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Platform & Engineering

One binary replaces seven Python libraries.

The translation, routing, guardrails, cost and fallback your team currently maintains as seven separate libraries is one Rust process you don't own and never page for.

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TODAY · 7 LIBRARIES, IN SEQUENCE Auth +18ms Translate +27ms Route +36ms Guardrails +45ms Cost ledger +54ms Tracing +63ms Fallback +72ms Sequential overhead compounds → ~197ms median, seconds at p99 WITH VIDAI · ONE IN-PATH ENGINE Vidai. all seven, one pass 1.95ms

The problem

Nobody built that stack on purpose.

LiteLLM for translation, something for traces, custom code for cost, custom code for guardrails, custom code for fallback. Seven libraries. Seven CVE surfaces. Seven on-call pages.

01

Maintenance burden

Afraid to upgrade one library because the seams break. The middleware is your code now.

02

On-call for the LLM pager

Production gateway failures discovered at 2am: rules silently shadowing each other, models with no rate card.

03

It's all your code now

Glue between seven libraries is bespoke, undocumented, and owned by whoever last touched it. The middleware became a product you didn't mean to build.

Drop-in. One line.

Keep the SDKs your team already uses.

No rewrite, no new framework. Point your existing stack at Vidai by changing one line. Backed by a real conformance suite, tested end-to-end against the live server.

your_app.py
# OpenAI SDK · only base_url changes
client = OpenAI(
    base_url="https://vidai.your-co.internal/v1",
    api_key=key,
)

OpenAI SDK, Anthropic SDK, Google ADK, Gemini, LangChain, LangGraph, each in its own native dialect.

The part most gateways skip

Bidirectional translation, not just "OpenAI-compatible".

Most gateways make you speak OpenAI's dialect, even to reach Anthropic or Google. Your Anthropic-SDK app gets rewritten to fit the gateway. Vidai translates both ways: your code keeps speaking the Anthropic SDK, the Google ADK or Gemini natively, and Vidai converts to and from whatever the target model actually speaks.

→Native in, native out. Call with the Anthropic SDK, route to OpenAI, and the response comes back in Anthropic shape. The translation is invisible to your code.
→No forced rewrite. Teams already on the Anthropic SDK or Google ADK don't refactor onto an OpenAI-shaped client just to get a gateway.
→Mix dialects across one fleet. Different teams keep different SDKs; Vidai is the common layer underneath, not a lowest-common-denominator.
→Proven, not claimed. Every direction of every pairing is exercised by the conformance suite against the live server.

Fits your stack

Three integration surfaces, by design.

Vidai slots into the enterprise stack you already run. It doesn't ask you to replace it.

→Webhooks (push). Guardrail blocks, circuit trips, budget breakers and errors. HMAC-SHA-256 signed, idempotent, SSRF-hardened. Straight into PagerDuty, Slack or your SIEM.
→BI tables (pull). Usage, per-request Events, Aggregates and Audit log as schema-stable CSV/API. A read-only service role feeds Snowflake, BigQuery, Looker or Tableau without touching production.
→Prometheus (scrape). Live metrics on the endpoint your monitoring already watches.

High availability In progress

Vidai Mesh: HA is one more server, not one more stack.

Making a typical AI proxy highly available means standing up another infrastructure layer, a Redis or a coordination service, beyond your load balancer, then operating it. Vidai servers gossip directly with each other. Adding availability is adding a Vidai server, not re-architecting your stack.

→No Redis, no coordination tier. Peers share state by gossip. There is no quorum service to deploy, secure, patch or page for.
→Scale by adding a node. Put another Vidai server behind your existing load balancer and it joins the mesh. No major reconfiguration.
→You add nodes by choice, not to keep up. One node carries the load with headroom to spare, so the node count is set by the HA you want, not by a per-node ceiling you hit.
→In progress for Scale and Enterprise. Actively in build. Talk to us about timing if HA topology is part of your evaluation.

Infrastructure efficiency

Agent-pace traffic is a fleet-sizing question now.

Once one task fans out into ten or more calls, the layer in front of your models is carrying agent-pace load. What one node clears at that load decides how many nodes you run, and a fleet is what you pay for, load-balance and patch. A verified 21,803 requests per second on a single legacy 8-core box means real traffic, even agent-pace, fits in one node with headroom; you add nodes for availability, not to keep up.

AI traffic calculator

Size the fleet for your own traffic.

Add up your interactive, customer-facing and batch AI work. See the agentic call volume, the peak load, and how many gateway nodes it actually needs.

Open the calculator

What you get

The middleware is not your code anymore.

One process you don't own, don't patch and never page for, in front of every model.

→One Rust binary on the hot path. Translation, observability, routing, guardrails, cost ledger, fallback. All built in. Read the Rust-vs-Python deep dive →
→Maintained by us, not by you. Provider API changes, new models, CVE patches are our release cycle now, not your on-call rotation. Latency under load is covered too.
→Drop-in, model-independent. Switching or mixing providers is a routing change in one place, not a refactor in fifty. Vendor sovereignty is part of the deal.
→Runs on your infrastructure. One ~25MB engine in your network. It doesn't phone home.

Drop your CVE surface.

A 20-minute technical walkthrough: architecture, the translation matrix, the benchmark methodology.

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