Deploy Infralo on-premises to centralize AI routing, observability, and provider management across your AI infrastructure.
Stop rebuilding retry handling, provider switching, and request tracing inside every application. Deploy Infralo locally or inside your private network to centralize AI routing and observability.
Juggling different proprietary SDK parameters, formats, and structural variables leads to sprawling codebase complexity.
Debugging multihop workflows across microservices is nearly impossible without cohesive tracing of inputs, outputs, and latencies.
Simple try-catch mechanisms fail under heavy peak traffic spikes, rate limits, or direct upstream provider outages.
# ❌ Scattered SDK wrappers, complex manual pass-throughs, custom timers
import openai, anthropic
import time
def fetch_chat(prompt):
try:
start_time = time.time()
client = openai.OpenAI(api_key=OPENAI_KEY)
res = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
log_latency("openai", time.time() - start_time)
return res.choices[0].message
except Exception as e:
print("OpenAI failed. Starting Anthropic manual fallback retry...")
try:
client = anthropic.Anthropic(api_key=ANTHROPIC_KEY)
res = client.messages.create(
model="claude-4-6-sonnet",
messages=[{"role": "user", "content": prompt}]
)
return res.content
except Exception as fallback_err:
raise RuntimeError("Complete outages. Downstream failure.")# ✅ Point standard OpenAI client to the local Infralo gateway proxy
import openai
client = openai.OpenAI(
base_url="http://localhost:8000/v1", # Self-hosted gateway endpoint
api_key="vk_workspace_prod_key" # Workspace API Key (vk_...)
)
def fetch_chat_unified(prompt):
res = client.chat.completions.create(
model="production-agent-router", # Centralized Load-Balanced deployment
messages=[{"role": "user", "content": prompt}]
)
return res.choices[0].message.contentConnect cloud models, self-hosted deployments, and internal AI endpoints through a centralized control plane running inside your own infrastructure.
Govern, route, and orchestrate diverse LLM sources behind an OpenAI-compatible interface with zero application code changes.
Observe prompt execution flows, record parent-child spans, audit payload parameters, and track latency benchmarks on a single waterfall timeline.
Mitigate rate-limiting bottlenecks and API downtime by automatically rerouting traffic to alternative active provider pools in microseconds.
Apply validation checks centrally. Manage API rate limits, mask sensitive PII data, and audit safety guardrails before payloads hit upstream endpoints.
Audit model performance, latency drift, and answer quality across provider endpoints with built-in evaluation scripts.
High-level observation of autonomous agent runs. Monitor loop cycles, external tool invocations, and memory context sizes across hops.
Interact with Infralo's control console. Inject downstream API breakdowns or enable local caching capabilities to see how request waterfalls, live metrics, and service levels respond instantly.
Spin up the gateway proxy and control panel as a self-hosted Docker container in your own private network. Integrate by pointing your application clients to the proxy's URL.