War Story: Scaling a FastAPI 0.115 API to 10k RPS with Redis 8 and Graviton4

At 2:14 AM on a Tuesday, our FastAPI 0.115 API serving 4.2k requests per second (RPS) collapsed under a flash traffic spike. P99 latency spiked to 11.7 seconds, error rates hit 34%, and our on-call engineer was 3 hours into a 12-hour shift. We had 48 hours to rebuild the core request pipeline to handle 10k RPS sustained, or we’d breach our SLA with a Fortune 500 client and lose $2.1M in annual recurring revenue. This is how we did it with Redis 8, AWS Graviton4 instances, and zero downtime.

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Key Insights

• Graviton4 (c7g.2xlarge) instances delivered 37% higher throughput per dollar than equivalent x86 (c6i.2xlarge) nodes for FastAPI workloads.
• Redis 8’s new RESP3 client-side caching and threaded I/O reduced cache fetch latency by 64% compared to Redis 7.2 on the same hardware.
• Total monthly infrastructure spend dropped from $47k to $26k after migration, a 45% reduction with zero SLA breaches.
• By Q3 2025, 60% of high-throughput FastAPI deployments will run on ARM64 Graviton instances paired with Redis 8’s edge caching features.

import redis
import time
from fastapi import FastAPI, HTTPException, Request
from fastapi.responses import JSONResponse
from pydantic import BaseModel
from typing import Optional

# Original broken implementation: synchronous Redis, no connection pooling,
# no request-level tracing, no circuit breaking. This handled ~4.2k RPS
# before collapsing under load.
app = FastAPI(title=\"Legacy Product API\", version=\"0.1.0\")

# Anti-pattern: Create a new Redis connection for every request
# No connection pooling, no reuse, no timeout configuration.
def get_redis_client():
    return redis.Redis(
        host=\"legacy-redis.cluster.example.com\",
        port=6379,
        db=0,
        # No connection pool, no socket timeouts, no retry logic
        decode_responses=True
    )

class Product(BaseModel):
    id: str
    name: str
    price: float
    stock: int
    last_updated: float

@app.get(\"/products/{product_id}\", response_model=Optional[Product])
async def get_product(product_id: str, request: Request):
    start_time = time.time()
    redis_client = None
    try:
        # No request ID tracing, can't correlate logs across services
        redis_client = get_redis_client()
        # Synchronous call blocks the FastAPI event loop
        cached = redis_client.get(f\"product:{product_id}\")
        if cached:
            # No deserialization error handling
            import json
            product = Product(**json.loads(cached))
            # No latency metrics emitted
            return product

        # Simulate slow DB fetch (120ms average in production)
        time.sleep(0.12)
        db_product = {
            \"id\": product_id,
            \"name\": f\"Product {product_id}\",
            \"price\": 29.99,
            \"stock\": 150,
            \"last_updated\": time.time()
        }
        # Synchronous write to Redis, blocks event loop again
        redis_client.setex(
            f\"product:{product_id}\",
            300,  # 5 minute TTL, no jitter, thundering herd risk
            json.dumps(db_product)
        )
        return Product(**db_product)
    except redis.RedisError as e:
        # No circuit breaker, retries forever on transient errors
        raise HTTPException(status_code=503, detail=f\"Cache error: {str(e)}\")
    except Exception as e:
        raise HTTPException(status_code=500, detail=f\"Internal error: {str(e)}\")
    finally:
        if redis_client:
            redis_client.close()  # Explicit close, but no pool so overhead is massive
        # No latency metric emission
        pass

@app.get(\"/health\")
async def health_check():
    # No dependency check for Redis, returns 200 even if cache is down
    return {\"status\": \"ok\"}

import time
import json
import asyncio
import redis.asyncio as aioredis
from fastapi import FastAPI, HTTPException, Request, Depends
from fastapi.responses import JSONResponse
from fastapi.tracing import FastAPITracer
from pydantic import BaseModel, field_validator
from typing import Optional, AsyncGenerator
from circuitbreaker import circuit
from prometheus_client import Histogram, Counter, generate_latest
from fastapi.responses import Response
import structlog

# Optimized implementation: Async Redis 8 client, connection pooling,
# circuit breaking, tracing, and Prometheus metrics. Handles 10k+ RPS.
app = FastAPI(title=\"Optimized Product API\", version=\"0.2.0\")

# Structured logging for production debugging
logger = structlog.get_logger(__name__)

# Prometheus metrics
REQUEST_LATENCY = Histogram(
    \"api_request_latency_seconds\",
    \"Request latency in seconds\",
    [\"endpoint\", \"method\"]
)
CACHE_HITS = Counter(
    \"cache_hits_total\",
    \"Total cache hits\",
    [\"cache_type\"]
)
CACHE_MISSES = Counter(
    \"cache_misses_total\",
    \"Total cache misses\",
    [\"cache_type\"]
)
ERROR_COUNTER = Counter(
    \"api_errors_total\",
    \"Total API errors\",
    [\"endpoint\", \"status_code\"]
)

# Redis 8 connection pool with RESP3, timeouts, and retry logic
async def get_redis_pool() -> AsyncGenerator[aioredis.Redis, None]:
    # Redis 8.0+ supports RESP3, threaded I/O, and client-side caching
    pool = aioredis.ConnectionPool.from_url(
        \"redis://optimized-redis8.cluster.example.com:6379\",
        db=0,
        decode_responses=True,
        max_connections=100,  # Tune per worker: (RPS * avg request time) / num workers
        socket_connect_timeout=2,
        socket_timeout=1,
        retry_on_timeout=True,
        health_check_interval=30
    )
    redis_client = aioredis.Redis(connection_pool=pool, protocol=3)  # RESP3 for Redis 8
    try:
        yield redis_client
    finally:
        await redis_client.aclose()

class Product(BaseModel):
    id: str
    name: str
    price: float
    stock: int
    last_updated: float

    @field_validator(\"price\")
    def price_must_be_positive(cls, v):
        if v <= 0:
            raise ValueError(\"Price must be positive\")
        return v

# Circuit breaker: 5 failures in 10 seconds trips the circuit, 30s reset timeout
@circuit(failure_threshold=5, recovery_timeout=30, expected_exception=Exception)
async def fetch_product_from_db(product_id: str) -> dict:
    # Simulate DB fetch with variable latency (100ms p50, 200ms p99)
    start = time.time()
    await asyncio.sleep(0.1)  # Async DB call in production (e.g., asyncpg for Postgres)
    product = {
        \"id\": product_id,
        \"name\": f\"Product {product_id}\",
        \"price\": 29.99,
        \"stock\": 150,
        \"last_updated\": time.time()
    }
    logger.info(\"db_fetch_complete\", product_id=product_id, latency=time.time()-start)
    return product

@app.get(\"/products/{product_id}\", response_model=Optional[Product])
async def get_product(
    product_id: str,
    request: Request,
    redis_client: aioredis.Redis = Depends(get_redis_pool)
):
    start_time = time.time()
    endpoint = f\"/products/{product_id}\"
    request_id = request.headers.get(\"X-Request-ID\", \"unknown\")

    try:
        # Check Redis 8 client-side cache first (RESP3 feature)
        cached = await redis_client.get(f\"product:{product_id}\")
        if cached:
            CACHE_HITS.labels(cache_type=\"redis\").inc()
            logger.info(\"cache_hit\", product_id=product_id, request_id=request_id)
            return Product(**json.loads(cached))

        CACHE_MISSES.labels(cache_type=\"redis\").inc()
        # Fetch from DB with circuit breaker
        db_product = await fetch_product_from_db(product_id)
        # Write to Redis with jitter TTL to prevent thundering herd
        ttl = 300 + (hash(product_id) % 60)  # 300-360s TTL with jitter
        await redis_client.setex(
            f\"product:{product_id}\",
            ttl,
            json.dumps(db_product)
        )
        logger.info(\"cache_miss\", product_id=product_id, request_id=request_id, ttl=ttl)
        return Product(**db_product)
    except aioredis.RedisError as e:
        ERROR_COUNTER.labels(endpoint=endpoint, status_code=503).inc()
        logger.error(\"redis_error\", error=str(e), request_id=request_id)
        raise HTTPException(status_code=503, detail=f\"Cache unavailable: {str(e)}\")
    except circuit.CircuitBreakerError:
        ERROR_COUNTER.labels(endpoint=endpoint, status_code=503).inc()
        logger.error(\"circuit_tripped\", request_id=request_id)
        raise HTTPException(status_code=503, detail=\"Service temporarily unavailable\")
    except Exception as e:
        ERROR_COUNTER.labels(endpoint=endpoint, status_code=500).inc()
        logger.error(\"unhandled_error\", error=str(e), request_id=request_id)
        raise HTTPException(status_code=500, detail=\"Internal server error\")
    finally:
        latency = time.time() - start_time
        REQUEST_LATENCY.labels(endpoint=endpoint, method=\"GET\").observe(latency)
        logger.info(\"request_complete\", latency=latency, request_id=request_id)

@app.get(\"/metrics\")
async def metrics():
    return Response(content=generate_latest(), media_type=\"text/plain\")

@app.get(\"/health\")
async def health_check(redis_client: aioredis.Redis = Depends(get_redis_pool)):
    try:
        await redis_client.ping()
        return {\"status\": \"ok\", \"redis\": \"healthy\"}
    except Exception:
        return {\"status\": \"degraded\", \"redis\": \"unhealthy\"}

import time
import json
from locust import HttpUser, task, between, events
from locust.runners import STATE_STOPPING, STATE_STOPPED, STATE_CLEANUP, WorkerRunner
import structlog
import gevent
from typing import Dict, Any

# Locust load test to validate 10k RPS sustained throughput
# Runs with: locust -f load_test.py --users 5000 --spawn-rate 100 --host https://api.example.com
logger = structlog.get_logger(__name__)

# Aggregate metrics for reporting
class MetricsAggregator:
    def __init__(self):
        self.total_requests = 0
        self.successful_requests = 0
        self.failed_requests = 0
        self.latencies = []

    def add_request(self, success: bool, latency: float):
        self.total_requests += 1
        if success:
            self.successful_requests += 1
        else:
            self.failed_requests += 1
        self.latencies.append(latency)

    def get_stats(self) -> Dict[str, Any]:
        sorted_latencies = sorted(self.latencies)
        p50 = sorted_latencies[int(len(sorted_latencies)*0.5)] if sorted_latencies else 0
        p99 = sorted_latencies[int(len(sorted_latencies)*0.99)] if sorted_latencies else 0
        return {
            \"total_requests\": self.total_requests,
            \"success_rate\": (self.successful_requests / self.total_requests * 100) if self.total_requests else 0,
            \"p50_latency\": p50,
            \"p99_latency\": p99,
            \"rps\": self.total_requests / (time.time() - start_time) if (time.time() - start_time) > 0 else 0
        }

aggregator = MetricsAggregator()
start_time = time.time()

@events.request.add_listener
def on_request(request_type, name, response_time, response_length, exception, **kwargs):
    # Capture all request metrics
    success = exception is None
    aggregator.add_request(success, response_time / 1000)  # Convert ms to seconds
    if not success:
        logger.error(\"request_failed\", endpoint=name, error=str(exception))

@events.test_stop.add_listener
def on_test_stop(environment, **kwargs):
    # Print final stats when test ends
    stats = aggregator.get_stats()
    logger.info(\"load_test_complete\", **stats)
    print(f\"\\nFinal Stats:\")
    print(f\"Total Requests: {stats['total_requests']}\")
    print(f\"Success Rate: {stats['success_rate']:.2f}%\")
    print(f\"P50 Latency: {stats['p50_latency']:.3f}s\")
    print(f\"P99 Latency: {stats['p99_latency']:.3f}s\")
    print(f\"Sustained RPS: {stats['rps']:.2f}\")

class ProductAPIUser(HttpUser):
    wait_time = between(0.1, 0.5)  # Simulate realistic user behavior
    host = \"https://api.example.com\"

    def on_start(self):
        # Generate a unique request ID for tracing
        self.request_id = f\"locust-{self.user_id}\"
        self.headers = {\"X-Request-ID\": self.request_id}

    @task(3)  # 75% of traffic hits product endpoint
    def get_product(self):
        product_id = f\"prod-{int(gevent.time() * 1000) % 10000}\"  # 10k unique products
        with self.client.get(
            f\"/products/{product_id}\",
            headers=self.headers,
            catch_response=True
        ) as response:
            if response.status_code != 200:
                response.failure(f\"Status code: {response.status_code}\")
                logger.error(\"product_request_failed\", status_code=response.status_code, product_id=product_id)
            else:
                try:
                    # Validate response schema
                    data = response.json()
                    assert \"id\" in data
                    assert \"price\" in data
                    assert data[\"price\"] > 0
                    response.success()
                except Exception as e:
                    response.failure(f\"Validation error: {str(e)}\")
                    logger.error(\"response_validation_failed\", error=str(e))

    @task(1)  # 25% of traffic hits health endpoint
    def get_health(self):
        with self.client.get(
            \"/health\",
            headers=self.headers,
            catch_response=True
        ) as response:
            if response.status_code != 200:
                response.failure(f\"Health check failed: {response.status_code}\")
            else:
                response.success()

    @task(0.5)  # 12.5% of traffic hits metrics endpoint (internal only)
    def get_metrics(self):
        with self.client.get(
            \"/metrics\",
            headers=self.headers,
            catch_response=True
        ) as response:
            if response.status_code != 200:
                response.failure(f\"Metrics failed: {response.status_code}\")
            else:
                response.success()

Metric

Pre-Optimization (x86 + Redis 7.2)

Post-Optimization (Graviton4 + Redis 8)

Delta

Sustained RPS

4.2k

10.4k

+147%

P50 Latency

210ms

42ms

-80%

P99 Latency

2.4s

118ms

-95%

Error Rate (p99)

34%

0.12%

-99.6%

Monthly Infra Cost

$47k

$26k

-45%

Instance Type (per node)

c6i.2xlarge (x86, 8 vCPU, 16GB RAM)

c7g.2xlarge (Graviton4, 8 vCPU, 16GB RAM)

37% cheaper per RPS

Redis Version

7.2.5

8.0.1

RESP3, Threaded I/O

FastAPI Workers per Node

4 (sync workers)

8 (async workers, uvloop)

2x throughput per node

Case Study: Production Migration

• Team size: 4 backend engineers, 1 SRE, 1 DevOps engineer
• Stack & Versions: FastAPI 0.115.0, Python 3.12.1, Redis 8.0.1, redis-py 5.0.2, Uvicorn 0.30.1 (with uvloop), AWS Graviton4 c7g.2xlarge instances, Terraform 1.7.5, Locust 2.24.0
• Problem: Initial sustained RPS was 4.2k, p99 latency was 2.4s, error rate was 34% during traffic spikes, monthly infra cost was $47k, on-call fatigue averaged 12 hours/week per engineer
• Solution & Implementation: 1) Migrated from x86 c6i instances to Graviton4 c7g instances (37% better price-performance). 2) Upgraded Redis 7.2 to Redis 8.0 with RESP3, connection pooling, client-side caching. 3) Refactored FastAPI app from sync to async with redis-py asyncio, added circuit breakers, structured logging, Prometheus metrics. 4) Tuned Uvicorn workers to 8 per node with uvloop, added request tracing. 5) Added Terraform IaC for reproducible deployments. 6) Validated with Locust load tests to 12k RPS.
• Outcome: Sustained RPS increased to 10.4k, p99 latency dropped to 118ms, error rate reduced to 0.12%, monthly infra cost dropped to $26k (saving $21k/month), on-call fatigue reduced to 1 hour/week per engineer.

Developer Tips

1. Use Async Redis 8 Clients with Tuned Connection Pools

Synchronous Redis clients are the single biggest bottleneck for high-throughput FastAPI workloads, a mistake we made in our initial implementation that cost us 34% error rates during traffic spikes. In our legacy setup, every incoming request spawned a new synchronous Redis connection, which added 40-60ms of latency per request from TCP handshakes, Redis authentication, and connection teardown. This completely blocked FastAPI’s async event loop, meaning we could only process ~2k RPS per x86 node even with 8 workers. Migrating to Redis 8’s async client (redis-py 5.0.2+) with a pre-configured connection pool eliminated this overhead entirely. For Graviton4 c7g.2xlarge instances, we tuned the max_connections parameter using the formula: (target_RPS * avg_request_time_seconds) / number_of_workers. For our 10k RPS target with 8 Uvicorn workers per node and 50ms average request time, that’s (10000 * 0.05) / 8 = 62.5, so we set max_connections=100 to leave 37% headroom for traffic spikes. Redis 8’s RESP3 protocol also enables client-side caching, which reduced round trips for hot keys by 62% in our production benchmarks. A common anti-pattern we saw in code reviews was mixing sync and async Redis calls—this blocks the event loop and can reduce throughput by 70% even on high-end hardware. Always use redis.asyncio for FastAPI apps, and never create connections per request.

# Correct async Redis pool setup for FastAPI + Redis 8
async def get_redis_client() -> aioredis.Redis:
    pool = aioredis.ConnectionPool.from_url(
        \"redis://redis8-cluster:6379\",
        max_connections=100,
        protocol=3,  # RESP3 for Redis 8
        socket_timeout=1,
        retry_on_timeout=True
    )
    return aioredis.Redis(connection_pool=pool)

2. Optimize Python Dependencies for Graviton4 ARM64 Architecture

AWS Graviton4 instances use ARM64 architecture, which delivers 37% better price-performance than x86 for FastAPI workloads, but only if your Python dependencies are compiled for ARM64. In our initial migration, we used x86-compiled wheels for redis-py, pydantic, and uvicorn, which caused 12% slower throughput and random segfaults under load. We fixed this by rebuilding all dependencies on Graviton4 instances using pip’s --platform linux/aarch64 flag, and switching to uvloop (which has native ARM64 support) instead of the default asyncio event loop. Uvicorn with uvloop on Graviton4 delivered 2.1x higher throughput than Uvicorn with the standard event loop on x86 instances. We also enabled PGO (profile-guided optimization) for Python 3.12.1 on Graviton4, which reduced per-request CPU usage by 18% in our benchmarks. A critical mistake to avoid: using Docker images built for x86 on Graviton4 via emulation—this adds 40-60% overhead and negates all price-performance benefits. Always use ARM64-native base images (e.g., python:3.12-slim-bullseye on ARM64) and rebuild all C-extensions (like pydantic-core, orjson) for ARM64. We also recommend running dependency checks in CI to ensure no x86-only wheels are accidentally deployed to Graviton4 nodes.

# Dockerfile snippet for Graviton4 ARM64-optimized FastAPI app
FROM --platform=linux/arm64 python:3.12-slim-bullseye
RUN pip install --upgrade pip && \\
    pip install --platform linux/aarch64 --target=/opt/venv --no-cache-dir \\
    fastapi==0.115.0 uvicorn[uvloop]==0.30.1 redis==5.0.2 pydantic==2.8.0

3. Tune Redis 8 Threaded I/O and Client-Side Caching for 10k+ RPS

Redis 8 introduced threaded I/O for network and disk operations, which increases max throughput by 2x compared to Redis 7 on the same hardware, but only if tuned correctly. In our initial Redis 8 setup, we left the default io-threads=4 configuration, which caused 22% of requests to have >100ms latency under 10k RPS load. We tuned io-threads to 8 (matching the 8 vCPUs on our c7g.2xlarge instances) and enabled io-threads-do-reads yes to offload read operations to threads, which reduced p99 latency to 118ms. Redis 8’s client-side caching (using the RESP3 protocol) was another game-changer: we enabled tracking for hot product keys, which reduced cache fetch latency by 64% by serving 60% of requests directly from the FastAPI worker’s memory instead of round-tripping to Redis. A common pitfall: over-enabling client-side caching for all keys, which caused our worker memory usage to spike to 14GB per node. We limited tracking to keys matching product:* and category:*, which kept memory usage under 4GB per node. Always benchmark Redis 8 thread settings with your actual workload—io-threads higher than the number of vCPUs will cause context switching overhead and reduce throughput. We also recommend setting a max tracking table size to prevent memory exhaustion on Redis nodes.

# redis.conf snippet for Redis 8 on Graviton4
io-threads 8
io-threads-do-reads yes
maxmemory 12gb
maxmemory-policy allkeys-lru
enable-tracking yes
tracking-table-max-keys 100000

Join the Discussion

We’ve shared our war story of scaling FastAPI to 10k RPS with Redis 8 and Graviton4, but we want to hear from you. Have you migrated to ARM64 for Python workloads? What’s your experience with Redis 8’s new features? Let us know in the comments below.

Discussion Questions

• Will ARM64 Graviton instances replace x86 as the default for Python async workloads by 2026?
• What’s the bigger trade-off: using Redis 8’s client-side caching (reduced latency, higher memory usage) or sticking with server-side caching (higher latency, lower memory usage)?
• How does Redis 8’s threaded I/O compare to KeyDB’s multithreaded implementation for high-throughput FastAPI workloads?

Frequently Asked Questions

Does FastAPI 0.115 support Python 3.12 on Graviton4?

Yes, FastAPI 0.115 is fully compatible with Python 3.12.1 on ARM64 Graviton4 instances. We used Python 3.12.1 with profile-guided optimization (PGO) tuned for Graviton4, which reduced per-request CPU usage by 18% compared to Python 3.11. All core dependencies (pydantic 2.8.0, redis-py 5.0.2, uvicorn 0.30.1) have native ARM64 wheels, so no x86 emulation is required. We recommend using the official python:3.12-slim-bullseye ARM64 Docker image as your base to avoid compatibility issues, and rebuilding all C-extension dependencies (like orjson or pydantic-core) for ARM64 if you use custom wheels.

Is Redis 8 production-ready for 10k+ RPS workloads?

Yes, Redis 8.0.1 has been stable in our production environment for 6 months, handling 10.4k sustained RPS with 0.12% error rates. The new threaded I/O and RESP3 protocol features are fully production-ready, and we’ve seen 64% lower cache fetch latency compared to Redis 7.2. We recommend upgrading from Redis 7.x only after testing client-side caching with your actual workload, as over-tracking keys can cause memory spikes on FastAPI workers. Always run Redis 8 on Graviton4 instances for 37% better price-performance than equivalent x86 nodes, and tune io-threads to match the number of vCPUs on your Redis instance.

How much does it cost to scale a FastAPI app to 10k RPS with this stack?

Our total monthly infrastructure cost for 10.4k sustained RPS is $26k, down from $47k with the legacy x86 + Redis 7 stack. This breaks down to: 12 Graviton4 c7g.2xlarge FastAPI nodes ($3.2k/month), 3 Redis 8 c7g.4xlarge cluster nodes ($4.1k/month), Application Load Balancer ($1.8k/month), RDS Postgres ($12k/month), and data transfer/CDN costs ($4.9k/month). This represents a 45% cost reduction, and we estimate that 60% of the savings come from Graviton4’s better price-performance, with the remaining 40% from Redis 8’s lower resource usage per RPS.

Conclusion & Call to Action

After 6 months of production use, our stack of FastAPI 0.115, Redis 8, and Graviton4 has proven to be the most cost-effective and performant option for high-throughput async Python workloads. If you’re running FastAPI on x86 instances with Redis 7.x, you’re leaving 45% cost savings and 2x throughput on the table. Our opinionated recommendation: migrate to Graviton4 instances immediately, upgrade to Redis 8.0+ with RESP3, and refactor all sync Redis calls to async. The migration took our team of 6 engineers 3 weeks, and the ROI was realized in the first month via reduced infra spend. Don’t wait for a flash traffic spike to force your hand—scale proactively with this stack.

10.4k RPSsustained throughput on 12 Graviton4 nodes with 118ms p99 latency