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Home/Capabilities/Multi LLM
Capability #08 · Models

Multi-LLM Support

OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral and private / local LLMs. Route intelligently per workflow.

Model catalogSmart routingFallbackEval-driven
#08
Capability
Models
Category
Live
In production
Day 1
Available
Models

Multi-LLM Support

OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral and private / local LLMs. Route intelligently per workflow.

  • OpenAI, Claude, Gemini, Llama, Mistral
  • Private/local models in your VPC
  • Smart routing by task/cost/latency
  • Provider fallback chains
  • Per-workflow eval & selection
Smart Router cost · latency · accuracy Claude OpenAI Gemini Private
How it works

Six pillars of Multi-LLM Support.

Each pillar can be enabled, configured and audited independently.

Model catalog

Curated and policy-controlled.

Smart routing

Task, cost, latency, accuracy.

Fallback

Provider failover automatic.

Eval-driven

Empirical scoring per workflow.

Private models

BYO weights in your VPC.

Cost guardrails

Per-tenant budgets and rate limits.

How it works

Pick the right model for every task.

OpenAI, Claude, Gemini, Llama, Mistral, your private models — routed automatically, fallback-protected, governed centrally.

1

Provider abstraction

A single API across providers — switch models per task, per environment, per region without rewriting code.

2

Routing policy

Route based on task class, latency budget, sensitivity, region or cost. Different models for different jobs.

3

Fallback chain

If the primary fails or hits a limit, requests cascade to fallbacks transparently — no dropped workflows.

4

Privacy classification

Sensitive payloads automatically route to private or in-region models. Public models never see classified data.

5

Cost & quality telemetry

Every call is tagged with provider, model, tokens, dollars and outcome quality — feeding routing decisions.

Outcomes

What customers measurably ship with this capability.

Real numbers from production deployments — across banking, healthcare, telco, manufacturing and the public sector.

Multi
Provider
Private
Models supported
Fallback
Built-in
Region
Aware routing
Time-to-value

Vendor independence

Don't bet the enterprise on one provider's roadmap, pricing or outage. Switch the underlying model without re-engineering the workflow.

Risk reduction

Cost-quality optimisation

Cheap models for high-volume routine tasks, top-tier models for hard decisions. The platform makes the call so you don't have to.

Industry use cases

How Multi-LLM support shows up in production.

Six concrete patterns from regulated enterprises across financial services, healthcare, telecom, public sector, energy and manufacturing.

Banking

Private models for sensitive flows

KYC and trade workflows route to in-VPC models; analytics workflows can use top-tier managed.

Insurance

Quality-based routing

Underwriting goes to the highest-quality model; FAQs go to a cheap, fast one.

Healthcare

PHI-safe routing

Patient-data workflows route to HIPAA-eligible providers and private endpoints.

Telecom

Latency-based routing

Customer-facing flows pick the lowest-latency provider per region in real time.

Public sector

Sovereign models

Workflows pinned to in-region or government-cloud-hosted models.

Manufacturing

Local models at the edge

Plant-floor agents run local models for sub-second decisions, sync to cloud for analytics.

Why xyner

Single-provider lock-in vs. multi-model platform.

Models change, prices change, regulations change. A multi-provider architecture future-proofs the investment.

Dimension
Without xyner
With xyner
Provider risk
One throat to choke; one bet to lose
Multi-provider with fallback
Pricing leverage
None
Re-route based on price
Outage tolerance
Down with the provider
Fallback chain
Region coverage
Wherever the provider is
Pick a provider per region
Private model support
Maybe
First-class
Migration cost
Months
Routing config change
Multi-LLM routing turned a $4M cost ceiling into a $1.2M floor — without sacrificing quality.
Head of AI Platform · Insurance Carrier
FAQ

Common questions, straight answers.

Can I pin a model per workflow?

Yes. You can also A/B test, version-pin, and define fallback chains.

Are private models supported?

Yes — hosted in your VPC with FIPS-compliant inference options.

How quickly can we adopt this capability?

Most customers adopt new capabilities in 2-4 weeks through starter packs and onboarding workshops.

Does this require new infrastructure?

No. The capability runs on your existing xyner deployment — cloud, hybrid, on-prem or sovereign.

Do you provide migration help?

Yes — our customer success team and partners deliver guided migrations and pilots.

Get started

Ready to put autonomous agents to work?

See xyner in your environment with a guided executive demo.

Request a demo Contact us
Keep exploring

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Multi-Agent Orchestration

Specialist agents work together — planning, delegating, sharing memory — for end-to-end enterprise workflows.

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Platform

Multi-LLM Routing & Model Agnostic

Route to the right model for every task: OpenAI, Claude, Gemini, Llama, Mistral and private models.

Learn more →
Solutions

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