Self-hosted model gateway

Control which model handles each request.

Every call goes through one gateway. ContextLane reads what a request actually needs, checks it against your policy, and picks a model that's good enough for the job, instead of your priciest one by default.

Runs in your own environment. Point one app at the gateway to start; the rest of your stack stays as it is.

Routing decisionreq_7c19e2
RequestSummarize these release notes in five bullets.
SummarizationLow complexityNo sensitive data
Candidates considered
gpt-5.4$0.00122Skipped
gpt-5.4-mini$0.00018Selected
85% cheaperSame answer, a fraction of the cost.

Nothing here needs a flagship model. It's a short summary, no sensitive data, and the mini model clears the quality bar. Paying flagship prices for it would just be waste.

Provider coverage

Route across the providers your team already uses.

Use one routing and policy layer across direct model APIs and enterprise cloud providers. Start with one provider, then expand as budgets, fallbacks and controls mature.

Direct model APIs
OpenAIAnthropicGoogle GeminiQwenKimiZ.AI / Zhipu
Enterprise cloud routes
Azure OpenAIAWS BedrockGoogle Vertex AI
$2kto$20kin eight weeks

Why ContextLane exists

“We weren't using AI ten times more. We were just sending almost every request to our most expensive model.”

ContextLane started after we watched simple summaries, doc drafts and one-line code questions all default to flagship models. So we built a layer that sits in front of the providers: it reads each request, applies your policy, picks the model, and records what the call cost.

How routing works

One endpoint. A visible decision for every request.

Keep your messages, tools and response handling unchanged. Replace the endpoint and credentials.

01Application

Existing messages, tools and response handling stay the same.

02ContextLane endpoint

Replace endpoint and credentials with the gateway values.

03Task classification

Infer task type, complexity, risk and confidence.

04Policy check

Apply provider allowlists, pins, exclusions and sensitivity rules.

05Model selection

Select the approved model that clears the configured threshold.

06Cost record

Store route, usage, baseline and decision metadata.

Routing examples

Not every request should be downgraded.

The route is bounded by quality, confidence and policy. Some requests save money; others stay on a stronger model or a pinned provider on purpose. Pick one to see the decision.

Routing decisionreq_c40f1a
RequestSummarize these release notes in five bullets.
SummarizationLow complexityNo sensitive data
Candidates considered
gpt-5.4$0.00122Skipped
gpt-5.4-mini$0.00018Selected
85% cheaperSame answer, a fraction of the cost.

Nothing here needs a flagship model. It's a short summary, no sensitive data, and the mini model clears the quality bar. Paying flagship prices for it would just be waste.

Illustrative example. Real routing depends on your policy, provider pricing and traffic.

Evidence and methodology

Start with shadow mode before changing production traffic.

ContextLane can watch your traffic and estimate what routing would do before it ever changes a live request. The numbers below are illustrative. A real pilot replaces them with your own.

Illustrative routing analysis
14 daysanalysis window
18,420requests analyzed
64%eligible candidates
41%projected reduction
Why you can trust the number
  • Measured on your real traffic: real requests, real token counts.
  • Priced against live provider rates on the day it's calculated.
  • Your policies apply: quality floors, allowlists, pinned workloads.
  • Sensitive and pinned requests are left out, so nothing is inflated.

Analytics is the visibility layer

See spend, avoided spend, failures and overrides in one place.

A demo interface. A real dashboard runs on your own traffic, with the calculation date on record.

WorkspaceEngineering platform
RangeLast 14 days
Request IDreq_94bc1f
Spend over time
  • Baseline
  • Actual spend
  • Avoided
Spend$7,812
Avoided spend$11,240
Route overrides3.2%
Provider failures12
10:42:18docs-summarygpt-5.4-minipolicy passed
10:44:03auth-debugclaude-sonnet-4-6kept high capability
10:47:51customer-escalationpinned providersensitive route

Who it is for

Different teams, one routing control plane.

01

Platform engineering

Own providers, credentials, policies and fallback in one place instead of scattered across services.

02

Product teams

Cut inference cost without hand-rolling routing logic in every app.

03

Finance and FinOps

Attribute model spend by team, app, feature and request type.

Security and deployment

Designed as infrastructure, not a hosted prompt box.

ContextLane is deployed in the customer's environment. Provider credentials and policies are configured by admins, and route metadata is recorded for audit and cost attribution.

  • Provider allowlists and model exclusions
  • Audit logging for routing decisions and policy results
  • Self-hosted customer deployment path
Your environment
Your appschat · API · CLI
ContextLane gatewaypolicy · route · log
Keys, prompts and audit logs stay in here.
Approved providersOpenAI · Anthropic · Gemini · Qwen · Kimi · Zhipu

ContextLane's own servers are never in the request path. The gateway is the only thing that reaches out to providers.

Interfaces

One routing layer. Three ways to use it.

API

Controlled gateway endpoint

Best fit for products, internal services, agents and CI workflows that need repeatable controls.

Chat

Shared workbench

Team chat with session continuity, skills and visible model/cost metadata.

CLI

Developer terminal access

Repository-aware asks through the same policies and provider routing.

Pilot path

See what your current LLM traffic could cost.

Run ContextLane in shadow mode, or send us an anonymized sample of your requests. We'll show you which ones could move to cheaper models, and exactly how we got there.