You're paying for the same tokens twice. Every API call reloads the same context: system prompts, docs, history. Charged in full. Every time. Context caching in Model Studio fixes that. Learn how today 👇 #AlibabaCloud# #ModelStudio# #CostOptimization#

# Snowflake Features and Practical Usage 🚀 "Bumping the size up makes it faster, but what about cost?" Snowflake cost optimization hinges on answering that question correctly. Let's master virtual warehouse sizing and auto-suspend. 📌 Title and Feature URL Title: Working with Virtual Warehouses URL: 📝 Overview A virtual warehouse is a cluster of compute resources that supplies the CPU, memory, and temporary storage needed to run SQL queries and data operations such as INSERT, UPDATE, DELETE, and COPY. It consumes credits only while running and can be resized or auto-suspended flexibly. Designing size and auto-suspend per workload is the first step in Snowflake cost optimization. 🔧 How It Works Key facts about warehouse sizing and billing: ・Sizes range from X-Small to 6X-Large, and each step up doubles compute and credit consumption. X-Small=1, Small=2, Medium=4, Large=8, X-Large=16, 2X-Large=32 ... up to 6X-Large=512 credits/hour. ・Billing is per-second with a 60-second minimum each time a warehouse starts or resumes. For example, an X-Large running 61 seconds costs about 0.271 credits, while a full hour costs 16 credits. ・Larger warehouses speed up large, complex queries, but larger is not necessarily faster for small, basic queries. ・Besides standard warehouses, Snowpark-optimized warehouses target memory-heavy workloads like ML training. 🛠 Practical Usage ・Use AUTO_SUSPEND (on by default) to suspend after idle time and AUTO_RESUME (on by default) to resume when a statement arrives, preventing wasted credits while idle. ・Create with CREATE WAREHOUSE etl_wh WAREHOUSE_SIZE = XLARGE; for batch, and use WAREHOUSE_SIZE = SMALL AUTO_SUSPEND = 60 for ad-hoc analytics to "pay only for what you use." ・Add INITIALLY_SUSPENDED = TRUE to create the warehouse in a suspended state. ・Warehouses can be resized even while running, so you can temporarily scale up just before a heavy job. 🎯 Use Cases ・Run a daily batch on X-Large to finish fast. Since one size step roughly doubles speed and halves runtime, you cut wall-clock time at a comparable credit cost. ・Set an ad-hoc analytics warehouse to Small with AUTO_SUSPEND=60 so it costs nothing when nobody is querying. ・For data loading, small-to-medium sizes are often sufficient; tune based on file count and size rather than warehouse size. ⚠️ Caveats ・Every resume bills a 60-second minimum, so an extremely short AUTO_SUSPEND (a few seconds) can backfire by triggering frequent start/stop cycles. ・A large size is wasted on small queries. "Scale up for slow queries" is the rule — bigger is not universally better. ・Loading performance depends more on file count and size than on warehouse size. Consider parallelizing files before scaling up. #Snowflake# #DataEngineering#

# Practical and Useful Patterns with ADK 🚀 Parallel tool execution, response size reduction, latency and token cost optimization — maximize production throughput with ADK's Tool Performance guide. 📌 Title: Tool Performance — Optimizing Tool Execution for Production 🔗 URL: 🧩 Overview ADK provides multiple approaches for optimizing tool performance: parallel execution of read-only tools, response size reduction for token cost optimization, and latency reduction best practices. These optimizations are essential for achieving high throughput in production environments. 🛠 Usage Examples of parallel execution and response optimization. First, define multiple read-only tools: `get_user_profile(user_id: str)`, `get_user_orders(user_id: str)`, and `get_user_preferences(user_id: str)` are side-effect-free functions that ADK can safely execute in parallel. Next, design a response-size-optimized tool like `search_products(query: str, limit: int = 5)` that returns only essential fields (`id`, `name`, `price`) while omitting image URLs, full descriptions, and metadata. Finally, create an `Agent` with `name="customer_service"` and `model="gemini-2.5-flash"`, passing all four functions in the `tools` list. 🏗 Practical Patterns **Parallel Execution Criteria**: Tools with no side effects and no mutual dependencies are safe for parallel execution. The LLM can invoke multiple tools simultaneously, and ADK executes them in parallel. Data retrieval tools (GET-equivalent) are great candidates. For parallel-friendly design, define independent tools like `get_weather(city: str)`, `get_news(topic: str)`, and `get_stock_price(symbol: str)`. Each is a read-only function returning a simple dict with its respective data (weather conditions, news articles, stock prices). When the LLM invokes all three simultaneously, ADK automatically runs them in parallel. **Response Size Optimization**: Tool return values consume LLM context window tokens. Removing unnecessary fields, summarizing data, and implementing pagination can dramatically reduce token costs. **Latency Optimization Checklist**: 1. Cache cacheable results 2. Set timeouts on external API calls 3. Avoid returning unnecessarily large data 4. Split into multiple small tools to encourage parallel execution 💡 Use Cases 📊 Parallel fetching from multiple data sources for dashboards 🔍 Field filtering in search results for token savings ⚡ Parallel API calls across microservices 💰 Token cost optimization in high-volume request environments ⚠️ Caveats - Parallel execution of tools with side effects (writes, deletes) can cause race conditions. Sequential execution is recommended for write operations. - Over-reducing responses may leave the LLM without sufficient information, degrading answer quality. Ensure essential information is always included. - Timeouts that are too short may interrupt legitimate responses. Set appropriate timeout values. ✨ Small optimizations compound into significant performance differences in production. Start by reviewing your response sizes! #ADK# #AIAgent#

# Practical and Useful Patterns for OpenAI Agent SDK 🌍 Control agent input and output safely with guardrails! Use cheap, fast models for pre-checks to block inappropriate requests and reduce costs. 📌 Title: Guardrails 🔗 URL: 🧩 Overview Guardrails run checks on agent inputs and outputs. Input guardrails validate user input, while output guardrails validate agent responses. By using cheap, fast models as guardrails to prevent unnecessary execution of expensive models, you achieve cost optimization. The tripwire mechanism immediately halts execution when a problem is detected. 🛠 Usage Import `Agent`, `InputGuardrail`, `OutputGuardrail`, and `GuardrailFunctionOutput` from `agents`. Define a guardrail function `check_homework_request(context, agent, input_data)` that calls ` input_data)` and returns `GuardrailFunctionOutput(output_info= tripwire_triggered= Create the agent with `Agent(name="TutorBot", instructions="You are a tutoring assistant.", input_guardrails=[InputGuardrail(guardrail_function=check_homework_request)])`. 🏗 Practical Patterns **Pre-filtering with Cheap Models (Cost Reduction)** Before running the expensive main model, use a cheap fast model to detect and block "homework requests" or "abuse." When the tripwire triggers, the main model execution is skipped, reducing costs. Define a cheap classifier `Agent(name="Classifier", model="gpt-4o-mini", output_type=ClassificationResult)` and an `abuse_check` function that runs ` input_data)` with `tripwire_triggered= Attach it to the main agent via `Agent(name="Assistant", model="gpt-4o", input_guardrails=[InputGuardrail(guardrail_function=abuse_check)])`. **Output Guardrails for Content Checking** Verify that agent responses don't contain sensitive or inappropriate content. Define an output guardrail function `check_sensitive_output(context, agent, output)` that runs ` f"Check this output for sensitive content: {output}")` and returns `GuardrailFunctionOutput(output_info= tripwire_triggered= Attach it with `Agent(name="SupportBot", output_guardrails=[OutputGuardrail(guardrail_function=check_sensitive_output)])`. **Relevance Check for Support Bots** Pre-determine whether user questions fall within scope, handling out-of-scope queries early. Define `relevance_check(context, agent, input_data)` which runs ` input_data)` and sets `tripwire_triggered=not to catch out-of-scope questions. Apply it with `Agent(name="SupportBot", instructions="Answer customer support questions about our product.", input_guardrails=[InputGuardrail(guardrail_function=relevance_check)])`. 💡 Use Cases 🛡 Block homework completion and abuse requests (cost reduction) 🔍 Prevent sensitive data in output (PII, internal information leak prevention) 📋 Control support bot response scope ⚡ Optimize API costs with cheap model pre-screening ⚠️ Considerations - When a guardrail tripwire triggers, an `InputGuardrailTripwireTriggered` exception is raised — catch and handle it appropriately - Input guardrails run **in parallel** with the main model by default, so the main execution may proceed if the guardrail doesn't finish in time - Factor in the model call cost of the guardrails themselves - Multiple guardrails can be set — execution halts if any one triggers ✨ Use guardrails to build agents that balance safety and cost efficiency! #OpenAIAgentSDK# #AIAgent#

Token spend becoming a problem? An AI gateway at the traffic layer gives you 3 levers that can drastically reduce token consumption. 1) Prompt Compression: strips unnecessary characters from a prompt before it ever reaches the foundation model. 2) Semantic Caching: caches responses based on meaning, not exact wording, so duplicate intent doesn't trigger a redundant model call. 3) Semantic Routing: routes prompts to lower-cost models based on intent, reserving expensive models for complex tasks and cheaper ones for simple requests. These are valuable at any size org, but at enterprise scale (millions of daily requests) the token cost optimization could reshape your budget entirely.

# Useful but Little-Known Features of ADK 2.0 🌍 Sending the same system prompt and tool definitions to the LLM on every call wastes both money and time. What if the SDK could cache that for you? ADK 2.0's ContextCacheConfig caches repeated context data sent to the LLM, reducing both API costs and response latency. Available with Gemini 2.0+, Python v1.15.0+, and Java v0.1.0+. 📌 Title: Context Cache (ContextCacheConfig) 🔗 URL: 🧩 Overview ContextCacheConfig reduces token consumption by caching context sent to the LLM — system prompts, tool definitions, fixed portions of conversation history, and more. It has three key parameters: min_tokens sets the minimum token threshold for caching to activate (default 0), ttl_seconds controls cache lifetime (default 1800 seconds / 30 minutes), and cache_intervals limits maximum cache reuse count (default 10). Configure it on the App object and caching is applied automatically. 🛠 How to use it Create a ContextCacheConfig and set it on the App. ```python from import App from google.adk.context import ContextCacheConfig cache_config = ContextCacheConfig( min_tokens=1000, # Cache only when context >= 1000 tokens ttl_seconds=3600, # Keep cache for 1 hour cache_intervals=20, # Reuse up to 20 times ) app = App( agent=my_agent, context_cache_config=cache_config, ) ``` Setting min_tokens appropriately ensures that small contexts are sent normally while large contexts benefit from caching. 🏗 Building it into production ・Agents with large system prompts or many tool definitions benefit the most from caching ・Tune ttl_seconds to match your workload pattern (short conversations → shorter TTL, long ones → longer TTL) ・Adjust cache_intervals based on request frequency to balance freshness and cost savings ・Monitor cost reduction metrics and continuously optimize parameters 💡 Use cases 💰 Cut API costs for agents with large, stable system prompts ⚡ Reduce response latency by skipping repeated tool definition transmission 🔁 Optimize token consumption for high-frequency chatbot interactions 📋 Efficiently handle fixed context (rules, guidelines, policies) that rarely changes ⚠️ Watch out This feature requires Gemini 2.0 or later. Context changes won't take effect while a cache is active, so set a shorter ttl_seconds if you frequently update system prompts. When cache_intervals is exceeded, a new cache is created, which can cause cost optimization effects to fluctuate. ✨ Context caching delivers significant cost and performance improvements, especially in scenarios with large, frequently-accessed context. It's a quick win for production deployments. #ADK# #AIAgent#

# Learning Palantir Foundry 🚀 Are you recomputing billion-row tables in full every single day? Process only the delta, and your compute costs drop dramatically. 📌 Title and Feature URL Title: Incremental Transforms URL: 📝 Overview Incremental transforms enable efficient data processing by handling only the data added or changed since the last run, instead of reprocessing the entire dataset. They're enabled with the `@incremental()` decorator, which automatically chooses between incremental and snapshot execution based on how the inputs changed. 🔧 How It Works The `@incremental()` decorator wraps a transform function to give it delta-processing capability. - It converts the standard input/output objects into incremental variants: `IncrementalTransformInput`, `IncrementalTransformOutput`, and `IncrementalTransformContext` - Input read modes can be `added` (new rows since last run, the default), `previous` (state from the last run), or `current` (the full current dataset) - Output write modes are `modify` (append to existing output) or `replace` (overwrite entirely); the default is `modify` for incremental runs and `replace` for snapshot runs - Key parameters include `require_incremental` (fail if incremental isn't possible), `semantic_version` (bumping it triggers a snapshot rebuild), `snapshot_inputs` (exempt specific inputs from incremental constraints), and `strict_append` (enforce append-only safety) 🛠 Practical Usage - Add `@incremental()` to large append-heavy log or transaction tables to replace daily full recomputes with delta processing - When you change logic, bump `semantic_version` to safely trigger a snapshot rebuild - Use `require_incremental` to force delta execution when you don't want a silent full reprocess - Use `strict_append` when you need strict append-only guarantees 🎯 Use Cases - Slashing soaring compute costs from daily full recomputes of billion-row tables via delta processing - Serving as the core cost-optimization technique that determines the economics of large-scale projects - Daily ingestion of append-only transaction histories and event logs - Streamlining pipelines whose upstream grows only through additions (APPEND/UPDATE) ⚠️ Caveats - Preview features always run non-incrementally - Unless requirements are met (all non-snapshot inputs contain additions only via APPEND/UPDATE, the input list stays stable, `semantic_version` is unchanged, etc.), the transform automatically runs in snapshot mode and fully replaces the output - Updated or deleted input files must be marked as snapshot inputs - The `previous` mode requires schema validation matching the previous output structure - Transform logic must support both incremental and snapshot execution paths #PalantirFoundry# #DataEngineering#

A useful but little-known OpenAI API feature 🔄 Want to fairly compare OpenAI models against alternatives? Now you can, on the same evaluation framework. OpenAI's "External models" feature lets you evaluate non-OpenAI models on the same Evals infrastructure. One platform, one set of graders, apples-to-apples comparison. 📌 Title: External models 🔗 URL: 🧩 Overview Model selection and migration decisions require fair comparison on the same criteria. External models lets you evaluate Claude, Gemini, open-source models, and others alongside OpenAI models using the same graders and datasets on OpenAI's Evals platform. No more maintaining separate evaluation tools for each vendor. 🛠 How to use it Register external model connection details (API endpoints, credentials) in Evals and add them as evaluation targets. Then run tests using your datasets and graders just like normal Evals. Results show up side by side on the same dashboard for easy comparison. 🏗 Building it into production ・Model selection process: when a new model drops, benchmark it against your current model on the same tasks. ・Migration decisions: quantitatively compare quality on your actual tasks before switching models. ・Cost optimization: periodically scan for cheaper models that deliver equivalent quality on your workloads. ・Multi-model strategy: build an evaluation framework for choosing the best model per task type. 💡 Use cases 🏆 Multi-model benchmark comparisons 🔀 Quality verification before model migration 💰 Cost-to-quality optimization 📋 Task-specific optimal model selection ⚠️ Watch out External model API keys and usage costs are separate. Some models may have different response formats or error handling, requiring output normalization during evaluation. Always compare on the same tasks and datasets for a fair assessment. ✨ Move model selection from gut feeling to data-driven. Start by lining up your current model against a challenger in Evals and see the numbers. #OpenAI# #LLM#

A useful but little-known OpenAI API feature 📦 Sending thousands of LLM requests one by one and wincing at the bill? There's a much cheaper way. OpenAI's "Batch" API lets you bundle requests together for async execution at a significant discount. It's the go-to for evaluations, classification, data generation, and any high-volume job that doesn't need real-time responses. 📌 Title: Batch 🔗 URL: 🧩 Overview When you're calling the LLM at scale, sending requests one at a time is expensive and slow. The Batch API lets you upload requests as a JSONL file, process them all asynchronously, and get results at a steep discount compared to standard API calls. Results are collected once the batch completes. 🛠 How to use it Compile your requests into a JSONL file, upload it, and create a batch job. When processing finishes, download the results file. Each request uses the same Chat Completions format you already know, so existing prompts work as-is. Pair with Webhooks to get notified automatically when a batch is done. 🏗 Building it into production ・Dataset classification and labeling: run tens of thousands of text categorizations as an overnight batch. Labeled data is ready by morning. ・Synthetic data generation pipelines: when you're generating training data at scale, the batch discount makes a material difference to your bill. ・Model evaluation and benchmarking: run quality comparisons across multiple prompts in one shot. Analyze results together. ・Periodic summarization and reporting: weekly article digests, customer feedback analysis, anything that processes in bulk on a schedule. 💡 Use cases 🗂 Large-scale text classification and tagging 🧬 Synthetic and training data generation 📊 Model evaluation and prompt comparison 📝 Scheduled batch summarization and extraction ⚠️ Watch out Batch processing is async, so results take time to come back. Not suitable for anything that needs a real-time response. Individual requests within a batch can also fail, so build proper error handling when parsing the results file. Start with a small test batch before submitting massive jobs. ✨ The foundation of cost optimization at scale is batching. Switch your evaluation pipeline to Batch first and see the difference on your next invoice. #OpenAI# #LLM#

A useful but little-known OpenAI API feature 📦 Sending thousands of LLM requests one by one and wincing at the bill? There's a much cheaper way. OpenAI's "Batch" API lets you bundle requests together for async execution at a significant discount. It's the go-to for evaluations, classification, data generation, and any high-volume job that doesn't need real-time responses. 📌 Title: Batch 🔗 URL: 🧩 Overview When you're calling the LLM at scale, sending requests one at a time is expensive and slow. The Batch API lets you upload requests as a JSONL file, process them all asynchronously, and get results at a steep discount compared to standard API calls. Results are collected once the batch completes. 🛠 How to use it Compile your requests into a JSONL file, upload it, and create a batch job. When processing finishes, download the results file. Each request uses the same Chat Completions format you already know, so existing prompts work as-is. Pair with Webhooks to get notified automatically when a batch is done. 🏗 Building it into production ・Dataset classification and labeling: run tens of thousands of text categorizations as an overnight batch. Labeled data is ready by morning. ・Synthetic data generation pipelines: when you're generating training data at scale, the batch discount makes a material difference to your bill. ・Model evaluation and benchmarking: run quality comparisons across multiple prompts in one shot. Analyze results together. ・Periodic summarization and reporting: weekly article digests, customer feedback analysis, anything that processes in bulk on a schedule. 💡 Use cases 🗂 Large-scale text classification and tagging 🧬 Synthetic and training data generation 📊 Model evaluation and prompt comparison 📝 Scheduled batch summarization and extraction ⚠️ Watch out Batch processing is async, so results take time to come back. Not suitable for anything that needs a real-time response. Individual requests within a batch can also fail, so build proper error handling when parsing the results file. Start with a small test batch before submitting massive jobs. ✨ The foundation of cost optimization at scale is batching. Switch your evaluation pipeline to Batch first and see the difference on your next invoice. #OpenAI# #LLM#