Checkpointing LLM chains with Temporal

The moment you move past a demo, inference becomes a chain of compute-dependent calls:

1. Classify the user's intent.
2. Generate a response based on that classification.
3. Validate the output against your schema or safety rules.
4. Post-process: parse, format, guardrail checks.

Four calls minimum - often more if you're doing RAG retrieval, summarization, or agent tool selection upstream. Each call depends on the one before it. The generation needs the classification. The validation needs the generation. Post-processing needs all of it.

Most teams build this with try/catch blocks and exponential backoff. It works on the most trivial of issues. On anything else, a failure restarts the entire chain from scratch.

A four-call chain where the validation step times out loses the validation and the classification and generation that preceded it. Those tokens and that latency are already spent, and the user is still waiting.

The duct-tape solutions are well-known. Retry with exponential backoff handles transient failures but re-executes every completed step. Circuit breakers prevent cascade failures but don't preserve intermediate results. Manual caching (Redis, DynamoDB) can checkpoint results, but now you're writing and maintaining state management code for every pipeline stage - TTLs, invalidation, coherency, the full distributed state nightmare.

These are band-aids on structural problems, ones that have been solved for years by other software engineering domains.^[1] The pipeline has no durable checkpoints. Every failure is a full restart because nothing remembers what already succeeded.

Temporal is a durable execution platform.^[1] Each LLM call becomes a Temporal activity - or its own child workflow. When an activity completes, Temporal durably records the result as a checkpoint, without you writing any persistence code. If the workflow crashes, restarts, or times out on step three, it replays from the last checkpoint. Steps one and two don't re-execute. Their results are already recorded.

The workflow definition is the retry logic. No dead letter queues, no manual state reconciliation, no Redis cache to keep coherent. You write the pipeline as a sequence of activity calls. Temporal handles the rest - timeouts, retries, replay, and recovery.^[2]

Longer chains gain more: agent loops that iterate 10+ times; RAG pipelines with retrieval, ranking, generation, and validation; multi-model routing where different models handle different parts of the response. Each additional step is another checkpoint that Temporal preserves. OpenAI's Codex agent and Replit's Agent both run on Temporal.^[3]

Three of the four pipeline steps are LLM calls: Classify, Generate, Validate. Post-processing is deterministic. When step three (Validate) fails, three LLM calls have run, including the one that failed. The retry re-runs all three from scratch. That's six total LLM calls to get one successful result, double the cost. With Temporal, the retry runs only the failed Validate call again, for four total LLM calls.

Scale that to an agent loop doing 10 LLM calls where call 8 fails. Without durability, the retry runs all 10 again, for 18 calls in total. With Temporal it runs call 8 again and carries on, for 11.

Durability itself is cheap. Measured against the same model call made directly, 200 runs per arm on two runtimes, wrapping it in a Temporal workflow costs 24 to 28 ms at p95 outside the call, and 25 to 32 ms at p99. Continuum's whole platform around the call, with Redis Streams for I/O, PostgreSQL for state and a notice back to the client, is a separate, real-world number: 35 to 43 ms at p95 outside the model's activity, on LM Studio. Both are broken down, stretch by stretch, in the durable inference benchmark.