG.
Plug into Any LLM.
Fine-tuning locks knowledge into weights that go obsolete. Standard RAG fractures context with generic embeddings. LastLayer bundles your data and retrieval intelligence into a unified package—achieving 98%+ retrieval precision while staying completely independent of the LLM you choose.
Turn Any Corpus into a Live Cognitive Layer
Drop your documents, train with precision, and chat or query via drop-in API in minutes.
Why the World Needs a New Approach
You shouldn't have to choose between obsolete model weights and inaccurate vector similarity.
| Capability | LastLayer | Fine-Tuning | Standard RAG |
|---|---|---|---|
| LLM Independence | ✓ Use Any LLM (Claude Opus 5.5, GPT-6 Astra, Gemini 4 Argon, Llama 4) | ❌ Locked to a single base model | ✓ Model agnostic |
| Retrieval Precision | ✓ 98% – 100% held-out accuracy | ❌ Hallucinations baked into weights | ❌ 60% – 75% vector accuracy ceiling |
| Data & Intelligence Together | ✓ Complete knowledge in one package | ❌ Data obscured, unextractable | ❌ Fragmented vector database & chunker |
| Upgrade Velocity | ✓ Switch frontier models in seconds | ❌ Retrain entire model at huge GPU expense | ✓ Switch models anytime |
| Inference Latency & Cost | ✓ <15ms on standard CPU | ❌ Expensive dedicated GPU hosting | ❌ Multi-stage re-ranking overhead |
Your Data & Intelligence. Universal Compatibility.
Package your domain expertise once. Query it with any current or future language model seamlessly.
Independent of the LLM
Unlike fine-tuning, LastLayer doesn't trap your company's intellectual property inside a specific checkpoint. Swap Claude Opus 5.5, GPT-6 Astra, Gemini 4 Argon, or Llama 4 tomorrow with zero re-work.
Self-Contained Intelligence
Every knowledge layer houses both the verifiable truth of your documentation and the cognitive precision to locate answers. No brittle vector indexes, no complex embedding pipelines to synchronize.
1-to-1 Drop-In OpenAI Protocol
Works out of the box with your existing LangChain, LlamaIndex, or native AI agents by simply configuring the base endpoint.
client = OpenAI(
base_url="https://sys.lastlayer.dev/api/v1",
api_key="ll_live_..."
)
Simple, Credit-Based Pricing
Pick a monthly plan. Credits cover both training your knowledge layers and querying them through any frontier model.
Training costs 2 credits per 1,000 characters (20-credit minimum per job). Query cost depends on the tokens used and the model tier you pick: fast models use the fewest credits, frontier reasoning models use more. Every new account starts with 100 free credits.