The decision layer for AI compute.
Every workload.The right compute.
Waymesh evaluates the execution paths a request is eligible for before dispatch—balancing cost, latency, reliability, capability and policy—then selects the best route for that workload.
Start in shadow mode. Your production route stays in control while Waymesh records what it would have chosen—and why.
Decision inputs
- Cost
- Price before dispatch
- Latency
- Observed by workload
- Reliability
- Success rate + uncertainty
- Capability
- Context, modality, output
- Policy
- Privacy, budget, residency
Evidence before authority
Trust the record before you trust the router.
Waymesh does not need a logo wall to make its case. Start with your existing route still in control and measure where the decision layer would disagree, what it predicts, and whether those predictions hold up against real outcomes.
Illustrative evaluation record
79,175
Routing decisions evaluated
Last 30 days
53%
Traffic assigned to Groq
11% of illustrative spend
99.26%
Observed execution success
Weighted by request volume
11.5%
Decisions that challenged the incumbent route
The starting point for proving routing value
Illustrative example — not customer production results.
Decision record
See which routes were eligible, which constraints rejected candidates, what won and why.
Economic record
Compare expected and observed spend by workload and provider instead of relying on list-price assumptions.
Operational record
Measure latency, reliability and routing disagreement on your traffic before production authority changes hands.
The decision
The same four routes. A different answer each time.
Pick a workload and watch the decision change. Nothing about the available providers moves — only what this particular request needs from them.
Routing decision
Request
Summarize a 40-page quarterly report into an executive brief.
Long-document summarization · 48,200 tokens in · ~900 tokens out · single call
- AnthropicSelectedclaude · 200k ctxCost$0.0412Latency8.4 sReliability99.4%Quality93%0.894
- OpenAIgpt · 128k ctxCost$0.0508Latency7.1 sReliability99.1%Quality90%0.881
- Private / self-hostedollama · on-premCost$0.0111Latency22.6 sReliability96.2%Quality78%0.798
- Groqllama · 32k ctxCost$0.0063Latency1.2 sReliability97.8%Quality81%
Excluded
Context window is 32,000 tokens; this request needs 48,200.
Anthropic won narrowly, scoring 0.894 against OpenAI at 0.881. It gained most on cost and gave up ground on speed.
The whole report has to fit in one call, and a summary nobody can trust is worse than a slow one. Reliability and answer quality carry the weight here; the budget is generous enough that cost breaks ties rather than deciding them.
One route was ruled out before scoring began, so it never competed on price or speed at all.
Illustrative example — not customer production results.
Decision record
- Classification
- Long-document summarization
- Policy
- Standard · Context window ≥ 48,200 tokens
- Budget ceiling
- $0.25 per request
- Eligible routes
- 3 of 4
- Rejected routes
- Groq
- Selected route
- Anthropic · claude · 200k ctx
- Expected cost
- $0.0412
- Expected latency
- 8.4 s
- Reliability
- 99.4%
- Routing score
- 0.894
Illustrative example — not customer production results.
Weighting for this workload
What this request is judged on, before any provider is considered.
- Answer quality30%
- Reliability30%
- Cost25%
- Latency15%
Capability and policy do not appear here. They are checked first, as pass-or-fail gates — a route that fails one is never scored, however well it would have done.
Illustrative example — not customer production results.
The problem
Your AI stack shouldn’t hard-code infrastructure decisions.
Choosing a provider once, in code, turns an operational decision into a structural one. The choice ages; the code does not notice.
Providers are good at different things
One model handles long documents, another returns structured output reliably, another is an order of magnitude cheaper for short classification. No single provider is the right answer for a whole application.
The economics move underneath you
Prices change, new models land, and a routing choice that was correct at integration time quietly stops being correct. Nothing in the application notices.
Reliability is not a constant
Availability and error rates drift by provider, by model and by time of day. Behaviour measured during evaluation is not the behaviour you get in production six months later.
Provider logic accumulates in the application
Retries, model names, token accounting, per-provider quirks and fallbacks spread through the codebase until the routing policy exists only as the sum of scattered conditionals.
Failover becomes a rewrite
Once provider-specific behaviour is embedded, moving a workload elsewhere is an engineering project rather than a configuration change — precisely when you need it to be quick.
Someone ends up maintaining it by hand
In practice an engineer re-benchmarks providers, updates a spreadsheet and edits constants. That work is never finished, and it is never anyone's actual job.
How it works
One path, measured end to end.
The loop is closed on purpose: a decision that is never compared against its outcome cannot improve, and cannot be audited either.
01
Request
Your application submits a workload with its requirements — task type, privacy tier, budget ceiling — and no provider name.
02
Evaluate
Every eligible execution path is scored on predicted cost, latency, reliability and capability, against the policy in force.
03
Route
Candidates failing a hard constraint are rejected outright. The winner is pinned to an exact endpoint, model and price.
04
Execute
The workload runs on the pinned route. Nothing is re-selected mid-flight, so what executes is what was scored.
05
Measure
Actual cost, latency and outcome are recorded against the prediction that chose the route — the comparison is the point.
06
Learn
Observed results update the statistics behind future decisions, per provider and per task type.
Measured outcomes feed the next evaluation
Decision intelligence
Evidence decides the route. Provider habit does not.
Waymesh evaluates eligible paths from observed performance, economics and policy fit. The selected route is a decision record—not a provider preference disguised as one.
- Cost
Priced before dispatch, reconciled after
Each candidate is costed against the pricing actually in force for its route, not a list price. The predicted figure is later compared to what was settled, so the estimate is accountable.
- Performance
Observed, per provider and per task type
Latency is measured from real executions and held separately for each kind of workload, because a provider fast at classification is not necessarily fast at long-document analysis.
- Reliability
Success rate carried with its uncertainty
A provider with four observations and one with four hundred are not treated as equally known. Thin evidence is penalised rather than rounded up to confidence.
- Capabilities
A gate, not a preference
Context window, modality and structured-output support are hard requirements. A candidate that cannot do the job is excluded outright — never scored down and then selected anyway.
- Policy
Your constraints, enforced before scoring
Privacy tier, budget ceiling and execution locality are evaluated before scoring, so a workload that must stay on private infrastructure has no path to a public API.
Shadow mode
Earn authority before routing production.
Waymesh observes first. Your existing production route remains in control while every request produces a second decision that can be compared against what actually happened.
Shadow observation
Request
Summarize a 40-page quarterly report into an executive brief.
Your existing route — executed
OpenAIgpt · 128k ctx
- Predicted cost
- $0.0508
- Actual cost
- $0.0531+4.5% vs predicted
- Predicted latency
- 7.1 s
- Actual latency
- 7.6 s+7.6% vs predicted
- Verification
- Passed
Waymesh preferred — not executed
Anthropicclaude · 200k ctx
- Predicted cost
- $0.0412
- Actual cost
- Not executed
- Predicted latency
- 8.4 s
- Actual latency
- Not executed
- Verification
- Not applicable
Shadow mode does not run the work twice. The preferred route is recorded and costed, never executed. Over enough requests, the gap between predicted and observed outcomes becomes the evidence needed to decide whether Waymesh should earn routing authority.
Illustrative example — not customer production results.
How authority is earned
Shadow deployment
Observe
Waymesh evaluates each request and records the route it would have chosen. Your existing routing executes the work, unchanged.
Shadow deployment
Compare
Predictions are checked against what the executed route actually cost and took, and disagreements between the two routers are counted rather than averaged away.
Ahead
Validate
Evidence has to cover the routes traffic would move to, not only the ones already running — and hold up across enough tenants and enough time to mean something.
Ahead
Promote
Only once that record supports it does routing take control of real traffic, and only for the segments the evidence actually covers.
A shadow deployment lives in the first two stages. Promotion is gated on evidence and is not enabled by default. Waymesh does not claim routing authority it has not earned.
Routing record
See where traffic moved—and whether the decision paid off.
Every route produces evidence: spend and traffic by provider, observed latency and reliability, and the requests where the shadow decision would have moved execution elsewhere.
Provider operations
- GroqSpend$46.00Requests41,820Avg latency420 msSuccess rate99.76%StatusOperational
- OpenAISpend$175.22Requests18,640Avg latency2.2 sSuccess rate99.12%StatusOperational
- AnthropicSpend$200.57Requests12,305Avg latency3.9 sSuccess rate99.38%StatusOperational
- Private / self-hostedSpend$4.49Requests6,410Avg latency9.7 sSuccess rate96.14%StatusDegradedOne of three nodes offline
Illustrative example — not customer production results.
Routing signal
11.5%
Requests where Waymesh would have chosen a different route
9,142 of 79,175 shadow decisions
53%
Share of traffic on Groq
11% of spend
2.1 s
Mean latency, weighted by request volume
An unweighted average would flatter the tail
The provider handling most of the traffic accounts for a fraction of the spend. That gap is the case for deciding per workload instead of selecting one provider for everything.
Illustrative example — not customer production results.
Execution mesh
Different compute paths. One decision layer.
Waymesh is useful when the available routes are genuinely different. Each supported provider adds another cost curve, latency profile, capability set or trust boundary to evaluate.
Breadth is not the goal by itself. A new route matters when it changes the answer for a real workload.
- OpenAIHosted API
Broad commercial model ecosystem
A wide spread of model sizes and capabilities behind one integration, useful when a workload has unusual capability requirements.
- AnthropicHosted API
Independent frontier route
A separate frontier path, reducing dependence on any single provider for high-capability workloads.
- GroqHosted API
High-speed inference option
A materially different latency and cost profile for workloads that do not need the most capable frontier model.
- Private / self-hostedOllama
Privacy-sensitive execution
Execution on infrastructure you control, so workloads with strict egress or locality requirements still have an eligible path.
Compatible execution providers. Waymesh is not affiliated with, endorsed by or partnered with any provider listed here.
Security & control
An infrastructure layer has to be answerable.
Waymesh sits between your application and the providers that execute its work. That position only earns trust if every decision it makes can be inspected afterwards.
Provider abstraction
Applications submit workloads, not provider names. Credentials, model identifiers and per-provider behaviour stay inside the control layer.
Policy-controlled routing
Privacy tier, budget ceiling and execution locality are hard constraints evaluated before scoring — not preferences a high score can override.
Private and self-hosted execution
Workloads that may not leave your infrastructure route to your own endpoints, and are structurally ineligible for public APIs.
Explainable decisions
Every decision records the candidates considered, the constraint that excluded each rejected one, and why the selected route scored highest.
Billing integrity
Spend is reserved before execution and settled after it, in exact integer amounts, with the ledger reconciled against what actually ran.
Auditable routing evidence
Predictions are retained alongside outcomes, so a routing claim can be checked after the fact rather than taken on trust.
Waymesh holds no third-party security certification at this time, and claims none.
Evaluate Waymesh
Put the decision layer against your real workloads.
Measure whether Waymesh would make better routing decisions without handing it production control on day one.
What an evaluation looks like
- You bring real AI workloads already running in production or staging.
- Waymesh evaluates them in shadow mode while your existing route keeps execution authority.
- You get a decision record covering cost, latency, reliability, policy fit and routing disagreements on your own traffic.