LLM routing infrastructure
One endpoint.
Every model.
Lowest cost per answer.
Most teams pay frontier prices for work a small model handles perfectly. Thalera sits in front of your providers and routes each call to the cheapest model that can answer it — with failover, caching and live cost telemetry built in.
- 62%
- median spend cut
- <10ms
- routing overhead
- 99.98%
- request success
from openai import OpenAI
client = OpenAI(
base_url="https://api.thalera.dev/v1",
api_key=THALERA_API_KEY,
)
# No model name needed — Thalera picks the cheapest
# model that clears your quality bar for this prompt.
client.chat.completions.create(
model="thalera-auto",
messages=[{"role": "user", "content": prompt}],
)Same SDK. Same prompts. One line changed.
Routes across
- OpenAI
- Anthropic
- Mistral
- Meta Llama
- DeepSeek
- Groq
- Your own weights
Routing happens in three steps, on every single call
Classify
Every incoming request is scored in under 10 ms — complexity, context size, tool use, latency budget.
Route
The policy you set picks the cheapest qualifying model across providers, with live health and price data.
Verify
Responses are checked against your quality signals. Anything below bar is escalated to a stronger model automatically.
Everything a production LLM stack needs in front of it
Prompt-level routing
A lightweight classifier scores each request for difficulty, context length and required capabilities, then sends it to the smallest model that clears your quality bar.
Failover that holds
Provider timeout, 429 or degraded latency and the request re-routes mid-flight to a healthy equivalent. Your users never see a provider incident.
Semantic cache
Near-duplicate prompts return a stored answer instead of a fresh generation. Configurable similarity threshold and TTL, per route.
Spend you can see
Cost, tokens and latency broken down by key, route, model and team — in real time, not at the end of a billing cycle.
Built to scale
Edge-deployed routing adds single-digit milliseconds. Streaming, batching and concurrency limits work exactly as they do upstream.
Drop-in compatible
Change the base URL and keep your SDK, your prompts and your tooling. Migrating back out is one line too.
Where the money actually goes
A typical support-assistant workload, one million calls a month. Most of it is classification, extraction and short answers — work that never needed a frontier model.
See how routing policies work| Traffic class | Share | Routed to |
|---|---|---|
| Classification & routing | 38% | Small / fast tier |
| Extraction & formatting | 24% | Small / fast tier |
| Cached near-duplicates | 17% | Cache — $0 |
| General reasoning | 15% | Mid tier |
| Hard reasoning & code | 6% | Frontier tier |
Cut your inference bill this week
Point one service at Thalera, compare the numbers, then roll it out. No rewrite, no lock-in.