How to Manage LLM-Specific Constraints as First-Class Operations

Production LLM pipelines fail when constraints exist as afterthoughts. Rate limits trigger at runtime. Token capacity errors surface after API calls. Cost tracking requires manual instrumentation. Concurrency controls scatter across application code.

The traditional approach treats constraints as exceptions to handle. A better approach treats them as configuration that shapes execution strategy.

This guide shows how to configure LLM constraints declaratively using Fenic's DataFrame framework, where rate limits, token budgets, and concurrency controls become query planning inputs rather than runtime errors.

Constraint Types in LLM Operations

LLM workloads require managing multiple constraint categories simultaneously. Each category impacts both cost and reliability.

Provider Rate Limits

• Requests per minute (RPM) caps
• Tokens per minute (TPM) quotas
• Input and output token separation
• Model-specific throughput boundaries

Token Capacity

• Context window limitations per model
• Prompt size validation before execution
• Dynamic chunking for oversized inputs
• Token counting accuracy across providers

Cost Controls

• Per-operation budget tracking
• Model tier selection based on task complexity
• Quota exhaustion vs temporary rate limiting
• Usage metrics for optimization

Concurrency Boundaries

• Maximum parallel API requests
• MCP server execution limits
• Async operation throttling
• Resource pool management

Timing Constraints

• Per-request timeout thresholds
• Batch processing windows
• Retry backoff intervals
• Service level objectives

Session Configuration for Rate Management

Fenic sessions define constraints at initialization. The query planner uses this configuration to optimize execution before making API calls.

Basic Provider Setup

Configure rate limits when defining language models:

python
import fenic as fc

config = fc.SessionConfig(
    app_name="constraint_demo",
    semantic=fc.SemanticConfig(
        language_models={
            "fast": fc.OpenAILanguageModel(
                "gpt-4.1-nano",
                rpm=500,
                tpm=200_000
            ),
            "accurate": fc.AnthropicLanguageModel(
                "claude-sonnet-4",
                rpm=300,
                input_tpm=150_000,
                output_tpm=75_000
            ),
        },
        default_language_model="fast",
    ),
)

session = fc.Session.get_or_create(config)

The engine enforces these limits through self-throttling. When batched operations would exceed TPM quotas, execution automatically staggers to remain within constraints.

Multi-Model Configuration

Production systems often use multiple models with different cost and performance characteristics:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "nano": fc.OpenAILanguageModel(
                "gpt-4.1-nano",
                rpm=500,
                tpm=200_000
            ),
            "flash": fc.GoogleVertexLanguageModel(
                "gemini-2.0-flash-lite",
                rpm=300,
                tpm=150_000
            ),
            "reasoning": fc.OpenAILanguageModel(
                "gpt-5",
                rpm=100,
                tpm=50_000
            ),
        },
        default_language_model="nano"
    )
)

Task complexity determines model selection. Classification and simple extraction use "nano". Analysis requiring reasoning uses "reasoning". The configuration enforces appropriate rate limits for each tier.

Token Capacity Validation

Token limits require validation at two points: during planning and during execution. Planning-time validation prevents invalid operations. Execution-time validation handles dynamic content that exceeds capacity.

Planning-Time Checks

The query planner validates token capacity before execution:

python
from pydantic import BaseModel, Field
from typing import List

class DocumentAnalysis(BaseModel):
    summary: str = Field(max_length=500)
    key_points: List[str]
    categories: List[str]

# If documents exceed model capacity, this fails during planning
df = df.select(
    fc.semantic.extract(
        "document_content",
        DocumentAnalysis,
        model_alias="nano"
    ).alias("analysis")
)

When the planner detects capacity violations, it raises errors specifying which records exceed limits and by what margin. This fail-fast behavior prevents wasting API calls on operations guaranteed to fail.

The Fenic 0.5.0 release added token capacity guardrails that validate requests against model limits during query construction.

Dynamic Chunking with semantic.reduce

For operations requiring processing of large content volumes, semantic.reduce implements automatic chunking:

python
# Automatically chunks and recursively processes large document sets
summary = df.group_by("customer_id").agg(
    fc.semantic.reduce(
        "Summarize customer interactions: {{events}}",
        fc.col("support_events"),
        order_by=fc.col("timestamp").asc(),
        model_alias="flash"
    ).alias("interaction_summary")
)

The reduce operation packs maximum content into each API call while respecting token limits. Intermediate results get recursively processed until producing final outputs. This approach minimizes API calls while maintaining token capacity compliance.

Quota vs Rate Limit Handling

The engine distinguishes between temporary rate limiting and permanent quota exhaustion. This distinction prevents retry logic from amplifying problems.

Fail-Fast on Quota Exhaustion

When providers return 429 errors indicating exhausted quotas, Fenic fails immediately:

python
# Quota exhaustion triggers immediate failure with clear error messages
# No retry attempts waste time on depleted accounts
df = df.select(
    fc.semantic.classify(
        "content",
        ["bug", "feature", "question"],
        model_alias="fast"
    ).alias("category")
)

This pattern prevents cascading failures where retry logic attempts hundreds of requests against accounts with zero remaining quota. The explicit error enables teams to address quota issues immediately rather than discovering them through accumulated timeout failures.

Transient Rate Limit Retry

Temporary rate limiting (429 responses indicating request rate exceeded) triggers automatic retry with exponential backoff:

python
# Transient rate limits automatically retry with backoff
# Engine manages retry timing to prevent thundering herd
df = df.select(
    fc.semantic.map(
        "Generate description for {{product}}",
        product=fc.col("product_name"),
        model_alias="flash"
    ).alias("description")
)

Backoff timing adapts based on response headers and observed failure patterns. The engine increases wait times progressively to allow provider systems to recover without overwhelming them with retry storms.

Cost Tracking Through Query Metrics

Production systems require visibility into operation costs. Fenic provides automatic cost tracking through the built-in metrics table.

Accessing Cost Data

Every semantic operation logs detailed metrics:

python
# Access comprehensive cost and performance data
metrics = session.table("fenic_system.query_metrics")

# Analyze costs by model
cost_breakdown = metrics.select(
    "model",
    fc.sum("cost_usd").alias("total_cost"),
    fc.sum("input_tokens").alias("total_input_tokens"),
    fc.sum("output_tokens").alias("total_output_tokens"),
    fc.count("*").alias("num_operations")
).group_by("model")

cost_breakdown.show()

This visibility enables continuous cost optimization without scattered instrumentation code throughout applications.

Identifying Expensive Operations

Query metrics reveal which operations drive costs:

python
# Find operations exceeding budget thresholds
expensive_operations = metrics.filter(
    fc.col("cost_usd") > 0.50
).select(
    "operation_type",
    "model",
    "cost_usd",
    "input_tokens",
    "output_tokens",
    "latency_ms"
).order_by(fc.col("cost_usd").desc())

expensive_operations.show(20)

High-cost operations often benefit from model downgrade or prompt optimization. The metrics table provides the data required to make informed optimization decisions.

Concurrency Controls for Parallel Operations

Concurrency limits prevent resource exhaustion and protect downstream systems. Fenic provides multiple concurrency control mechanisms.

MCP Server Concurrency

When exposing tools through MCP servers, configure maximum concurrent executions:

python
from fenic.api.mcp import create_mcp_server, run_mcp_server_sync

tools = session.catalog.list_tools()

server = create_mcp_server(
    session,
    "ProductionServer",
    tools=tools,
    concurrency_limit=10  # Maximum concurrent tool executions
)

run_mcp_server_sync(
    server,
    transport="http",
    stateless_http=True,
    port=8000
)

This limit caps simultaneous tool invocations. Excess requests queue rather than getting rejected, maintaining predictable throughput under variable load.

Async UDF Concurrency

For I/O-bound operations within DataFrames, async UDFs provide per-operation concurrency control:

python
import aiohttp

@fc.async_udf(
    return_type=fc.StringType,
    max_concurrency=15,   # Concurrent request limit
    timeout_seconds=10,    # Per-item timeout
    num_retries=2,         # Retry attempts
)
async def enrich_from_api(record_id: str) -> str:
    async with aiohttp.ClientSession() as session:
        async with session.get(
            f"https://api.example.com/enrich/{record_id}"
        ) as resp:
            data = await resp.json()
            return data["enriched_content"]

# Apply with automatic concurrency management
enriched = df.select(
    fc.col("record_id"),
    enrich_from_api(fc.col("record_id")).alias("enrichment")
)

The async UDF system manages three constraints simultaneously:

• Concurrency bounds prevent overwhelming external APIs
• Timeouts prevent hanging on slow endpoints
• Retries handle transient failures through exponential backoff

Individual failures return None instead of failing entire batches, maintaining pipeline throughput even when external services experience intermittent issues.

Provider-Specific Configuration

Different providers require different constraint approaches. Fenic abstracts these differences while maintaining unified interfaces.

OpenAI with Reasoning Control

OpenAI models support reasoning effort controls impacting both latency and cost:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "reasoning": fc.OpenAILanguageModel(
                "gpt-5",
                rpm=100,
                tpm=50_000,
                profiles={
                    "minimal": fc.OpenAILanguageModel.Profile(
                        reasoning_effort="low"
                    ),
                    "standard": fc.OpenAILanguageModel.Profile(
                        reasoning_effort="medium"
                    ),
                }
            )
        }
    )
)

Profiles enable cost optimization by adjusting reasoning intensity based on task complexity. Simple classification uses "minimal" while analysis requiring deeper reasoning uses "standard".

Anthropic with Thinking Budgets

Anthropic models with extended thinking require explicit thinking token budgets:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "claude": fc.AnthropicLanguageModel(
                "claude-opus-4.1",
                rpm=50,
                input_tpm=100_000,
                output_tpm=50_000,
                profiles={
                    "default": fc.AnthropicLanguageModel.Profile(
                        thinking_token_budget=10000
                    )
                }
            )
        }
    )
)

Thinking token budgets cap extended reasoning, preventing unbounded consumption on tasks requiring iterative analysis.

Google with Native Token Counting

Google models use native token counting for accurate accounting:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "gemini": fc.GoogleDeveloperLanguageModel(
                "gemini-2.0-flash",
                rpm=100,
                tpm=1_000_000
            )
        }
    )
)

Native counting eliminates estimation errors causing unexpected cost variances. The engine queries provider token counters directly, ensuring constraints reflect actual model behavior.

OpenRouter with Provider Routing

OpenRouter enables multi-provider fallback with routing strategies:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "routed": fc.OpenRouterLanguageModel(
                model_name="openai/gpt-4o",
                profiles={
                    "default": fc.OpenRouterLanguageModel.Profile(
                        provider=fc.OpenRouterLanguageModel.Provider(
                            sort="latency"
                        )
                    )
                },
                default_profile="default"
            )
        }
    )
)

Provider routing respects constraints across multiple backends. When primary providers hit rate limits, OpenRouter automatically routes to alternatives while maintaining identical interfaces.

Schema Constraints for Type Safety

Schema-driven extraction enforces type constraints through Pydantic models. The schema defines allowed values, required fields, and validation rules.

Enumerated Value Constraints

Restrict outputs to predefined categories:

python
from pydantic import BaseModel, Field
from typing import Literal, List

class TicketClassification(BaseModel):
    category: Literal["bug", "feature", "documentation", "support"]
    priority: Literal["low", "medium", "high", "critical"]
    affected_components: List[str]
    estimated_effort: Literal["small", "medium", "large"]

# Schema constraints enforced during extraction
tickets = df.with_column(
    "classification",
    fc.semantic.extract("ticket_content", TicketClassification)
)

When extraction produces values outside allowed sets, validation fails immediately. This prevents downstream errors from propagated invalid data.

Field-Level Validation

Add validation rules through Pydantic Field constraints:

python
from pydantic import BaseModel, Field

class ProductReview(BaseModel):
    summary: str = Field(min_length=20, max_length=200)
    rating: int = Field(ge=1, le=5)
    key_features: List[str] = Field(min_items=1, max_items=10)
    would_recommend: bool

# Field validators prevent invalid extraction results
reviews = df.with_column(
    "review",
    fc.semantic.extract("review_text", ProductReview)
)

Length constraints, numeric bounds, and list size limits enforce quality requirements during extraction rather than requiring post-processing validation.

The schema-driven approach achieves 74.2-96.1% F1 scores in evaluated domains without task-specific labeled data, according to research from the Association for Computational Linguistics.

Timeout and Retry Configuration

Timeout constraints prevent operations from hanging indefinitely. Retry strategies handle transient failures without manual intervention.

Per-Operation Timeouts

Async UDFs enforce item-level timeouts:

python
@fc.async_udf(
    return_type=fc.IntegerType,
    max_concurrency=20,
    timeout_seconds=8,    # Per-item timeout
    num_retries=3,        # Retry attempts
)
async def score_content(content_id: str) -> int:
    # Implementation with timeout enforcement
    pass

# Apply with automatic timeout management
scored = df.select(
    fc.col("content_id"),
    score_content(fc.col("content_id")).alias("score")
)

When individual items exceed timeouts, the engine cancels those requests and returns None for affected rows. Remaining batch items proceed normally, maintaining pipeline throughput.

Exponential Backoff Retry

The engine implements intelligent retry logic distinguishing retryable errors from permanent failures:

python
# Automatic retry with exponential backoff
# Network timeouts and 503 responses: retry
# Quota exhaustion and invalid requests: fail immediately
df = df.select(
    fc.semantic.map(
        "Analyze sentiment: {{review}}",
        review=fc.col("customer_review"),
        model="flash"
    ).alias("sentiment")
)

Backoff timing adapts based on response codes. Connection resets trigger aggressive retries. Ambiguous failures receive cautious retry timing. Permanent errors skip retry attempts entirely.

Resource Cleanup on Failure

When operations fail, the engine performs proper cleanup through cooperative cancellation:

python
try:
    # Process with async operations
    results = df.select(
        enrich_from_api(fc.col("record_id")).alias("enrichment")
    ).collect()
except Exception as e:
    # All pending async work cancelled cleanly
    # No hanging connections or resource leaks
    print(f"Pipeline failed: {e}")

This cleanup extends to event loops and task cancellation, preventing memory leaks in long-running services. The Fenic 0.4.0 release added comprehensive cleanup ensuring stable operation in production deployments.

Batch Optimization Based on Constraints

The query planner uses constraint configuration to determine optimal batching strategies. Batch size calculation incorporates token capacity, rate limits, concurrency targets, and provider capabilities.

Automatic Batch Size Determination

The engine analyzes operations and determines appropriate batching:

python
# Engine automatically batches based on configured constraints
# Tight rate limits trigger small frequent batches
# Generous limits enable large infrequent batches
classified = df.with_column(
    "priority",
    fc.semantic.classify(
        "ticket_text",
        ["urgent", "normal", "low"],
        model="nano"
    )
)

Factors influencing batch size:

• Token capacity ensures batch prompts fit within model context windows
• Rate limits prevent batches from exceeding RPM or TPM constraints
• Concurrency balances batch size against parallel execution opportunities
• Provider capabilities use native batching APIs when available

Context Window Packing

The semantic.reduce operator implements greedy context packing maximizing information density:

python
# Automatically handles chunking and recursive aggregation
aggregated = df.group_by("conversation_id").agg(
    fc.semantic.reduce(
        "Summarize conversation: {{messages}}",
        fc.col("messages"),
        order_by=fc.col("timestamp").asc(),
        model_alias="flash"
    ).alias("summary")
)

The operation packs maximum records into each model call, then recursively applies reduction to intermediate results. A 10,000 record dataset might require only 100 reduce calls through efficient packing, compared to 10,000 individual map operations.

Multi-Provider Load Distribution

When configuring multiple providers, the engine distributes load based on configured constraints:

python
config = fc.SessionConfig(
    semantic=fc.SemanticConfig(
        language_models={
            "primary": fc.OpenAILanguageModel(
                "gpt-4o",
                rpm=500,
                tpm=200_000
            ),
            "secondary": fc.AnthropicLanguageModel(
                "claude-sonnet-4",
                rpm=300,
                input_tpm=150_000,
                output_tpm=75_000
            ),
        },
        default_language_model="primary"
    )
)

Operations automatically route to alternative providers when primary limits approach. The engine tracks provider state internally, directing requests to providers with available capacity.

Credential Validation at Session Creation

Provider credentials validate during session initialization. Invalid API keys trigger immediate failures with clear error messages identifying problematic providers.

Session-Level Validation

python
# Provider key validation happens at session creation
# Invalid credentials fail immediately with actionable errors
session = fc.Session.get_or_create(
    fc.SessionConfig(
        app_name="production_app",
        semantic=fc.SemanticConfig(
            language_models={
                "openai": fc.OpenAILanguageModel(
                    "gpt-4o",
                    rpm=500,
                    tpm=200_000
                ),
                "anthropic": fc.AnthropicLanguageModel(
                    "claude-sonnet-4",
                    rpm=300,
                    input_tpm=150_000,
                    output_tpm=75_000
                )
            }
        )
    )
)

# By this point, all configured providers validated
# Pipeline execution proceeds with confidence

This pattern eliminates credential failures hours into pipeline execution. Configuration issues surface immediately during deployment validation.

Environment-Based Credentials

The engine detects credentials from standard environment variables:

bash
export OPENAI_API_KEY=sk-...
export ANTHROPIC_API_KEY=sk-ant-...
export GOOGLE_API_KEY=...
export COHERE_API_KEY=...
export OPENROUTER_API_KEY=...

Environment-based configuration supports standard deployment practices where credentials get injected through platform configuration rather than hardcoded in application code.

Monitoring and Observability

Production systems require visibility into constraint utilization and operation performance. Fenic provides monitoring through query metrics and execution plans.

Performance Analysis Through Metrics

The metrics table logs every semantic operation:

python
metrics = session.table("fenic_system.query_metrics")

# Calculate latency percentiles
latency_analysis = metrics.select(
    "operation_type",
    fc.avg("latency_ms").alias("avg_latency"),
    fc.percentile("latency_ms", 0.50).alias("p50_latency"),
    fc.percentile("latency_ms", 0.95).alias("p95_latency"),
    fc.percentile("latency_ms", 0.99).alias("p99_latency")
).group_by("operation_type")

latency_analysis.show()

Latency percentiles reveal operation performance characteristics. High p99 values indicate occasional slow requests requiring investigation.

Cost Attribution Analysis

Track costs across dimensions:

python
# Analyze costs by operation and model
cost_matrix = metrics.select(
    "operation_type",
    "model",
    fc.sum("cost_usd").alias("total_cost"),
    fc.count("*").alias("num_operations"),
    fc.avg("cost_usd").alias("avg_cost_per_op")
).group_by("operation_type", "model")

cost_matrix.show()

This analysis identifies optimization opportunities. Operations with high per-operation costs might benefit from model downgrade. High-volume operations could justify caching strategies.

Query Plan Inspection

The explain() method visualizes execution strategy:

python
# Inspect how constraints affect execution planning
plan = df.select(
    fc.semantic.extract("content", Schema, model_alias="flash")
).explain()

print(plan)

Query plans show batching decisions, parallelization strategy, and constraint enforcement points. This visibility aids debugging performance issues and verifying optimization effectiveness.

Row-Level Lineage

Track individual records through transformation pipelines:

python
# Trace specific record processing
lineage = df.get_lineage(record_id="ticket_12345")

# Examine transformation history
for step in lineage.steps:
    print(f"Operation: {step.operation}")
    print(f"Model: {step.model}")
    print(f"Tokens: {step.tokens_used}")
    print(f"Cost: ${step.cost_usd}")

Lineage tracking proves critical when debugging non-deterministic outputs. When records produce unexpected results, lineage shows exactly which operations transformed them and intermediate states throughout processing.

Production Case Study: RudderStack

RudderStack implemented constraint management patterns reducing triage time by 95% while maintaining 90%+ classification accuracy.

The implementation used several constraint management techniques:

Rate limit configuration - Semantic operations configured with appropriate RPM/TPM limits preventing API quota exhaustion during batch processing of support tickets.

MCP server with concurrency controls - Tools exposed through MCP servers enforced concurrency bounds respecting Linear's API quotas while maintaining responsiveness.

Schema validation - Pydantic models ensured extracted ticket classifications maintained referential integrity with source documents.

Automated write-back - The triage agent wrote decisions to Linear under concurrency and rate limit constraints respecting API quotas while maintaining throughput.

The constraint management architecture enabled scaling from manual triage to automated processing without compromising accuracy or overwhelming downstream systems. Product managers receive classified requests with supporting evidence rather than raw data requiring manual research.

Implementation Patterns

When building constraint-aware LLM pipelines:

Configure constraints declaratively

• Define RPM and TPM limits for each model provider
• Specify reasoning effort profiles for cost control
• Establish thinking token budgets where applicable
• Validate provider credentials at session initialization

Enforce type constraints through schemas

• Define Pydantic models for extraction operations
• Use Literal types for enumerated value constraints
• Add Field validators for length and format requirements
• Nest models for hierarchical validation

Establish concurrency and timeout controls

• Configure MCP server concurrency limits
• Set max_concurrency for async UDFs
• Define per-operation timeout thresholds
• Implement retry strategies with exponential backoff

Enable cost and performance monitoring

• Track operation costs through query metrics
• Monitor latency percentiles by operation type
• Use explain() to inspect query execution plans
• Implement lineage tracking for debugging

Optimize batch processing

• Allow engine to determine batch sizes based on constraints
• Use semantic.reduce for aggregation operations
• Configure multi-provider fallback for resilience
• Monitor batch efficiency through metrics analysis

The semantic operators architecture treats constraints as configuration shaping execution strategy rather than runtime obstacles triggering errors.

Conclusion

Managing LLM constraints as first-class operations requires treating rate limits, token capacity, concurrency bounds, and cost budgets as declarative configuration. The DataFrame abstraction provides unified interfaces where constraints shape query planning, enable automatic optimization, and ensure reliable execution.

This approach eliminates common failure modes. Pipelines fail during planning rather than after consuming API credits. Constraints adapt execution strategy automatically rather than requiring manual tuning. Monitoring surfaces utilization patterns rather than requiring custom instrumentation.

Production systems built on these patterns handle constraint violations gracefully, scale under variable load, and provide observability for continuous optimization. The Typedef platform moves constraint management from scattered application logic into declarative configuration that the engine enforces systematically.

Start with provider-level configuration establishing rate and token limits. Add schema-driven validation enforcing type constraints during extraction. Layer in concurrency controls through MCP servers and async UDFs. Monitor through built-in metrics tracking cost and performance automatically.

For additional implementation details, see the Fenic documentation and constraint management examples. How to Manage LLM-Specific ... fcf080b7ba58f7c98c082c24.md External Displaying How to Manage LLM-Specific Constraints as First-Cl 2a6df41efcf080b7ba58f7c98c082c24.md.