Make every AI request measurable.
GPUs can be healthy while AI applications perform poorly.
You're billed for tokens and blamed for latency, but the traffic between the user and the model is still a black box. Traditional observability tells you traffic is flowing — AI-Parser tells you what that traffic means.
Know who is consuming AI
Separate traffic by user IP, tenant, application, or stable API-key identity, not only by model.
Measure real token usage
Capture input, output, and total token counts carried in the AI response, then roll them up by tenant.
See the experience users receive
Track time to first token, end-to-end response time, inter-token latency, and tokens per second.
Find waste and contention
Identify aborted streams, truncated answers, over-allocation, noisy neighbors, and struggling workloads.
AI-Parser observes the request and response as delivered on the wire, creating an independent transaction record for AI usage and experience.
See the AI call, not just the GPU.
AI-Parser reconstructs OpenAI-compatible HTTP and JSON traffic, including streamed responses, to expose the information needed for cost, performance, governance, and operations.
One pass over the wire — cost, performance, and governance all read from the same record.
Turn shared GPU infrastructure into accountable services.
When the orchestration platform maps each tenant or application to an IP and GPU allocation, AI-Parser records align naturally with that model.
What AI-Parser adds beyond model-server metrics
Engine metrics stay the best source for GPU internals. AI-Parser adds identity, content, and delivered experience.
| Area | Model-server view | AI-Parser view | Why it matters |
|---|---|---|---|
| Identity | Aggregate, labelled by model | Client IP, tenant, stable API-key identity | Chargeback, SLA, and accountability per tenant |
| Prompt content | Never exposed | Parsed from the body — fingerprint or text | Governance, DLP, prompt-cache analysis |
| Tokens | Self-reported by the engine | Counted independently on the wire | An audit trail against mis-billing and drift |
| End-to-end latency | Request entry to completion | First request byte to last response byte | The latency the user actually experienced |
| Time to first token | Admit to first token | Request in to first byte out | Catches host stack, API server, and queue delay |
| Aborts and waste | Partial failure counters | TCP FIN/RST with no completion marker | Sees the client hang up, and attributes it |
Vendor-neutral: vLLM, TGI, Triton, TensorRT-LLM, Dynamo, and SGLang all speak the same OpenAI-compatible schema, so AI-Parser reads them the same way.
Bridging network, GPU, and application observability
Most AI infrastructure monitoring operates in separate domains:
AI-Parser is the correlation layer between them — particularly important for AI Clouds and GPU-as-a-Service providers, who need to tie network flows to tenant identity, workload type, and real-time performance signals rather than treating traffic as generic flows.
A different use case for every team, from one tap.
The same passive read of AI traffic answers a different question depending on who's asking — cost, performance, governance, streaming, or agentic-flow visibility.
Usage and token economics
Model, call type, request parameters, prompt tokens, completion tokens, total tokens, and request volume.
Performance as delivered
Time to first token, wire end-to-end time, inter-token latency, throughput, and stream completion.
Identity and privacy controls
Per-IP and per-tenant records, stable identity fingerprints, prompt hashing by default, and optional raw text.
Streaming-aware analysis
Reassemble token chunks, detect first and last response events, count SSE chunks, and recognize completion.
Agentic-flow visibility
Observe the multiple backend model calls created by one user question and regroup related calls through identity.
Honest, structured records
Read or compute fields from observed traffic. When a value is not present, leave it blank instead of guessing.
From “Is the GPU busy?” to “Is the GPU delivering AI value?”
Technical questions.
Does AI-Parser require instrumenting my application?
No. It observes traffic as delivered on the wire — there's nothing to instrument in the application or inference engine.
Where does it deploy?
As a passive tap on an x86 host or a BlueField-3 DPU, out of the data path — it reads mirrored traffic, not live production traffic.
Which inference engines does it support?
vLLM, TGI, Triton, TensorRT-LLM, Dynamo, and SGLang — all read the same way, since they share an OpenAI-compatible schema.
Does it store my users' prompts?
Prompts are converted to one-way fingerprints by default. Raw prompt text is retained only when explicitly enabled.
What happens to fields AI-Parser can't observe?
They're left blank — never guessed or fabricated.
How does this relate to my model server's own metrics?
They're complementary, not competing. Model-server metrics remain the best source for GPU internals; AI-Parser adds the identity, content, and delivered-experience context that's only visible on the wire.
Talk to us about licensing.
Pricing details coming soon
AI-Parser licensing terms are being finalized. Talk to an Aviz architect for current pricing and deployment options for your environment.