diff --git a/README.md b/README.md
index 6fd424f..2bff690 100644
--- a/README.md
+++ b/README.md
@@ -81,6 +81,9 @@ sc.pl.umap(adata, color='cytetype_annotation_clusters')
 🚀 [Try it in Google Colab](https://colab.research.google.com/drive/1aRLsI3mx8JR8u5BKHs48YUbLsqRsh2N7?usp=sharing)
 
 > **Note:** No API keys required for default configuration. See [custom LLM configuration](docs/configuration.md#llm-configuration) for advanced options.
+>
+> `run()` now handles artifact packaging and upload automatically (`vars.h5` + `obs.duckdb`) before annotation.
+> Generated artifact files are kept on disk by default; use `cleanup_artifacts=True` to remove them after run completion/failure.
 
 **Using R/Seurat?** → [CyteTypeR](https://github.com/NygenAnalytics/CyteTypeR)
 
diff --git a/cytetype/__init__.py b/cytetype/__init__.py
index 2ba78ed..2b8ad3e 100644
--- a/cytetype/__init__.py
+++ b/cytetype/__init__.py
@@ -1,4 +1,47 @@
+__version__ = "0.14.0"
+
+import requests
+
+from .config import logger
 from .main import CyteType
 
 __all__ = ["CyteType"]
-__version__ = "0.13.0"
+
+_PYPI_JSON_URL = "https://pypi.org/pypi/cytetype/json"
+
+
+def _parse_simple_version(value: str) -> tuple[int, ...] | None:
+    cleaned = value.lstrip("v")
+    parts = cleaned.split(".")
+    if not parts or any(not part.isdigit() for part in parts):
+        return None
+    return tuple(int(part) for part in parts)
+
+
+def _warn_if_update_available() -> None:
+    current_version = _parse_simple_version(__version__)
+    if current_version is None:
+        return
+
+    try:
+        response = requests.get(_PYPI_JSON_URL, timeout=2)
+        response.raise_for_status()
+        latest_raw = response.json().get("info", {}).get("version")
+        if not isinstance(latest_raw, str):
+            return
+        latest_version = _parse_simple_version(latest_raw)
+        if latest_version is None:
+            return
+        if latest_version > current_version:
+            logger.warning(
+                "A newer CyteType version is available on PyPI ({} -> {}). "
+                "Run `pip install -U cytetype` to update.",
+                __version__,
+                latest_raw,
+            )
+    except Exception:
+        # Best-effort check: stay silent on all failures (offline, timeout, parsing, etc).
+        return
+
+
+_warn_if_update_available()
diff --git a/cytetype/api/__init__.py b/cytetype/api/__init__.py
index db8bce3..d6a29ef 100644
--- a/cytetype/api/__init__.py
+++ b/cytetype/api/__init__.py
@@ -6,7 +6,14 @@
     fetch_job_results,
     wait_for_completion,
 )
-from .schemas import LLMModelConfig, InputData
+from .schemas import (
+    LLMModelConfig,
+    InputData,
+    AnnotateRequest,
+    UploadResponse,
+    UploadedFiles,
+    UploadFileKind,
+)
 from .exceptions import (
     CyteTypeError,
     APIError,
@@ -29,6 +36,10 @@
     # Schemas
     "LLMModelConfig",
     "InputData",
+    "AnnotateRequest",
+    "UploadResponse",
+    "UploadedFiles",
+    "UploadFileKind",
     # Exceptions
     "CyteTypeError",
     "APIError",
diff --git a/cytetype/api/client.py b/cytetype/api/client.py
index 09f76dd..0d29f73 100644
--- a/cytetype/api/client.py
+++ b/cytetype/api/client.py
@@ -1,12 +1,68 @@
 import time
+from pathlib import Path
 from typing import Any
 
 from .transport import HTTPTransport
 from .progress import ProgressDisplay
 from .exceptions import JobFailedError, TimeoutError, APIError
+from .schemas import UploadResponse, UploadFileKind
 from ..config import logger
 
 
+MAX_UPLOAD_BYTES: dict[UploadFileKind, int] = {
+    "obs_duckdb": 100 * 1024 * 1024,  # 100MB
+    "vars_h5": 10 * 1024 * 1024 * 1024,  # 10GB
+}
+
+
+def _upload_file(
+    base_url: str,
+    auth_token: str | None,
+    file_kind: UploadFileKind,
+    file_path: str,
+    timeout: float | tuple[float, float] = (30.0, 3600.0),
+) -> UploadResponse:
+    path_obj = Path(file_path)
+    if not path_obj.is_file():
+        raise FileNotFoundError(f"Upload file not found: {path_obj}")
+
+    size_bytes = path_obj.stat().st_size
+    max_size = MAX_UPLOAD_BYTES[file_kind]
+    if size_bytes > max_size:
+        raise ValueError(
+            f"{file_kind} exceeds upload limit: {size_bytes} bytes > {max_size} bytes"
+        )
+
+    transport = HTTPTransport(base_url, auth_token)
+    with path_obj.open("rb") as f:
+        _, response = transport.post_binary(
+            f"upload/{file_kind}",
+            data=f,
+            timeout=timeout,
+        )
+    return UploadResponse(**response)
+
+
+def upload_obs_duckdb(
+    base_url: str,
+    auth_token: str | None,
+    file_path: str,
+    timeout: float | tuple[float, float] = (30.0, 3600.0),
+) -> UploadResponse:
+    """Upload obs duckdb file and return upload metadata."""
+    return _upload_file(base_url, auth_token, "obs_duckdb", file_path, timeout=timeout)
+
+
+def upload_vars_h5(
+    base_url: str,
+    auth_token: str | None,
+    file_path: str,
+    timeout: float | tuple[float, float] = (30.0, 3600.0),
+) -> UploadResponse:
+    """Upload vars h5 file and return upload metadata."""
+    return _upload_file(base_url, auth_token, "vars_h5", file_path, timeout=timeout)
+
+
 def submit_annotation_job(
     base_url: str,
     auth_token: str | None,
diff --git a/cytetype/api/schemas.py b/cytetype/api/schemas.py
index cd4eac2..45ec539 100644
--- a/cytetype/api/schemas.py
+++ b/cytetype/api/schemas.py
@@ -1,7 +1,15 @@
-from typing import Any, TypeAlias, Literal
+from importlib.metadata import PackageNotFoundError, version
+from typing import Any, Literal, TypeAlias
 from pydantic import BaseModel, Field, model_validator
 
 
+def _get_client_version() -> str | None:
+    try:
+        return version("cytetype")
+    except PackageNotFoundError:
+        return None
+
+
 LLMProvider: TypeAlias = Literal[
     "anthropic",
     "bedrock",
@@ -18,6 +26,7 @@
 AgentType: TypeAlias = Literal[
     "contextualizer", "annotator", "reviewer", "summarizer", "clinician", "chat"
 ]
+UploadFileKind: TypeAlias = Literal["obs_duckdb", "vars_h5"]
 
 
 class LLMModelConfig(BaseModel):
@@ -64,6 +73,16 @@ def check_aws_credentials(self) -> "LLMModelConfig":
         raise ValueError("Either apiKey or all AWS credentials must be provided")
 
 
+class ClientInfo(BaseModel):
+    clientType: Literal["anndata"] = Field(
+        default="anndata", description="The type of client that is using the API"
+    )
+    clientVersion: str | None = Field(
+        default_factory=_get_client_version,
+        description="The version of the client that is using the API",
+    )
+
+
 class InputData(BaseModel):
     studyInfo: str = Field(
         default="",
@@ -99,6 +118,10 @@ class InputData(BaseModel):
         le=50,
         description="Number of parallel requests to make to the model",
     )
+    clientInfo: ClientInfo = Field(
+        default_factory=ClientInfo,
+        description="Client information",
+    )
 
     @classmethod
     def get_example(cls) -> "InputData":
@@ -171,6 +194,31 @@ def get_example(cls) -> "InputData":
         )
 
 
+class UploadedFiles(BaseModel):
+    obs_duckdb: str | None = None
+    vars_h5: str | None = None
+
+    @model_validator(mode="after")
+    def validate_at_least_one_reference(self) -> "UploadedFiles":
+        if self.obs_duckdb is None and self.vars_h5 is None:
+            raise ValueError("At least one uploaded file reference must be provided")
+        return self
+
+
+class AnnotateRequest(BaseModel):
+    input_data: InputData
+    llm_configs: list[LLMModelConfig] | None = None
+    uploaded_files: UploadedFiles | None = None
+
+
+class UploadResponse(BaseModel):
+    upload_id: str
+    file_kind: UploadFileKind
+    file_name: str
+    size_bytes: int
+    expires_at: str
+
+
 # New schemas for API responses
 class ErrorResponse(BaseModel):
     """Standard error response from CyteType API."""
diff --git a/cytetype/api/transport.py b/cytetype/api/transport.py
index 9f08649..3e15ca9 100644
--- a/cytetype/api/transport.py
+++ b/cytetype/api/transport.py
@@ -1,5 +1,5 @@
 import requests
-from typing import Any
+from typing import Any, BinaryIO
 
 from .exceptions import create_api_exception, NetworkError, TimeoutError
 from .schemas import ErrorResponse
@@ -13,13 +13,13 @@ def __init__(self, base_url: str, auth_token: str | None = None):
         self.auth_token = auth_token
         self.session = requests.Session()
 
-    def _build_headers(self, content_type: bool = False) -> dict[str, str]:
+    def _build_headers(self, content_type: str | None = None) -> dict[str, str]:
         """Build request headers with auth token."""
         headers = {}
         if self.auth_token:
             headers["Authorization"] = f"Bearer {self.auth_token}"
         if content_type:
-            headers["Content-Type"] = "application/json"
+            headers["Content-Type"] = content_type
         return headers
 
     def _parse_error(self, response: requests.Response) -> None:
@@ -61,7 +61,33 @@ def post(
             response = self.session.post(
                 url,
                 json=data,
-                headers=self._build_headers(content_type=True),
+                headers=self._build_headers(content_type="application/json"),
+                timeout=timeout,
+            )
+
+            if not response.ok:
+                self._parse_error(response)
+
+            return response.status_code, response.json()
+
+        except requests.RequestException as e:
+            self._handle_request_error(e)
+            raise  # For type checker
+
+    def post_binary(
+        self,
+        endpoint: str,
+        data: bytes | BinaryIO,
+        timeout: float | tuple[float, float] = (30.0, 3600.0),
+    ) -> tuple[int, dict[str, Any]]:
+        """Make POST request with raw binary body (application/octet-stream)."""
+        url = f"{self.base_url}/{endpoint.lstrip('/')}"
+
+        try:
+            response = self.session.post(
+                url,
+                data=data,
+                headers=self._build_headers(content_type="application/octet-stream"),
                 timeout=timeout,
             )
 
diff --git a/cytetype/core/artifacts.py b/cytetype/core/artifacts.py
new file mode 100644
index 0000000..a70b757
--- /dev/null
+++ b/cytetype/core/artifacts.py
@@ -0,0 +1,107 @@
+import re
+from typing import Any
+
+import duckdb
+import h5py
+import hdf5plugin
+import numpy as np
+import pandas as pd
+import scipy.sparse as sp
+from anndata.abc import CSCDataset, CSRDataset
+
+from ..config import logger
+
+
+def save_features_matrix(
+    out_file: str,
+    mat: Any,
+    min_chunk_size: int = 10_000_000,
+    col_batch: int | None = None,
+) -> None:
+    """Persist normalized count matrix in the vars.h5 format expected by the API."""
+    if not hasattr(mat, "shape") or not hasattr(mat, "__getitem__"):
+        raise TypeError(
+            "mat must be an array-like matrix with shape and slicing support"
+        )
+
+    n_rows, n_cols = mat.shape
+
+    if not isinstance(mat, (CSRDataset, CSCDataset)):
+        logger.warning(
+            "For large datasets, use AnnData backed mode (e.g. sc.read_h5ad(..., backed='r')) "
+            "so `adata.X` is a backed sparse dataset and avoids loading the full matrix in memory.",
+        )
+
+    if col_batch is None:
+        col_batch = max(1, int(100_000_000 / max(n_rows, 1)))
+
+    chunk_size = max(1, min(n_rows * 10, min_chunk_size))
+    with h5py.File(out_file, "w") as f:
+        group = f.create_group("vars")
+        group.attrs["n_obs"] = n_rows
+        group.attrs["n_vars"] = n_cols
+
+        d_indices = group.create_dataset(
+            "indices",
+            shape=(0,),
+            maxshape=(n_rows * n_cols,),
+            chunks=(chunk_size,),
+            dtype=np.int32,
+            compression=hdf5plugin.LZ4(),
+        )
+        d_data = group.create_dataset(
+            "data",
+            shape=(0,),
+            maxshape=(n_rows * n_cols,),
+            chunks=(chunk_size,),
+            dtype=np.float32,
+            compression=hdf5plugin.LZ4(),
+        )
+
+        indptr: list[int] = [0]
+        for start in range(0, n_cols, col_batch):
+            end = min(start + col_batch, n_cols)
+            raw_chunk = mat[:, start:end]
+            chunk = (
+                raw_chunk.tocsc()
+                if sp.issparse(raw_chunk)
+                else sp.csc_matrix(raw_chunk)
+            )
+
+            old_size = d_indices.shape[0]
+            chunk_indices = chunk.indices.astype(np.int32, copy=False)
+            chunk_data = chunk.data.astype(np.float32, copy=False)
+
+            d_indices.resize(old_size + len(chunk_indices), axis=0)
+            d_indices[old_size : old_size + len(chunk_indices)] = chunk_indices
+
+            d_data.resize(old_size + len(chunk_data), axis=0)
+            d_data[old_size : old_size + len(chunk_data)] = chunk_data
+
+            indptr.extend((chunk.indptr[1:] + indptr[-1]).tolist())
+
+        group.create_dataset("indptr", data=np.asarray(indptr, dtype=np.int64))
+
+
+def save_obs_duckdb(
+    out_file: str,
+    obs_df: pd.DataFrame,
+    table_name: str = "obs",
+    threads: int = 4,
+    memory_limit: str = "4GB",
+    temp_directory: str = "/tmp/duckdb",
+) -> None:
+    """Persist adata.obs to a DuckDB table."""
+    if not re.fullmatch(r"[A-Za-z_][A-Za-z0-9_]*", table_name):
+        raise ValueError(
+            "Invalid table_name. Use letters, numbers, and underscores only."
+        )
+
+    dd_config: dict[str, Any] = {
+        "threads": threads,
+        "memory_limit": memory_limit,
+        "temp_directory": temp_directory,
+    }
+    with duckdb.connect(out_file, config=dd_config) as con:
+        con.register("obs_df", obs_df)
+        con.execute(f"CREATE OR REPLACE TABLE {table_name} AS SELECT * FROM obs_df")
diff --git a/cytetype/core/payload.py b/cytetype/core/payload.py
index 8e90685..219ba13 100644
--- a/cytetype/core/payload.py
+++ b/cytetype/core/payload.py
@@ -2,7 +2,7 @@
 import json
 from pydantic import ValidationError
 
-from ..api import LLMModelConfig, InputData
+from ..api import LLMModelConfig, InputData, UploadedFiles
 from ..config import logger
 
 
@@ -16,6 +16,7 @@ def build_annotation_payload(
     expression_percentages: dict[str, dict[str, float]],
     n_parallel_clusters: int,
     llm_configs: list[dict[str, Any]] | None = None,
+    uploaded_files: dict[str, str | None] | UploadedFiles | None = None,
 ) -> dict[str, Any]:
     """Build and validate API payload.
 
@@ -29,9 +30,10 @@ def build_annotation_payload(
         expression_percentages: Gene expression percentages
         n_parallel_clusters: Number of parallel requests
         llm_configs: Optional LLM configurations
+        uploaded_files: Optional upload IDs for obs_duckdb / vars_h5
 
     Returns:
-        Validated payload dict with input_data and llm_configs
+        Validated payload dict for /annotate
 
     Raises:
         ValidationError: If payload validation fails
@@ -57,12 +59,10 @@ def build_annotation_payload(
         raise
 
     # Validate llm_configs
-    validated_llm_configs = []
+    validated_llm_configs: list[LLMModelConfig] = []
     if llm_configs:
         try:
-            validated_llm_configs = [
-                LLMModelConfig(**config).model_dump() for config in llm_configs
-            ]
+            validated_llm_configs = [LLMModelConfig(**config) for config in llm_configs]
         except ValidationError:
             logger.error(
                 "LLM configuration validation failed. Please check your llm_configs parameter. "
@@ -71,10 +71,33 @@ def build_annotation_payload(
             )
             raise
 
-    return {
+    validated_uploaded_files: UploadedFiles | None = None
+    if uploaded_files is not None:
+        try:
+            if isinstance(uploaded_files, UploadedFiles):
+                validated_uploaded_files = uploaded_files
+            else:
+                validated_uploaded_files = UploadedFiles(**uploaded_files)
+        except ValidationError:
+            logger.error(
+                "Uploaded file reference validation failed. Expected one or both "
+                "of: obs_duckdb, vars_h5."
+            )
+            raise
+
+    payload: dict[str, Any] = {
         "input_data": input_data,
-        "llm_configs": validated_llm_configs if validated_llm_configs else None,
+        "llm_configs": (
+            [config.model_dump() for config in validated_llm_configs]
+            if validated_llm_configs
+            else None
+        ),
     }
+    if validated_uploaded_files:
+        payload["uploaded_files"] = validated_uploaded_files.model_dump(
+            exclude_none=True
+        )
+    return payload
 
 
 def save_query_to_file(input_data: dict[str, Any], filename: str) -> None:
diff --git a/cytetype/main.py b/cytetype/main.py
index 8899fca..af606f3 100644
--- a/cytetype/main.py
+++ b/cytetype/main.py
@@ -1,10 +1,16 @@
+from pathlib import Path
 from typing import Any
+from importlib.metadata import PackageNotFoundError, version
 
 import anndata
 from natsort import natsorted
 
 from .config import logger
 from .api import submit_annotation_job, wait_for_completion
+from .api.client import (
+    upload_obs_duckdb as upload_obs_duckdb_file,
+    upload_vars_h5 as upload_vars_h5_file,
+)
 from .preprocessing import (
     validate_adata,
     aggregate_expression_percentages,
@@ -13,6 +19,10 @@
     extract_visualization_coordinates,
 )
 from .core.payload import build_annotation_payload, save_query_to_file
+from .core.artifacts import (
+    save_features_matrix,
+    save_obs_duckdb as save_obs_duckdb_file,
+)
 from .core.results import (
     store_job_details,
     store_annotations,
@@ -23,6 +33,13 @@
 __all__ = ["CyteType"]
 
 
+def _get_cytetype_version() -> str | None:
+    try:
+        return version("cytetype")
+    except PackageNotFoundError:
+        return None
+
+
 class CyteType:
     """CyteType class for characterizing clusters from single-cell RNA-seq data.
 
@@ -46,6 +63,7 @@ class CyteType:
     marker_genes: dict[str, list[str]]
     group_metadata: dict[str, dict[str, dict[str, int]]]
     visualization_data: dict[str, Any]
+    __version__: str | None = _get_cytetype_version()
 
     def __init__(
         self,
@@ -175,6 +193,62 @@ def __init__(
 
         logger.info("Data preparation completed. Ready for submitting jobs.")
 
+    def _build_and_upload_artifacts(
+        self,
+        vars_h5_path: str,
+        obs_duckdb_path: str,
+        upload_timeout_seconds: int,
+    ) -> dict[str, str]:
+        """Build local artifacts and upload them before annotate."""
+        logger.info("Saving vars.h5 artifact from normalized counts...")
+        save_features_matrix(
+            out_file=vars_h5_path,
+            mat=self.adata.X,
+        )
+
+        logger.info("Saving obs.duckdb artifact from observation metadata...")
+        save_obs_duckdb_file(
+            out_file=obs_duckdb_path,
+            obs_df=self.adata.obs,
+        )
+
+        logger.info("Uploading obs.duckdb artifact...")
+        obs_upload = upload_obs_duckdb_file(
+            self.api_url,
+            self.auth_token,
+            obs_duckdb_path,
+            timeout=(30.0, float(upload_timeout_seconds)),
+        )
+        if obs_upload.file_kind != "obs_duckdb":
+            raise ValueError(
+                f"Unexpected upload file_kind for obs artifact: {obs_upload.file_kind}"
+            )
+
+        logger.info("Uploading vars.h5 artifact...")
+        vars_upload = upload_vars_h5_file(
+            self.api_url,
+            self.auth_token,
+            vars_h5_path,
+            timeout=(30.0, float(upload_timeout_seconds)),
+        )
+        if vars_upload.file_kind != "vars_h5":
+            raise ValueError(
+                f"Unexpected upload file_kind for vars artifact: {vars_upload.file_kind}"
+            )
+
+        return {
+            "obs_duckdb": obs_upload.upload_id,
+            "vars_h5": vars_upload.upload_id,
+        }
+
+    @staticmethod
+    def _cleanup_artifact_files(paths: list[str]) -> None:
+        for artifact_path in paths:
+            try:
+                Path(artifact_path).unlink(missing_ok=True)
+            except OSError as exc:
+                logger.warning(f"Failed to cleanup artifact {artifact_path}: {exc}")
+
     def run(
         self,
         study_context: str,
@@ -188,6 +262,10 @@ def run(
         auth_token: str | None = None,
         save_query: bool = True,
         query_filename: str = "query.json",
+        vars_h5_path: str = "vars.h5",
+        obs_duckdb_path: str = "obs.duckdb",
+        upload_timeout_seconds: int = 3600,
+        cleanup_artifacts: bool = False,
         show_progress: bool = True,
         override_existing_results: bool = False,
     ) -> anndata.AnnData:
@@ -222,6 +300,14 @@ def run(
             save_query (bool, optional): Whether to save the query to a file. Defaults to True.
             query_filename (str, optional): Filename for saving the query when save_query is True.
                 Defaults to "query.json".
+            vars_h5_path (str, optional): Local output path for generated vars.h5 artifact.
+                Defaults to "vars.h5".
+            obs_duckdb_path (str, optional): Local output path for generated obs.duckdb artifact.
+                Defaults to "obs.duckdb".
+            upload_timeout_seconds (int, optional): Socket read timeout used for each artifact upload.
+                Defaults to 3600.
+            cleanup_artifacts (bool, optional): Whether to delete generated artifact files after run
+                completes or fails. Defaults to False.
             show_progress (bool, optional): Whether to display progress updates with spinner and
                 cluster status. Set to False to disable all visual progress output. Defaults to True.
             override_existing_results (bool, optional): Whether to override existing results with the
@@ -256,8 +342,10 @@ def run(
             self.api_url = api_url.strip("/")
         if auth_token:
             self.auth_token = auth_token
+        if upload_timeout_seconds <= 0:
+            raise ValueError("upload_timeout_seconds must be greater than 0")
 
-        # Build and validate payload
+        # Validate the base payload before doing potentially expensive uploads.
         payload = build_annotation_payload(
             study_context,
             metadata,
@@ -270,36 +358,48 @@ def run(
             llm_configs,
         )
 
-        # Save query if requested
-        if save_query:
-            save_query_to_file(payload["input_data"], query_filename)
-
-        # Submit job and store details
-        job_id = submit_annotation_job(self.api_url, self.auth_token, payload)
-        store_job_details(self.adata, job_id, self.api_url, results_prefix)
-
-        # Wait for completion
-        result = wait_for_completion(
-            self.api_url,
-            self.auth_token,
-            job_id,
-            poll_interval_seconds,
-            timeout_seconds,
-            show_progress,
-        )
+        artifact_paths = [vars_h5_path, obs_duckdb_path]
+        try:
+            uploaded_file_refs = self._build_and_upload_artifacts(
+                vars_h5_path=vars_h5_path,
+                obs_duckdb_path=obs_duckdb_path,
+                upload_timeout_seconds=upload_timeout_seconds,
+            )
+            payload["uploaded_files"] = uploaded_file_refs
+
+            # Save query if requested
+            if save_query:
+                save_query_to_file(payload["input_data"], query_filename)
+
+            # Submit job and store details
+            job_id = submit_annotation_job(self.api_url, self.auth_token, payload)
+            store_job_details(self.adata, job_id, self.api_url, results_prefix)
+
+            # Wait for completion
+            result = wait_for_completion(
+                self.api_url,
+                self.auth_token,
+                job_id,
+                poll_interval_seconds,
+                timeout_seconds,
+                show_progress,
+            )
 
-        # Store results
-        store_annotations(
-            self.adata,
-            result,
-            job_id,
-            results_prefix,
-            self.group_key,
-            self.clusters,
-            check_unannotated=True,
-        )
+            # Store results
+            store_annotations(
+                self.adata,
+                result,
+                job_id,
+                results_prefix,
+                self.group_key,
+                self.clusters,
+                check_unannotated=True,
+            )
 
-        return self.adata
+            return self.adata
+        finally:
+            if cleanup_artifacts:
+                self._cleanup_artifact_files(artifact_paths)
 
     def get_results(
         self,
diff --git a/docs/configuration.md b/docs/configuration.md
index 075d575..0321ace 100644
--- a/docs/configuration.md
+++ b/docs/configuration.md
@@ -29,6 +29,14 @@ adata = annotator.run(
 )
 ```
 
+`run()` now performs the full upload pipeline internally:
+- Creates `vars.h5` from `adata.X`
+- Creates `obs.duckdb` from `adata.obs`
+- Uploads both artifacts to the CyteType API
+- Calls `/annotate` with uploaded file references
+
+If artifact creation or upload fails, `run()` fails fast.
+
 ## LLM Configuration
 You can provide your own LLM providers/models:
 ```python
@@ -63,5 +71,19 @@ adata = annotator.run(
     poll_interval_seconds=30,   # How often to poll (default)
     timeout_seconds=7200,       # Max wait time (default: 2 hours)
     api_url="https://custom-api.example.com",  # Custom API endpoint if needed
+    vars_h5_path="vars.h5",     # Local artifact output path
+    obs_duckdb_path="obs.duckdb",  # Local artifact output path
+    upload_timeout_seconds=3600,   # Per-upload socket read timeout
+    cleanup_artifacts=False,       # Keep artifacts by default
 )
-``` 
\ No newline at end of file
+```
+
+### Memory Recommendation for Large Datasets
+
+For large datasets, open your AnnData object in backed mode to reduce memory usage while building `vars.h5`:
+
+```python
+import scanpy as sc
+
+adata = sc.read_h5ad("input.h5ad", backed="r")
+```
\ No newline at end of file
diff --git a/docs/troubleshooting.md b/docs/troubleshooting.md
index 85b6fa7..56cc768 100644
--- a/docs/troubleshooting.md
+++ b/docs/troubleshooting.md
@@ -3,4 +3,6 @@
 - Rate limit responses include retry information; wait or provide your own LLM.
 - Verify preprocessing: clustering and `rank_genes_groups` must be present. 
 - Make sure you have valid gene symbols in the AnnData object and are passing the correct gene symbols column name to parameter `gene_symbols_column`.
-- If you are using a custom LLM, make sure you have the correct API key and base URL.
\ No newline at end of file
+- If you are using a custom LLM, make sure you have the correct API key and base URL.
+- For large datasets, load AnnData in backed mode (`sc.read_h5ad(..., backed="r")`) to reduce memory use during artifact generation.
+- `run()` creates `vars.h5` and `obs.duckdb` before annotation. Use `cleanup_artifacts=True` if you do not want to keep these local files.
\ No newline at end of file
diff --git a/pyproject.toml b/pyproject.toml
index b903d17..4b86c78 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -5,13 +5,15 @@ description = "Python client for characterization of clusters from single-cell R
 readme = "README.md"
 requires-python = ">=3.12"
 dependencies = [
-    "anndata~=0.12.7",
+    "anndata~=0.12.10",
     "loguru~=0.7.3",
     "natsort~=8.4.0",
     "requests~=2.32.5",
     "pydantic~=2.12.5",
     "session-info~=1.0.1",
     "python-dotenv~=1.2.1",
+    "duckdb>=1.4.4",
+    "hdf5plugin>=6.0.0",
 ]
 authors = [
     {name = "Parashar Dhapola", email = "parashar@nygen.io"}
@@ -26,11 +28,11 @@ Repository = "https://github.com/NygenAnalytics/cytetype"
 
 [dependency-groups]
 dev = [
-    "jupyterlab~=4.4.6",
+    "jupyterlab~=4.5.4",
     "mypy~=1.19.1",
     "pytest~=9.0.2",
     "ruff~=0.14.11",
-    "scanpy~=1.11.5",
+    "scanpy~=1.12",
     "igraph~=1.0.0",
     "leidenalg~=0.11.0",
     "types-requests~=2.32.4.20260107",
diff --git a/tests/test_cytetype_integration.py b/tests/test_cytetype_integration.py
index 795f49a..5f4f55c 100644
--- a/tests/test_cytetype_integration.py
+++ b/tests/test_cytetype_integration.py
@@ -1,6 +1,7 @@
 """Integration tests for CyteType class."""
 
 import pytest
+from pathlib import Path
 from unittest.mock import patch, MagicMock
 from pydantic import ValidationError
 from typing import Any
@@ -8,6 +9,7 @@
 
 from cytetype import CyteType
 from cytetype.api.exceptions import RateLimitError, AuthenticationError
+from cytetype.api import UploadResponse
 
 
 def test_fixture_works(mock_adata: anndata.AnnData) -> None:
@@ -41,6 +43,40 @@ def test_cytetype_initialization(mock_adata: anndata.AnnData) -> None:
     assert ct.visualization_data["coordinates"] is not None
 
 
+@pytest.fixture(autouse=True)
+def mock_internal_artifact_pipeline(monkeypatch: pytest.MonkeyPatch) -> None:
+    """Avoid file and network work for run() in tests by mocking internals."""
+
+    def _save_vars(*args: Any, **kwargs: Any) -> None:
+        return None
+
+    def _save_obs(*args: Any, **kwargs: Any) -> None:
+        return None
+
+    def _upload_obs(*args: Any, **kwargs: Any) -> UploadResponse:
+        return UploadResponse(
+            upload_id="obs_upload_123",
+            file_kind="obs_duckdb",
+            file_name="obs.duckdb",
+            size_bytes=100,
+            expires_at="2099-01-01T00:00:00Z",
+        )
+
+    def _upload_vars(*args: Any, **kwargs: Any) -> UploadResponse:
+        return UploadResponse(
+            upload_id="vars_upload_456",
+            file_kind="vars_h5",
+            file_name="vars.h5",
+            size_bytes=100,
+            expires_at="2099-01-01T00:00:00Z",
+        )
+
+    monkeypatch.setattr("cytetype.main.save_features_matrix", _save_vars)
+    monkeypatch.setattr("cytetype.main.save_obs_duckdb_file", _save_obs)
+    monkeypatch.setattr("cytetype.main.upload_obs_duckdb_file", _upload_obs)
+    monkeypatch.setattr("cytetype.main.upload_vars_h5_file", _upload_vars)
+
+
 @patch("cytetype.main.wait_for_completion")
 @patch("cytetype.main.submit_annotation_job")
 def test_cytetype_run_success(
@@ -61,6 +97,11 @@ def test_cytetype_run_success(
     # Verify API calls made
     mock_submit.assert_called_once()
     mock_wait.assert_called_once()
+    payload = mock_submit.call_args.args[2]
+    assert payload["uploaded_files"] == {
+        "obs_duckdb": "obs_upload_123",
+        "vars_h5": "vars_upload_456",
+    }
 
     # Verify result is the modified adata
     assert result_adata is mock_adata
@@ -85,6 +126,67 @@ def test_cytetype_run_success(
     assert result_adata.uns["cytetype_jobDetails"]["job_id"] == "test_job_123"
 
 
+@patch("cytetype.main.wait_for_completion")
+@patch("cytetype.main.submit_annotation_job")
+def test_cytetype_run_auto_uploads_artifacts(
+    mock_submit: MagicMock,
+    mock_wait: MagicMock,
+    mock_adata: anndata.AnnData,
+    mock_api_response: dict[str, Any],
+) -> None:
+    """Test run() auto-creates upload references in /annotate payload."""
+    mock_submit.return_value = "job_with_uploads"
+    mock_wait.return_value = mock_api_response
+
+    ct = CyteType(mock_adata, group_key="leiden")
+    ct.run(study_context="Test")
+
+    call_args = mock_submit.call_args.args
+    payload = call_args[2]
+    assert payload["uploaded_files"] == {
+        "obs_duckdb": "obs_upload_123",
+        "vars_h5": "vars_upload_456",
+    }
+
+
+@patch("cytetype.main.wait_for_completion")
+@patch("cytetype.main.submit_annotation_job")
+def test_cytetype_run_cleanup_artifacts(
+    mock_submit: MagicMock,
+    mock_wait: MagicMock,
+    mock_adata: anndata.AnnData,
+    mock_api_response: dict[str, Any],
+    tmp_path: Path,
+    monkeypatch: pytest.MonkeyPatch,
+) -> None:
+    """Test run() can cleanup generated artifact files when requested."""
+    mock_submit.return_value = "job_cleanup"
+    mock_wait.return_value = mock_api_response
+
+    vars_path = tmp_path / "vars.h5"
+    obs_path = tmp_path / "obs.duckdb"
+
+    def _save_vars(*args: Any, **kwargs: Any) -> None:
+        Path(kwargs["out_file"]).write_bytes(b"vars")
+
+    def _save_obs(*args: Any, **kwargs: Any) -> None:
+        Path(kwargs["out_file"]).write_bytes(b"obs")
+
+    monkeypatch.setattr("cytetype.main.save_features_matrix", _save_vars)
+    monkeypatch.setattr("cytetype.main.save_obs_duckdb_file", _save_obs)
+
+    ct = CyteType(mock_adata, group_key="leiden")
+    ct.run(
+        study_context="Test",
+        vars_h5_path=str(vars_path),
+        obs_duckdb_path=str(obs_path),
+        cleanup_artifacts=True,
+    )
+
+    assert not vars_path.exists()
+    assert not obs_path.exists()
+
+
 @patch("cytetype.main.wait_for_completion")
 @patch("cytetype.main.submit_annotation_job")
 def test_cytetype_override_existing_results(
diff --git a/uv.lock b/uv.lock
index 31c40ea..5a4abd9 100644
--- a/uv.lock
+++ b/uv.lock
@@ -8,7 +8,7 @@ resolution-markers = [
 
 [[package]]
 name = "anndata"
-version = "0.12.7"
+version = "0.12.10"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "array-api-compat" },
@@ -21,9 +21,9 @@ dependencies = [
     { name = "scipy" },
     { name = "zarr" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d8/64/ea6da6d88c0b5ad3231828a8ab895cec871ff965e626e4986bc4dfae053d/anndata-0.12.7.tar.gz", hash = "sha256:10612d476e78570be2fdd391b09cb64d3b33cda32b1b46a0a4b999ba98d64d47", size = 2248853, upload-time = "2025-12-16T13:47:14.246Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/84/81/29809c5710123bb37ea9d9de9da0e83dda5be9d8419cce256e4406b37c44/anndata-0.12.10.tar.gz", hash = "sha256:73a73c99ca50400eb9dc7f2fdd400cf677ea4bb9ef1f7c04691c0fc557e43d7f", size = 2254675, upload-time = "2026-02-06T14:02:24.716Z" }
 wheels = [
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+    { url = "https://files.pythonhosted.org/packages/4c/f4/4d0193dc5bab3af74e9560a8b45830d88ac707467d15ceff7e3df17adc41/anndata-0.12.10-py3-none-any.whl", hash = "sha256:e3d940d8e34373dc250f998c1011c1da52721f980de9d83a0599daa2baa286e5", size = 176574, upload-time = "2026-02-06T14:02:23.097Z" },
 ]
 
 [[package]]
@@ -37,16 +37,15 @@ wheels = [
 
 [[package]]
 name = "anyio"
-version = "4.10.0"
+version = "4.12.1"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "idna" },
-    { name = "sniffio" },
     { name = "typing-extensions", marker = "python_full_version < '3.13'" },
 ]
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+sdist = { url = "https://files.pythonhosted.org/packages/96/f0/5eb65b2bb0d09ac6776f2eb54adee6abe8228ea05b20a5ad0e4945de8aac/anyio-4.12.1.tar.gz", hash = "sha256:41cfcc3a4c85d3f05c932da7c26d0201ac36f72abd4435ba90d0464a3ffed703", size = 228685, upload-time = "2026-01-06T11:45:21.246Z" }
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+    { url = "https://files.pythonhosted.org/packages/38/0e/27be9fdef66e72d64c0cdc3cc2823101b80585f8119b5c112c2e8f5f7dab/anyio-4.12.1-py3-none-any.whl", hash = "sha256:d405828884fc140aa80a3c667b8beed277f1dfedec42ba031bd6ac3db606ab6c", size = 113592, upload-time = "2026-01-06T11:45:19.497Z" },
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 [[package]]
@@ -103,85 +102,85 @@ wheels = [
 
 [[package]]
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+version = "1.13.0"
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+    { url = "https://files.pythonhosted.org/packages/df/5d/493b1b5528ab5072feae30821ff3a07b7a0474213d548efb1fdf135f85c1/array_api_compat-1.13.0-py3-none-any.whl", hash = "sha256:c15026a0ddec42815383f07da285472e1b1ff2e632eb7afbcfe9b08fcbad9bf1", size = 58585, upload-time = "2025-12-28T11:26:56.081Z" },
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 [[package]]
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-version = "1.3.0"
+version = "1.4.0"
 source = { registry = "https://pypi.org/simple" }
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-    { name = "types-python-dateutil" },
+    { name = "tzdata" },
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 [[package]]
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 [[package]]
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