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Lambda Cloud (compute — co-located GPU fingerprint probe)

Advanced — a specialized probe, not a starting integration

Same niche as the RunPod probe: only for teams that operate a model endpoint and want to measure its timing exposure. New to Tulip integrations? Start with the action and evidence integrations — EDR, identity, SIEM, threat-intel.

Security capability — inference fingerprinting. Same defensive recon as the RunPod probe: rent a GPU next to a target endpoint, time how it streams tokens, and infer the model class, inference engine, and hardware — the first move of model extraction (MITRE ATLAS AML.T0040 — Inference API Access → AML.T0024 — Exfiltration via ML Inference API). Point it at endpoints you operate to measure your own exposure; the verdict is grounded, so a thin measurement abstains rather than guessing.

Lambda Cloud differs from RunPod in how it collects the result. Lambda has no "wait for pod output" call, so the probe pushes its feature JSON to a result sink that the launcher polls — an instance + result-sink model.

Install

pip install "tulip-integrations[compute-lambda]"   # httpx only — no extra SDK

At a glance
Env LAMBDA_API_KEY · LAMBDA_REGION (default us-east-1) · LAMBDA_PROBE_RESULT_URL (where the probe uploads its JSON)
Install tulip-integrations[compute-lambda] — no extra runtime dep (uses core httpx)
Import from tulip_integrations.compute.lambda_cloud import lambda_probe
Probe lambda_probe(endpoint) → timing feature vector
Grounded probe_to_finding(endpoint, provider="lambda") → GroundedFinding (FingerprintFinding, or Abstention)
ATLAS tags AML.T0040 (Inference API Access) · AML.T0024 (Exfiltration via Inference API)

How the Lambda lifecycle works

  1. POST /instance-operations/launch over httpx (Bearer auth) — boots a gpu_1x_h100_pcie instance named tulip-timing-probe in LAMBDA_REGION.
  2. The probe on the instance measures the endpoint's streaming timing and uploads its feature JSON to LAMBDA_PROBE_RESULT_URL (an S3 object or a small HTTP endpoint).
  3. The launcher polls that sink (30 attempts × 10s) until the vector lands.
  4. The instance is terminated in a finally (POST /instance-operations/terminate) so the bill stops even on error.
  5. fingerprint_to_finding(...) grounds the vector exactly as RunPod does.

You supply two things: the probe that runs on the instance, and the sink it uploads to (LAMBDA_PROBE_RESULT_URL). The feature schema is identical to RunPod:

{"ttft_ms_p50": 38.2, "itl_ms_mean": 11.4, "itl_cv": 0.07, "tps_mean": 87.6}

Example

from tulip_integrations.compute import probe_to_finding

f = probe_to_finding("https://my-endpoint/v1", provider="lambda")
print(f.verdict.model, "/", f.verdict.engine, "/", f.verdict.hardware)
# 7-8B class / vLLM (continuous-batching) / H100/A100 class

Grounding

The feature vector routes through core fingerprint_to_finding (shared with RunPod), so a thin measurement abstains rather than asserting a model identity.

Unverified live path

Only the offline sample path is exercised in CI. The launch → poll → terminate lifecycle is written to Lambda Cloud's documented API but has not been run against a live account; you also supply the probe and its result sink. Treat the live path as a reference implementation to validate in your own environment.

The live path is billable

A live run boots an H100-class instance. Gate it behind explicit approval and a spend limit, and rely on the finally teardown.

Defensive framing

Same as RunPod: a grounded, abstaining verdict means a thin measurement never asserts a model identity. Use it to audit your own exposure (AML.T0040 / AML.T0024).

→ RunPod probe · Integrations overview · Security & grounding