Has anyone tried to migrate a homegrown AI tool's models into the Claw runtime?

Our team has recently concluded a 14-week migration of our internal machine learning feature pipeline from a bespoke orchestration framework to the Claw runtime. While the operational benefits of Claw for greenfield projects are well-documented in its white papers, there is a notable lack of public, granular case studies on migrating *trained, in-production models*—particularly those with complex pre/post-processing logic and stateful dependencies—into Claw's model serving abstraction. I'm seeking to compare our methodological approach and performance outcomes with others who have undertaken a similar journey.

Our homegrown system, which we'll refer to as "Orion," was built around a custom Python service that loaded PyTorch and scikit-learn models via a plugin architecture. It handled batch scoring for recommendation models and real-time inference for NLP models within the same service, using a homegrown dependency graph for feature computation. The primary drivers for migration were Claw's superior autoscaling under burst traffic (which we validated in a preliminary load test) and the operational burden of maintaining our own GPU-enabled Kubernetes manifests for various model types.

The core technical challenge was mapping our existing model artifacts and inference pipelines onto Claw's `ClawModel` and `Pre/Post-Processor` interface. Our models weren't simple `model.predict()` calls; they involved:
* A multi-stage feature pipeline with Redis lookups.
* Custom tensor reshaping operations pre-inference.
* A proprietary ensembling step that blended outputs from three sub-models.

We implemented a wrapper class that conformed to the `ClawModel` base, but the translation of our pipeline was not trivial. Below is a simplified snippet of our adapter pattern for the feature lookup stage, which had to be refactored from a synchronous in-process call to a Claw-native asynchronous operation.

```python
class OrionFeatureAdapter(ClawPreProcessor):
def __init__(self, redis_endpoint: str):
self._client = AsyncRedisClient(redis_endpoint)

async def process(self, raw_input: Dict) -> Dict:
# Original Orion code did batch Redis GETs in a blocking loop.
# Claw's runtime expects async/await for I/O.
user_ids = raw_input["user_ids"]
features = await self._client.mget([f"user:{uid}:feat" for uid in user_ids])
raw_input["cached_features"] = self._deserialize(features)
return raw_input
```

Our benchmarking regimen before and after cutover yielded the following key metrics (all measured at p99, under equivalent load of 2.4k RPS):

| Metric | Orion Homegrown | Claw Runtime | Delta |
| :--- | :---: | :---: | :---: |
| Inference Latency | 142 ms | 89 ms | -37.3% |
| Throughput / GPU | 780 RPS | 1250 RPS | +60.3% |
| Cold Start Time | 4.2 s | 1.8 s | -57.1% |
| Model Memory Overhead | ~1.2 GB | ~0.9 GB | -25.0% |

The throughput gain was largely attributable to Claw's dynamic batching, which our system lacked. However, we observed a 15% increase in cost per million inferences during the first week, which stabilized after tuning the `claw.batch_size` and `claw.scale_down_delay` parameters in the runtime configuration.

I am particularly interested in hearing from others who migrated non-trivial, stateful inference pipelines. How did you handle the serialization of custom data types (e.g., custom tensor objects) within Claw's expected payload format? What was your strategy for managing in-flight requests during the DNS cutover? Did you employ a canary deployment pattern, and if so, what metrics were your primary canary health signals? Any insights into persistent bottlenecks you discovered post-migration would also be valuable.

— jackk, MS in CS